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TSGS1_112_Dallas
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revised
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General
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3GPP TR 22.870 V0.4.1 (2025-10)
Technical Report
3rd Generation Partnership Project;
Technical Specification Group TSG SA;
Study on 6G Use Cases and Service Requirements;
Stage 1
(Release 20)
The present document has been developed within the 3rd Generation Partnership Project (3GPP TM) and may be further elaborated for the purposes of 3GPP.
The present document has not been subject to any approval process by the 3GPP Organizational Partners and shall not be implemented.
This Specification is provided for future development work within 3GPP only. The Organizational Partners accept no liability for any use of this Specification.
Specifications and Reports for implementation of the 3GPP TM system should be obtained via the 3GPP Organizational Partners' Publications Offices.
3GPP
Postal address
3GPP support office address
650 Route des Lucioles - Sophia Antipolis
Valbonne - FRANCE
Tel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16
Internet
http://www.3gpp.org
Copyright Notification
No part may be reproduced except as authorized by written permission.
The copyright and the foregoing restriction extend to reproduction in all media.
© 2025, 3GPP Organizational Partners (ARIB, ATIS, CCSA, ETSI, TSDSI, TTA, TTC).
All rights reserved.
UMTS™ is a Trade Mark of ETSI registered for the benefit of its members
3GPP™ is a Trade Mark of ETSI registered for the benefit of its Members and of the 3GPP Organizational Partners
LTE™ is a Trade Mark of ETSI registered for the benefit of its Members and of the 3GPP Organizational Partners
GSM® and the GSM logo are registered and owned by the GSM Association
Contents
Foreword 24
1 Scope 26
2 References 26
3 Definitions of terms, symbols and abbreviations 43
3.1 Terms 43
3.2 Symbols 44
3.3 Abbreviations 44
4 Overview 47
4.1 Sustainability 47
5 System and Operational Aspects 47
5.1 General 47
5.2 Interworking with legacy systems 48
5.3 Support of non-3GPP access 48
5.4 Support for legacy service requirements 48
5.4.1 Introduction 48
5.4.2 Support of legacy services 48
5.4.3 Support of other legacy requirements 49
5.5 Security for 6G 49
5.5.1 Network security for 6G 49
5.5.1.1 Description 49
5.5.1.2 Potential New Requirements 50
5.5.2 Use case on quantum-resistant security 50
5.5.2.1 Description 50
5.5.2.2 Pre-conditions 51
5.5.2.3 Service Flows 51
5.5.2.4 Post-conditions 51
5.5.2.5 Existing features partly or fully covering the use case functionality 51
5.5.2.6 Potential New Requirements needed to support the use case 52
5.5.3 Use case on UE selecting a base station after assessing its legitimacy 52
5.5.3.1 Description 52
5.5.3.2 Potential New Requirements needed to support the use case 52
5.5.4 6G security requirements on trust establishment, security management and digital identity 52
5.5.4.1 Description 52
5.5.4.2 Potential New Requirements 53
5.5.5 Use case on data exposure service 54
5.5.5.1 Description 54
5.5.5.2 Existing features partly or fully covering the use case functionality 55
5.5.5.3 Potential New Requirements needed to support the use case 55
5.5.6 Considerations on privacy 55
5.5.6.1 Description 55
5.5.6.2 Existing features partly or fully covering the functionality 56
5.5.6.3 Potential New Requirements 56
5.5.7 Use Case on privacy protection of data exposure 56
5.5.7.1 Description 56
5.5.7.2 Existing features partly or fully covering the use cases functionality 57
5.5.7.3 Potential New Requirements needed to support the use case 57
5.5.8 Use case on security control enhancement with NDT in 6G network 57
5.5.8.1 Description 57
5.5.8.2 Pre-conditions 59
5.5.8.3 Service Flows 59
5.5.8.4 Post-conditions 59
5.5.8.5 Existing features partly or fully covering the use case functionality 59
5.5.8.6 Potential New Requirements needed to support the use case 59
5.5.9 Use case on digital identity management for digital asset container 59
5.5.9.1 Description 59
5.5.9.2 Pre-conditions 60
5.5.9.3 Service Flows 60
5.5.9.4 Post-conditions 60
5.5.9.5 Existing features partly or fully covering the use case functionality 60
5.5.9.6 Potential New Requirements needed to support the use case 61
5.5.10 Roaming Services 61
5.5.10.1 Description 61
5.5.10.2 Potential New Requirements 61
5.6 Resilience 61
5.6.1 Zero-outage network 61
5.6.1.1 Description 61
5.6.1.2 Potential New Requirements 62
5.6.2 Use case on fast network provisioning to improve resilience 62
5.6.2.1 Description 62
5.6.2.2 Pre-conditions 62
5.6.2.3 Service Flows 62
5.6.2.4 Post-conditions 63
5.6.2.5 Existing features partly or fully covering the use case functionality 63
5.6.2.6 Potential New Requirements needed to support the use case 63
5.6.3 Use case on resiliency for 6G 63
5.6.3.1 Description 63
5.6.3.2 Precondition 64
5.6.3.3 Service Flows 64
5.6.3.4 Post-conditions 64
5.6.3.5 Existing features partly or fully covering the use case functionality 65
5.6.3.6 Potential New Requirements needed to support the use case 65
5.6.4 Use case on disaster risk-based network resilience 65
5.6.4.1 Description 65
5.6.4.2 Pre-conditions 66
5.6.4.3 Service Flows 66
5.6.4.4 Post-conditions 67
5.6.4.5 Existing features partly or fully covering the use case functionality 67
5.6.4.6 Potential New Requirements needed to support the use case 68
5.6.5 Use Case on prevention of signalling storm 69
5.6.5.1 Description 69
5.6.5.2 Pre-conditions 69
5.6.5.3 Service Flows 69
5.6.5.4 Post-conditions 69
5.6.5.5 Existing features partly or fully covering the use case functionality 69
5.6.5.6 Potential New Requirements needed to support the use case 69
5.7 6G enhancements of legacy/existing services and capabilities 70
5.7.1 Fixed wireless access (FWA) 70
5.7.1.1 Description 70
5.7.1.2 Potential New Requirements 70
5.7.2 IMS multimedia telephony service 70
5.7.2.1 Description 70
5.7.2.2 Potential New Requirements 71
5.7.3 Enhancement of short message service (SMS) 71
5.7.3.1 Description 71
5.7.3.2 Potential New Requirements 71
5.7.4 Network sharing 72
5.7.4.1 Description 72
5.7.4.2 Potential New Requirements 72
5.7.5 Network slicing 72
5.7.5.1 Description 72
5.7.5.2 Potential New Requirements 73
5.7.6 Unified Access Control (UAC) 73
5.7.6.1 Description 73
5.7.6.2 Potential New Requirements 73
5.7.7 Use Case on IMS Media Related Service 73
5.7.7.1 Description 73
5.7.7.2 Pre-conditions 73
5.7.7.3 Service Flows 74
5.7.7.4 Post-conditions 74
5.7.7.5 Existing features partly or fully covering the use case functionality 74
5.7.7.6 Potential New Requirements needed to support the use case 74
5.7.8 Enhancement of voice service 74
5.7.8.1 Description 74
5.7.8.2 Potential New Requirements 75
5.7.9 Network coverage and usage verification 75
5.7.9.1 Description 75
5.7.9.2 Existing features partly covering the required functionality 76
5.7.9.3 Potential New Requirements 76
5.7.10 Use case on network sharing on radio access network with sensing capability 76
5.7.10.1 Description 76
5.7.10.2 Pre-conditions 77
5.7.10.3 Service flows 78
5.7.10.4 Post-conditions 78
5.7.10.5 Existing features partly or fully covering the use case functionality 78
5.7.10.6 Potential New Requirements needed to support the use case 79
5.8 Sustainability and Energy Efficiency 79
5.8.1 Use case on end-to-end energy efficiency improvement for the network and UE 79
5.8.1.1 Description 79
5.8.1.2 Pre-conditions 80
5.8.1.3 Service Flows 80
5.8.1.4 Post-conditions 81
5.8.1.5 Existing features partly or fully covering the use case functionality 81
5.8.1.6 Potential New Requirements needed to support the use case 81
5.8.2 Use case on energy efficiency of 6G system with multiple access networks (TN and NTN) 81
5.8.2.1 Description 81
5.8.2.2 Pre-conditions 82
5.8.2.3 Service Flows 82
5.8.2.4 Post-conditions 82
5.8.2.5 Existing features partly or fully covering the use case functionality 82
5.8.2.6 Potential New Requirements needed to support the use case 82
5.8.3 Use case on supporting energy control at slice level 82
5.8.3.1 Description 82
5.8.3.2 Pre-conditions 83
5.8.3.3 Service Flows 83
5.8.3.4 Post-conditions 83
5.8.3.5 Existing features partly or fully covering the use case functionality 83
5.8.3.6 Potential New Requirements needed to support the use case 84
5.8.4 Use case on joint energy saving for network and UE with various loads 84
5.8.4.1 Description 84
5.8.4.2 Pre-conditions 85
5.8.4.3 Service Flows 85
5.8.4.4 Post-conditions 85
5.8.4.5 Existing features partly or fully covering the use case functionality 86
5.8.4.6 Potential New Requirements needed to support the use case 86
5.8.5 Use case on UE energy efficiency for XR rendering/AI tasks 86
5.8.5.1 Description 86
5.8.5.2 Pre-condition 87
5.8.5.3 Service flow 87
5.8.5.4 Post condition 88
5.8.5.5 Existing features partly or fully covering the use cases functionality 88
5.8.5.6 Potential New Requirements needed to support the use case 88
5.8.6 Use case on energy saving for network in industry park 88
5.8.6.1 Description 88
5.8.6.2 Pre-conditions 89
5.8.6.3 Service Flows 89
5.8.6.4 Post-conditions 89
5.8.6.5 Existing features partly or fully covering the use case functionality 89
5.8.6.6 Potential New Requirements needed to support the use case 90
5.8.7 Use case on green communications and computing optimisation using network digital twin 90
5.8.7.1 Description 90
5.8.7.2 Pre-conditions 90
5.8.7.3 Service Flows 91
5.8.7.4 Post-conditions 91
5.8.7.5 Existing features partly or fully covering the use case functionality 91
5.8.7.6 Potential New Requirements needed to support the use case 91
5.8.8 Use case on end-to-end energy saving by cooperating UEs 91
5.8.8.1 Description 91
5.8.8.2 Pre-conditions 92
5.8.8.3 Service Flows 92
5.8.8.4 Post-conditions 92
5.8.8.5 Existing features partly or fully covering the use case functionality 92
5.8.8.6 Potential New Requirements needed to support the use case 93
5.9 Network Aspects 93
5.9.1 Use case on support of femtocells for localized deployment 93
5.9.1.1 Description 93
5.9.1.2 Existing features partly or fully covering the use case functionality 94
5.9.1.3 Potential New Requirements 94
5.9.2 Efficient data collection and consumption for 6G system 94
5.9.2.1 Description 94
5.9.2.2 Potential New Requirements 96
5.9.3 Use case on network digital twin in the 6G network 96
5.9.3.1 Description 96
5.9.3.2 Pre-conditions 97
5.9.3.3 Service Flows 98
5.9.3.4 Post-conditions 99
5.9.3.5 Existing features partly or fully covering the use case functionality 99
5.9.3.6 Potential New Requirements needed to support the use case 99
5.9.4 Network simplification on 6G system 100
5.9.4.1 Description 100
5.9.4.2 Potential New Requirements 100
5.9.5 Use case on network simplification for rolling out new services 100
5.9.5.1 Description 100
5.9.5.2 Pre-conditions 100
5.9.5.3 Service Flows 101
5.9.5.4 Post-conditions 101
5.9.5.5 Existing features partly or fully covering the use case functionality 101
5.9.5.6 Potential New Requirements needed to support the use case 101
5.9.6 Use case on 6G Local Area Networks 101
5.9.6.1 Description 101
5.9.6.2 Pre-conditions 102
5.9.6.3 Service Flows 103
5.9.6.4 Post-conditions 104
5.9.6.5 Existing features partly or fully covering the use case functionality 104
5.9.6.6 Potential New Requirements needed to support the use case 105
5.9.7 Use case on flexible traffic routing in 6G 105
5.9.7.1 Description 105
5.9.7.2 Pre-conditions 106
5.9.7.3 Service Flows 107
5.9.7.4 Post-conditions 108
5.9.7.5 Existing features partly or fully covering the use case functionality 108
5.9.7.6 Potential New Requirements needed to support the use case 109
5.9.8 Enhanced Network Service Awareness 109
5.9.8.1 Description 109
5.9.8.2 Potential New Requirements 109
5.10 Device Support 110
5.10.1 Continued support for diverse UE types 110
5.10.1.1 Description 110
5.10.1.2 Existing features partly or fully covering the use case functionality 110
5.10.1.3 Potential New Requirements 110
5.10.2 Diversity of UEs for satellite access 111
5.10.2.1 Description 111
5.10.2.2 Potential New Requirements needed to support the use case 111
6 AI 111
6.1 General 111
6.2 Use case on optimizing 6G infrastructure utilization via resource exposure in 6G 112
6.2.1 Description 112
6.2.2 Pre-conditions 113
6.2.3 Service Flows 113
6.2.4 Post-conditions 113
6.2.5 Existing features partly or fully covering the use case functionality 114
6.2.6 Potential new requirements needed to support the use case 114
6.3 Use case on end-to-end AI for connected cars 114
6.3.1 Description 114
6.3.2 Pre-conditions 115
6.3.3 Service Flows 115
6.3.4 Post-conditions 115
6.3.5 Existing features partly or fully covering the use case functionality 115
6.3.6 Potential New Requirements needed to support the use case 116
6.4 Use case on system performance optimisation using AI 116
6.4.1 Description 116
6.4.2 Pre-conditions 116
6.4.3 Service Flows 116
6.4.4 Post-conditions 117
6.4.5 Existing features partly or fully covering the use case functionality 117
6.4.6 Potential New Requirements needed to support the use case 117
[PR 6.4.6-3] Based on operator policy and user consent, the 6G system shall be able to support mechanisms (e.g. AI capabilities in the network and UEs) allowing the network and UEs to negotiate communication parameters for a communication service.6.5 Use case on personalized AI for health monitoring 117
6.5.1 Description 117
6.5.2 Pre-conditions 117
6.5.3 Service Flows 117
6.5.4 Post-conditions 118
6.5.5 Existing features partly or fully covering the use case functionality 118
6.5.6 Potential New Requirements needed to support the use case 118
6.6 Use case on 6G AI agent collaboration with third-party AI using LLM 118
6.6.1 Description 118
6.6.2 Pre-conditions 119
6.6.3 Service Flows 119
6.6.4 Post Conditions 120
6.6.5 Existing features partly or fully covering the use case functionality 120
6.6.6 Potential new requirements needed to support the use case 120
6.7 Use case on AI-agents communication 121
6.7.1 Description 121
6.7.2 Pre-conditions 121
6.7.3 Service Flows 122
6.7.4 Post-conditions 123
6.7.5 Existing features partly or fully covering the use case functionality 123
6.7.6 Potential New Requirements needed to support the use case 124
6.8 Use case on 6G system assisted AI agent service 125
6.8.1 Description 125
6.8.2 Pre-conditions 126
6.8.3 Service Flows 126
6.8.5 Existing features partly or fully covering the use case functionality 126
6.8.6 Potential New Requirements needed to support the use case 126
6.9 Use case on collaborative AI agents 127
6.9.1 Description 127
6.9.2 Pre-conditions 127
6.9.3 Service Flows 127
6.9.4 Post-conditions 128
6.9.5 Existing features partly or fully covering the use case functionality 128
6.9.6 Potential New Requirements needed to support the use case 128
6.10 Use case on home robots 128
6.10.1 Description 128
6.10.2 Pre-conditions 130
6.10.3 Service Flows 130
6.10.4 Post-conditions 132
6.10.5 Existing features partly or fully covering the use case functionality 132
6.10.6 Potential New Requirements needed to support the use case 132
6.11 Use case on built-in Intelligent Communication Assistant 133
6.11.1 Description 133
6.11.2 Pre-conditions 134
6.11.3 Service Flows 135
6.11.4 Post-conditions 136
6.11.5 Existing features partly or fully covering the use case functionality 136
6.11.6 Potential New Requirements needed to support the use case 136
6.12 Use case on 6G System supporting AI model training service 137
6.12.1 Description 137
6.12.2 Pre-conditions 137
6.12.3 Service Flows 137
6.12.4 Post-conditions 138
6.12.5 Existing features partly or fully covering the use case functionality 138
6.12.6 Potential New Requirements needed to support the use case 139
6.13 Use case on network knowledge as part of Retrieval Augmented Generation for Generative AI 139
6.13.1 Description 139
6.13.2 Pre-conditions 141
6.13.3 Service Flows 141
6.13.4 Post-conditions 142
6.13.5 Existing features partly or fully covering the use case functionality 142
6.13.6 Potential New Requirements needed to support the use case 143
6.14 Use case on intelligent UAV swarms 143
6.14.1 Description 143
6.14.2 Pre-conditions 144
6.14.3 Service Flows 144
6.14.4 Post-conditions 145
6.14.5 Existing features partly or fully covering the use case functionality 145
6.14.6 Potential New Requirements needed to support the use case 145
6.15 Use case on 6G system assisted target object detection 145
6.15.1 Description 145
6.15.2 Pre-conditions 146
6.15.3 Service Flows 147
6.15.4 Post-conditions 147
6.15.5 Existing features partly or fully covering the use case functionality 147
6.15.6 Potential New Requirements needed to support the use case 147
6.16 Use case on energy of the system intelligent management 147
6.16.1 Description 147
6.16.2 Pre-conditions 148
6.16.3 Service Flows 148
6.16.4 Post-conditions 148
6.16.5 Existing features partly or fully covering the use case functionality 148
6.16.6 Potential New Requirements needed to support the use case 148
6.17 Use case on intelligent communication assistant 148
6.17.1 Description 148
6.17.2 Pre-conditions 149
6.17.3 Services Flows 149
6.17.4 Post-conditions 149
6.17.5 Existing features partly or fully covering the use case functionality 150
6.17.6 Potential New Requirements needed to support the use case 150
6.18 Use case on exposing achievable QoS to aid computational resource selection 150
6.18.1 Description 150
6.18.2 Pre-conditions 150
6.18.3 Service Flows 150
6.18.4 Post-conditions 151
6.18.5 Existing features partly or fully covering the use case functionality 151
6.18.6 Potential New Requirements needed to support the use case 151
6.19 Use case on AI-based video analysis 151
6.19.1 Description 151
6.19.2 Pre-conditions 151
6.19.3 Service Flows 151
6.19.4 Post-conditions 152
6.19.5 Existing features partly or fully covering the use cases functionality 152
6.19.6 Potential New Requirements needed to support the use case 152
6.20 Use case on smart housekeeping 152
6.20.1 Description 152
6.20.2 Pre-conditions 152
6.20.3 Service Flows 152
6.20.4 Post-conditions 153
6.20.5 Existing features partly or fully covering the use case functionality 153
6.20.6 Potential New Requirements needed to support the use case 153
6.21 Use case on 6G network providing on-demand networking with AI Agent 153
6.21.1 Description 153
6.21.2 Pre-conditions 154
6.21.3 Service Flows 154
6.21.4 Post-conditions 155
6.21.5 Existing features partly or fully covering the use case functionality 155
6.21.6 Potential New Requirements needed to support the use case 155
6.22 Use case on intelligent calling services 155
6.22.1 Description 155
6.22.2 Pre-conditions 156
6.22.3 Service Flows 156
6.22.4 Post-conditions 156
6.22.5 Existing features partly or fully covering the use case functionality 157
6.22.6 Potential New Requirements needed to support the use case 157
6.23 Use case on child health management assistant 157
6.23.1 Description 157
6.23.2 Pre-conditions 157
6.23.3 Service Flows 158
6.23.4 Post-conditions 159
6.23.5 Existing features partly or fully covering the use case functionality 159
6.23.6 Potential New Requirements needed to support the use case 160
6.24 Use case on distributed 6G network for AI computing 160
6.24.1 Description 160
6.24.2 Pre-conditions 160
6.24.3 Service Flows 161
6.24.4 Post-conditions 161
6.24.5 Existing features partly or fully covering the use case functionality 161
6.24.6 Potential New Requirements needed to support the use case 161
6.25 Use case on AI/ML model training and inference 161
6.25.1 Description 161
6.25.2 Pre-conditions 162
6.25.3 Service Flows 162
6.25.4 Post-conditions 163
6.25.5 Existing features partly or fully covering the use case functionality 164
6.25.6 Potential New Requirements needed to support the use case 167
6.26 Use case on optimizing user experience for GenAI applications 168
6.26.1 Description 168
6.26.2 Pre-conditions 170
6.26.3 Services Flows 170
6.26.4 Post-conditions 170
6.26.5 Existing features partly or fully covering the use case functionality 171
6.26.6 Potential New Requirements needed to support the use case 171
6.27 Use case on network federation for collaborative AI model training 171
6.27.1 Description 171
6.27.2 Pre-conditions 172
6.27.3 Service Flows 172
6.27.4 Post-conditions 172
6.27.5 Existing features partly or fully covering the use case functionality 172
6.27.6 Potential New Requirements needed to support the use case 173
6.28 Use case on network-assisted video-based AI inference task offloading for mobile embodied AI 173
6.28.1 Description 173
6.28.2 Pre-conditions 175
6.28.3 Service Flows 175
6.28.4 Post-conditions 175
6.28.5 Existing features partly or fully covering the use case functionality 176
6.28.6 Potential New Requirements needed to support the use case 176
6.29 Use case on smart home user-centric AI service 177
6.29.1 Description 177
6.29.2 Pre-conditions 177
6.29.3 Service Flows 177
6.29.4 Post-conditions 178
6.29.5 Existing features partly or fully covering the use case functionality 178
6.29.6 Potential New Requirements needed to support the use case 178
6.30 Use case on smart healthcare 178
6.30.1 Description 178
6.30.2 Pre-conditions 178
6.30.3 Service Flows 179
6.30.4 Post-conditions 179
6.30.5 Existing features partly or fully covering the use case functionality 179
6.30.6 Potential New Requirements needed to support the use case 180
6.31 Use case on UE-Network collaboration with AI capabilities 180
6.31.1 Description 180
6.31.2 Pre-conditions 181
6.31.3 Service Flows 181
6.31.4 Post-conditions 182
6.31.5 Existing features partly or fully covering the use case functionality 182
6.31.6 Potential New Requirements needed to support the use case 183
6.32 Use case on disaster rescue planning enabled by network AI Agents 183
6.32.1 Description 183
6.32.2 Pre-conditions 184
6.32.3 Service Flows 184
6.32.4 Post-conditions 184
6.32.5 Existing features partly or fully covering the use case functionality 185
6.32.6 Potential New Requirements needed to support the use case 185
6.33 Use case on AI text-to-video generation supported by computing 185
6.33.1 Description 185
6.33.2 Pre-conditions 185
6.33.3 Service Flows 186
6.33.4 Post-conditions 186
6.33.5 Existing features partly or fully covering the use case functionality 187
6.33.6 Potential New Requirements needed to support the use case 187
6.34 Use case on 6G computing support for AI model inference 187
6.34.1 Description 187
6.34.2 Pre-conditions 188
6.34.3 Service Flows 188
6.34.4 Post-conditions 188
6.34.5 Existing features partly or fully covering the use case functionality 188
6.34.6 Potential New Requirements needed to support the use case 188
6.35 Use Case on 6G native AI in multi-domain convergence 188
6.35.1 Description 188
6.35.2 Pre-conditions 189
6.35.3 Service Flows 189
6.35.4 Post-conditions 189
6.35.5 Existing feature partly or fully covering use case functionality 189
6.35.6 Potential New Requirements needed to support the use case 190
6.36 Use case on AI/ML model managed service for intelligent vehicles 190
6.36.1 Description 190
6.36.2 Pre-conditions 191
6.36.3 Service Flows 191
6.36.4 Post-conditions 192
6.36.5 Existing features partly or fully covering the use case functionality 192
6.36.6 Potential New Requirements needed to support the use case 192
6.37 Use case on energy efficiency for AI service 192
6.37.1 Description 192
6.37.2 Pre-conditions 193
6.37.3 Service Flows 193
6.37.4 Post-conditions 193
6.37.5 Existing features partly or fully covering the use case functionality 193
6.37.6 Potential New Requirements needed to support the use case 194
6.38 Use case on AI for disability support 194
6.38.1 Description 194
6.38.2 Pre-conditions 194
6.38.3 Service Flows 195
6.38.4 Post-conditions 195
6.38.5 Existing features partly or fully covering the use case functionality 195
6.38.6 Potential New Requirements needed to support the use case 195
6.39 Use case on considerations on responsible AI 196
6.39.1 Description 196
6.39.2 Pre-conditions 197
6.39.3 Service Flows 198
6.39.4 Post-conditions 198
6.39.5 Existing features partly or fully covering the use case functionality 198
6.39.6 Potential New Requirements needed to support the use case 198
6.40 Use case on AI-driven multi-vehicle cooperative perception 198
6.40.1 Description 198
6.40.2 Pre-conditions 198
6.40.3 Service Flows 198
6.40.4 Post-conditions 199
6.40.5 Existing features partly or fully covering the use case functionality 199
6.40.6 Potential New Requirements needed to support the use case 199
6.41 Use case on authentication and authorization for AI agents 200
6.41.1 Description 200
6.41.2 Pre-conditions 200
6.41.3 Service Flows 200
6.41.4 Post-conditions 201
6.41.5 Existing features partly or fully covering the use case functionality 201
6.41.6 Potential New Requirements needed to support the use case 202
6.42 Use case on AI-assisted multi-modal communication service 202
6.42.1 Description 202
6.42.2 Pre-conditions 202
6.42.3 Service Flows 202
6.42.4 Post-conditions 203
6.42.5 Existing features partly or fully covering the use case functionality 203
6.42.6 Potential New Requirements needed to support the use case 203
6.43 Use case on AI agent for network performance assurance 204
6.43.1 Description 204
6.43.2 Pre-conditions 204
6.43.3 Service Flows 205
6.43.4 Post-conditions 205
6.43.5 Existing features partly or fully covering the use case functionality 205
6.43.6 Potential New Requirements needed to support the use case 206
6.44 Use case on customized service provisioning based on AI Agents 207
6.44.1 Description 207
6.44.2 Pre-conditions 207
6.44.3 Service Flows 207
6.44.4 Post-conditions 208
6.44.5 Existing features partly or fully covering the use case functionality 208
6.44.6 Potential New Requirements needed to support the use case 208
6.45 Use case on flexible UE-Network coordination through AI agent(s) 208
6.45.1 Description 208
6.45.2 Pre-conditions 209
6.45.3 Service Flows 209
6.45.4 Post-conditions 210
6.45.5 Existing features partly or fully covering the use case functionality 210
6.45.6 Potential New Requirements needed to support the use case 210
6.46 Use case on AI agent management 210
6.46.1 Description 210
6.46.2 Pre-conditions 212
6.46.3 Service Flows 212
6.46.4 Post-conditions 212
6.46.5 Existing features partly or fully covering the use case functionality 212
6.46.6 Potential New Requirements needed to support the use case 213
6.47 Use case on proactive AI agent for personal safety 213
6.47.1 Description 213
6.47.2 Pre-conditions 213
6.47.3 Service Flows 213
6.47.4 Post-conditions 213
6.47.5 Existing features partly or fully covering the use cases functionality 214
6.47.6 Potential New Requirements needed to support the use case 214
6.48 Use case on service robot for power grid 214
6.48.1 Description 214
6.48.2 Pre-conditions 215
6.48.3 Service Flows 216
6.48.4 Post-conditions 216
6.48.5 Existing features partly or fully covering the use case functionality 217
6.48.6 Potential New Requirements needed to support the use case 217
6.49 Use case on 6GS providing low-latency AI inference service 217
6.49.1 Description 217
6.49.2 Pre-conditions 218
6.49.3 Service Flows 219
6.49.4 Post-conditions 220
6.49.5 Existing features partly or fully covering the use case functionality 220
6.49.6 Potential New Requirements needed to support the use case 221
6.50 Use case on real time video super-resolution service 221
6.50.1 Description 221
6.50.2 Pre-conditions 223
6.50.3 Service Flows 224
6.50.4 Post-conditions 225
6.50.5 Existing features partly or fully covering the use case functionality 225
6.50.6 Potential New Requirements needed to support the use case 225
6.51 Use case on network-based intelligent assistance (e.g. for autonomous driving) by a network-native AI Agent 225
6.51.1 Description 225
6.51.2 Pre-conditions 228
6.51.3 Service Flows 228
6.51.4 Post-conditions 229
6.51.5 Existing features partly or fully covering the use case functionality 229
6.51.6 Potential New Requirements needed to support the use case 230
6.52 Use case on smart support for data collection and fusion in multi-agent scenarios 231
6.52.1 Description 231
6.52.2 Pre-conditions 233
6.52.3 Service Flows 233
6.52.4 Post-conditions 234
6.52.5 Existing features partly or fully covering the use case functionality 234
6.52.6 Potential New Requirements needed to support the use case 234
6.53 Use case on AI-driven smart factory with computing service 235
6.53.1 Description 235
6.53.2 Pre-conditions 235
6.53.3 Service Flows 235
6.53.4 Post-conditions 235
6.53.5 Existing features partly or fully covering the use case functionality 236
6.53.6 Potential New Requirements needed to support the use case 236
6.54 Use case on AI-optimized smart call assistance for telecom networks 236
6.54.1 Description 236
6.54.2 Pre-conditions 236
6.54.3 Service Flows 236
6.54.4 Post-conditions 237
6.54.5 Existing features partly or fully covering the use case functionality 237
6.54.6 Potential New Requirements needed to support the use case 237
6.55 Use case on shared embodied AI agents 238
6.55.1 Description 238
6.55.2 Pre-conditions 238
6.55.3 Service Flows 238
6.55.4 Post-conditions 238
6.55.5 Existing features partly or fully covering the use case functionality 238
6.55.6 Potential New Requirements needed to support the use case 239
7 Integrated Sensing and Communication 239
7.1 General 239
7.2 Use case on coordination of search and rescue missions in large disaster areas 239
7.2.1 Description 239
7.2.2 Pre-conditions 239
7.2.3 Service Flows 240
7.2.4 Post-conditions 240
7.2.5 Existing features partly or fully covering the use case functionality 240
7.2.6 Potential New Requirements needed to support the use case 241
7.3 Use case on safety assistance for vulnerable pedestrians 241
7.3.1 Description 241
7.3.2 Pre-conditions 241
7.3.3 Service Flows 242
7.3.4 Post-conditions 242
7.3.5 Existing features partly or fully covering the use case functionality 242
7.3.6 Potential New Requirements needed to support the use case 242
7.4 Use case for high-resolution topographical maps 243
7.4.1 Description 243
7.4.2 Pre-conditions 244
7.4.3 Service Flows 244
7.4.4 Post-conditions 244
7.4.5 Existing features partly or fully covering the use case functionality 244
7.4.6 Potential New Requirements needed to support the use case 244
7.5 Use case on low-altitude UAV supervision 245
7.5.1 Description 245
7.5.2 Pre-conditions 246
7.5.3 Service Flows 247
7.5.4 Post-conditions 248
7.5.5 Existing features partly or fully covering the use case functionality 248
7.5.6 Potential New Requirements needed to support the use case 249
7.6 Use case on environment object reconstruction 249
7.6.1 Description 249
7.6.2 Pre-conditions 251
7.6.3 Service Flows 252
7.6.4 Post-conditions 253
7.6.5 Existing features partly or fully covering the use case functionality 253
7.6.6 Potential New Requirements needed to support the use case 253
7.7 Use case on road digitalization 253
7.7.1 Description 253
7.7.2 Pre-conditions 254
7.7.3 Service Flows 255
7.7.4 Post-conditions 256
7.7.5 Existing features partly or fully covering the use cast functionality 256
7.7.6 Potential New Requirements needed to support the use case 256
7.8 Use case on intelligence leveraging nearby entities for real time awareness 257
7.8.1 Description 257
7.8.2 Pre-conditions 258
7.8.3 Service Flows 258
7.8.4 Post-conditions 259
7.8.5 Existing features partly or fully covering the use case functionality 259
7.8.6 Potential New Requirements needed to support the use case 259
7.9 Use case on detection of ships on the coast or in rivers 260
7.9.1 Description 260
7.9.2 Pre-conditions 260
7.9.3 Service Flows 260
7.9.4 Post-conditions 261
7.9.5 Existing features partly or fully covering the use case functionality 261
7.9.6 Potential New Requirements needed to support the use case 261
7.10 Use case on advanced modern city transportation system 262
7.10.1 Description 262
7.10.2 Pre-conditions 263
7.10.3 Service Flows 263
7.10.4 Post-conditions 263
7.10.5 Existing features partly or fully covering the use case functionality 263
7.10.6 Potential New Requirements needed to support the use case 265
7.11 Use case on stored sensing data handling 265
7.11.1 Description 265
7.11.2 Pre-conditions 265
7.11.3 Service Flows 265
7.11.4 Post-conditions 266
7.11.5 Existing features partly or fully covering the use case functionality 266
7.11.6 Potential New Requirements needed to support the use case 266
7.12 Use case on improving the credibility of visuals using sensing 266
7.12.1 Description 266
7.12.2 Pre-conditions 266
7.12.3 Service flows 266
7.12.4 Post conditions 267
7.12.5 Existing features partly or fully covering the use cases functionality 267
7.12.6 Potential New Requirements needed to support the use case 267
7.13 Use case on enhanced XR user navigation 267
7.13.1 Description 267
7.13.2 Pre-conditions 267
7.13.3 Service Flows 268
7.13.4 Post-conditions 268
7.13.5 Existing features partly or fully covering the use case functionality 268
7.13.6 Potential New Requirements needed to support the use case 268
7.14 Use case on collaborative robots using digital twinning 269
7.14.1 Description 269
7.14.2 Pre-conditions 270
7.14.3 Service Flows 270
7.14.4 Post-conditions 270
7.14.5 Existing features partly or fully covering the use case functionality 270
7.14.6 Potential New Requirements needed to support the use case 270
7.15 Use case of infrastructure collapse monitoring 271
7.15.1 Description 271
7.15.2 Pre-conditions 272
7.15.3 Service Flows 273
7.15.4 Post-conditions 273
7.15.5 Existing features partly or fully covering the use case functionality 274
7.15.6 Potential New Requirements needed to support the use case 274
7.16 Use case on multi-sensor fusion based sensing for UAV takeoff and landing 275
7.16.1 Description 275
7.16.2 Pre-conditions 276
7.16.3 Service Flows 276
7.16.4 Post-conditions 276
7.16.5 Existing features partly or fully covering the use case functionality 277
7.16.6 Potential New Requirements needed to support the use case 277
7.17 Use case on enabling non-3GPP wireless sensing 277
7.17.1 Description 277
7.17.2 Pre-conditions 278
7.17.3 Service Flows 278
7.17.4 Post-conditions 279
7.17.5 Existing features partially or fully covering the use case functionality 280
7.17.6 Potential New Requirements needed to support the use case 285
7.18 Use case on safe & economic UAV transport 285
7.18.1 Description 285
7.18.2 Pre-conditions 289
7.18.3 Service Flows 289
7.18.4 Post-conditions 290
7.18.5 Existing features partly or fully covering the use case functionality 290
7.18.6 Potential New Requirements needed to support the use case 290
7.19 Use cases on network assisted smart transportation 291
7.19.1 Description 291
7.19.2 Pre-conditions 292
7.19.3 Service Flows 293
7.19.4 Post-conditions 293
7.19.5 Existing features partly or fully covering the use case functionality 293
7.19.6 Potential New Requirements needed to support the use case 294
7.20 Use case on sensing assisted communication in industry park 295
7.20.1 Description 295
7.20.2 Pre-conditions 295
7.20.3 Service Flows 296
7.20.4 Post-conditions 296
7.20.5 Existing features partly or fully covering the use case functionality 296
7.20.6 Potential New Requirements needed to support the use case 296
7.21 Use case on autonomous driving based on network-assisted sensing 297
7.21.1 Description 297
7.21.2 Pre-conditions 297
7.21.3 Service Flows 298
7.21.4 Post-conditions 298
7.21.5 Existing features partly or fully covering the use case functionality 298
7.21.6 Potential New Requirements needed to support the use case 299
7.22 Use case on structural health monitoring 299
7.22.1 Description 299
7.22.2 Pre-conditions 300
7.22.3 Service Flows 300
7.22.4 Post-conditions 300
7.22.5 Existing features partly or fully covering the use case functionality 300
7.22.6 Potential New Requirements needed to support the use case 301
7.23 Use Case on UAV Detection, Classification and Counting 301
7.23.1 Description 301
7.23.2 Pre-conditions 302
7.23.3 Service Flows 303
7.23.4 Post-conditions 303
7.23.5 Existing features partly or fully covering the use cast functionality 303
7.23.6 Potential New Requirements needed to support the use case 304
7.24 Use case on gesture recognition in industrial environments 304
7.24.1 Description 304
7.24.2 Pre-conditions 305
7.24.3 Service Flows 305
7.24.4 Post-conditions 306
7.24.5 Existing features partly or fully covering the use case functionality 306
7.24.6 Potential New Requirements needed to support the use case 306
7.25 Use case on Smart Shopping Tracker 307
7.25.1 Description 307
7.25.2 Pre-conditions 308
7.25.3 Service Flows 308
7.25.4 Post-conditions 309
7.25.5 Existing features partly or fully covering the use case functionality 309
7.25.6 Potential new requirements needed to support the use case 309
8 Ubiquitous Connectivity 309
8.1 General 309
8.2 Use case on ubiquitous and resilient network 309
8.2.1 Description 309
8.2.2 Pre-conditions 311
8.2.3 Service Flows 311
8.2.4 Post-conditions 311
8.2.5 Existing features partly or fully covering the use case functionality 311
8.2.6 Potential New Requirements needed to support the use case 311
8.3 Use case on enhanced user experience with sparse LEO satellite deployment 312
8.3.1 Description 312
8.3.2 Pre-conditions 312
8.3.3 Service Flows 312
8.3.4 Post-conditions 313
8.3.5 Existing features partly or fully covering the use case functionality 313
8.3.6 Potential New Requirements needed to support the use case 313
8.4 Use case on service continuity for wearable mobile devices 313
8.4.1 Description 313
8.4.2 Pre-conditions 314
8.4.3 Service Flows 314
8.4.4 Post-conditions 315
8.4.5 Existing features partly or fully covering the use case functionality 315
8.4.6 Potential New Requirements needed to support the use case 315
8.5 Use case on resilient positioning in satellite networks 315
8.5.1 Description 315
8.5.2 Pre-conditions 316
8.5.3 Service Flows 316
8.5.4 Post-conditions 316
8.5.5 Existing features partly or fully covering the use case functionality 316
8.5.6 Potential New Requirements needed to support the use case 317
8.6 Use case on disaster relief 318
8.6.1 Description 318
8.6.2 Pre-conditions 318
8.6.3 Service Flows 318
8.6.4 Post-conditions 319
8.6.5 Existing features partly or fully covering the use case functionality 319
8.6.6 Potential New Requirements needed to support the use case 320
8.7 Use case on low-energy positioning in satellite networks 321
8.7.1 Description 321
8.7.2 Pre-conditions 321
8.7.3 Service Flows 321
8.7.4 Post-conditions 321
8.7.5 Existing features partly or fully covering the use case functionality 322
8.7.6 Potential New Requirements needed to support the use case 322
8.8 Use case on global mobile video 322
8.8.1 Description 322
8.8.2 Pre-conditions 323
8.8.3 Service Flows 323
8.8.4 Post-conditions 323
8.8.5 Existing features partly or fully covering the use case functionality 324
8.8.6 Potential New Requirements needed to support the use case 324
8.9 Use case on low-altitude logistics supported by NTN 324
8.9.1 Description 324
8.9.2 Pre-conditions 325
8.9.3 Service Flows 325
8.9.4 Post-conditions 326
8.9.5 Existing features partly or fully covering the use case functionality 326
8.9.6 Potential New Requirements needed to support the use case 327
8.10 Use case on hybrid TN and NTN positioning 327
8.10.1 Description 327
8.10.2 Pre-conditions 328
8.10.3 Service Flows 328
8.10.4 Post-conditions 329
8.10.5 Existing features partly or fully covering the use case functionality 329
8.10.6 Potential New Requirements needed to support the use case 329
8.11 Use case on hybrid NTN and GNSS positioning 329
8.11.1 Description 329
8.11.2 Pre-conditions 330
8.11.3 Service Flows 330
8.11.4 Post-conditions 330
8.11.5 Existing features partly or fully covering the use case functionality 330
8.11.6 Potential New Requirements needed to support the use case 332
8.12 Use case on ubiquitous emergency rescue via UAVs 332
8.12.1 Description 332
8.12.2 Pre-conditions 333
8.12.3 Service Flows 333
8.12.4 Post-conditions 334
8.12.5 Existing features partly or fully covering the use case functionality 334
8.12.6 Potential New Requirements needed to support the use case 335
8.13 Use case on HAPS-based rapid deployable network for public safety and disaster response 335
8.13.1 Description 335
8.13.2 Pre-conditions 336
8.13.3 Service Flows 336
8.13.4 Post-conditions 336
8.13.5 Existing features partly or fully covering the use case functionality 337
8.13.6 Potential New Requirements needed to support the use case 337
8.14 Use case on resilient time distribution in satellite networks 337
8.14.1 Description 337
8.14.2 Pre-conditions 337
8.14.3 Service flows 337
8.14.4 Post-conditions 338
8.14.5 Existing features partly or fully covering the use case functionality 338
8.14.6 Potential New Requirements needed to support the use case 339
8.15 Use case on On board Computing in 6G NTN domain 339
8.15.1 Description 339
8.15.2 Pre-conditions 339
8.15.3 Service Flows 339
8.15.4 Post-conditions 340
8.15.5 Existing features partly or fully covering the use case functionality 340
8.15.6 Potential New Requirements needed to support the use case 340
8.16 Use case on positioning integrity in TN and NTN 340
8.16.1 Description 340
8.16.2 Pre-conditions 340
8.16.3 Service Flows 341
8.16.4 Post-conditions 341
8.16.5 Existing features partly or fully covering the use case functionality 341
8.16.6 Potential New Requirements needed to support the use case 341
8.17 Use case on 6G satellite backhaul 341
8.17.1 Description 341
8.17.2 Pre-conditions 343
8.17.3 Service Flows 344
8.17.4 Post-conditions 345
8.17.5 Existing features partly or fully covering the use case functionality 345
8.17.6 Potential New Requirements needed to support the use case 345
8.18 Massive user access over limited satellite links in disasters 346
8.18.1 Description 346
8.18.2 Pre-conditions 346
8.18.3 Service Flows 346
8.18.4 Post-conditions 346
8.18.5 Existing features partly or fully covering the use case functionality 347
8.18.6 Potential New Requirements needed to support the use case 347
9 Immersive Communication 347
9.1 General 347
9.2 Use case on immersive gaming 347
9.2.1 Description 347
9.2.2 Pre-conditions 349
9.2.3 Service Flows 349
9.2.4 Post-conditions 350
9.2.5 Existing features partly or fully covering the use case functionality 350
9.2.6 Potential New Requirements needed to support the use case 351
9.3 Use case on multi-media services with deterministic experience via collaborative processing among UE-network-cloud 352
9.3.1 Description 352
9.3.2 Pre-conditions 353
9.3.3 Service Flows 353
9.3.4 Post-conditions 355
9.3.5 Existing features partly or fully covering the use case functionality 355
9.3.6 Potential New Requirements needed to support the use case 355
9.4 Use case on XR rendering offload support 356
9.4.1 Description 356
9.4.2 Pre-conditions 356
9.4.3 Service Flows 356
9.4.4 Post-conditions 357
9.4.5 Existing features partly or fully covering the use cases functionality 357
9.4.6 Potential New Requirements needed to support the use case 357
9.5 Use case on seamless immersive reality in education 357
9.5.1 Description 357
9.5.2 Pre-conditions 358
9.5.3 Service Flows 358
9.5.4 Post-conditions 358
9.5.5 Existing features partly or fully covering the use case functionality 359
9.5.6 Potential New Requirements needed to support the use case 359
9.6 Use case on collaborative service in multi-site involved immersive communication 360
9.6.1 Description 360
9.6.2 Pre-conditions 360
9.6.3 Service Flows 360
9.6.4 Post-conditions 362
9.6.5 Existing features partly or fully covering the use case functionality 362
9.6.6 Potential New Requirements needed to support the use case 362
9.7 Use case on multiple application media synchronization 363
9.7.1 Description 363
9.7.2 Potential New Requirements needed to support the use case 364
9.8 Use case on holographic telepresence in healthcare 364
9.8.1 Description 364
9.8.2 Pre-conditions 365
9.8.3 Service Flows 365
9.8.4 Post-conditions 366
9.8.5 Existing features partly or fully covering the use case functionality 366
9.8.6 Potential new requirements needed to support the use case 366
9.9 Use case on mixed reality gaming 367
9.9.1 Description 367
9.9.2 Pre-conditions 368
9.9.3 Service Flows 368
9.9.4 Post-conditions 368
9.9.5 Existing features partly or fully covering the use case functionality 368
9.9.6 Potential New Requirements needed to support the use case 369
9.10 Use case on smart life for aging population with immersive real time communication 369
9.10.1 Description 369
9.10.2 Pre-conditions 369
9.10.3 Service Flows 370
9.10.4 Post-conditions 370
9.10.5 Existing features partly or fully covering the use case functionality 370
9.10.6 Potential New Requirements needed to support the use case 371
9.11 Use case on real time VR live service with deterministic user experience 372
9.11.1 Description 372
9.11.2 Pre-conditions 373
9.11.3 Service Flows 373
9.11.4 Post-conditions 374
9.11.5 Existing features partly or fully covering the use case functionality 374
9.11.6 Potential New Requirements needed to support the use case 374
9.12 Use case on personalized interactive immersive guided tour 375
9.12.1 Description 375
9.12.2 Pre-conditions 376
9.12.3 Service Flows 376
9.12.4 Post Conditions 377
9.12.5 Existing features partly or fully covering the use case functionality 377
9.12.6 Potential New Requirements needed to support the use case 378
9.13 Use case on intelligent transmission service for user experience improvement 379
9.13.1 Description 379
9.13.2 Pre-conditions 379
9.13.3 Service Flows 379
9.13.4 Post-conditions 380
9.13.5 Existing features partly or fully covering the use case functionality 380
9.13.6 Potential New Requirements needed to support the use case 380
9.14 Use case on improved user experience 380
9.14.1 Description 380
9.14.2 Pre-conditions 381
9.14.3 Service Flows 382
9.14.4 Post-conditions 382
9.14.5 Existing features partly or fully covering the use case functionality 382
9.14.6 Potential New Requirements needed to support the use case 383
9.15 Use case on coordinating computing and communication for XR rendering 383
9.15.1 Description 383
9.15.2 Pre-conditions 384
9.15.3 Service Flows 384
9.15.4 Post-conditions 385
9.15.5 Existing features partly or fully covering the use case functionality 385
9.15.6 Potential New Requirements needed to support the use case 385
9.16 Use case on communication between heterogeneous immersive terminals 385
9.16.1 Description 385
9.16.2 Pre-conditions 386
9.16.3 Service Flows 386
9.16.4 Post-conditions 386
9.16.5 Existing features partly or fully covering the use case functionality 386
9.16.6 Potential New Requirements needed to support the use case 386
9.17 Use case on Application Context Enhanced Communication Service 386
9.17.1 Description 386
9.17.2 Pre-conditions 387
9.17.3 Service Flows 387
9.17.4 Post-conditions 388
9.17.5 Existing features partly or fully covering the use case functionality 388
9.17.6 Potential New Requirements needed to support the use case 388
9.18 Use Case on Immersive Audio Production in Live Events 388
9.18.1 Description 388
9.18.2 Pre-conditions 391
9.18.3 Service Flows 391
9.18.4 Post-conditions 391
9.18.5 Existing features partly or fully covering the use case functionality 391
9.18.6 Potential New Requirements needed to support the use case 391
10 Massive Communication 392
10.1 General 392
10.2 Use case on wide-area coverage 392
10.2.1 Description 392
10.2.2 Pre-conditions 394
10.2.3 Service Flows 394
10.2.4 Post-conditions 394
10.2.5 Existing features partly or fully covering the use case functionality 395
10.2.6 Potential New Requirements needed to support the use case 395
10.3 Use case on utility infrastructure monitor and control 395
10.3.1 Description 395
10.3.2 Pre-conditions 396
10.3.3 Service Flows 397
10.3.4 Post-conditions 397
10.3.5 Existing features partly or fully covering the use case functionality 397
10.3.6 Potential New Requirements needed to support the use case 398
11 Further Use Cases on Industry and Verticals 398
11.1 General 398
11.2 Use case on communication on board of UAM aircrafts 398
11.2.1 Description 398
11.2.2 Pre-conditions 399
11.2.3 Service Flows 399
11.2.4 Post-conditions 400
11.2.5 Existing features partly or fully covering the use case functionality 400
11.2.6 Potential New Requirements needed to support the use case 400
11.3 Use case on cooperating mobile robots 401
11.3.1 Description 401
11.3.2 Pre-conditions 403
11.3.3 Service Flows 403
11.3.3.1 Scenario: Cooperative Carrying with Mobile Robots 403
11.3.3.2 Scenario: Autonomous Construction Site 404
11.3.4 Post-conditions 404
11.3.5 Existing features partly or fully covering the use case functionality 404
11.3.6 Potential New Requirements needed to support the use case 405
11.4 Use case on real time digital twins 405
11.4.1 Description 405
11.4.2 Pre-conditions 407
11.4.3 Service Flows 408
11.4.4 Post-conditions 409
11.4.5 Existing features partly or fully covering the use case functionality 409
11.4.6 Potential New Requirements needed to support the use case 409
11.5 Immersive media services for advanced air mobility (AAM) enabled by 6G NTN 410
11.5.1 Description 410
11.5.2 Pre-conditions 410
11.5.3 Service Flows 411
11.5.4 Post-conditions 412
11.5.5 Existing features partly or fully covering the use case functionality 413
11.5.6 Potential New Requirements needed to support the use case 414
11.6 Use cases on high-rate aircraft communication services in 6G 415
11.6.1 Description 415
11.6.2 Pre-conditions 415
11.6.3 Service Flows 415
11.6.4 Post-conditions 416
11.6.5 Existing features partly or fully covering the use case functionality 416
11.6.6 Potential New Requirements needed to support the use case 416
11.7 Use case on assisted airspace management of UAV and UAM aircrafts 417
11.7.1 Description 417
11.7.2 Pre-conditions 418
11.7.3 Service Flows 418
11.7.4 Post-conditions 419
11.7.5 Existing features partly or fully covering the use case functionality 419
11.7.6 Potential New Requirements needed to support the use case 419
11.8 Use case on 3D factory model based AR guided task 419
11.8.1 Description 419
11.8.2 Pre-conditions 420
11.8.3 Service Flows 420
11.8.4 Post-conditions 421
11.8.5 Existing features partly or fully covering the use case functionality 421
11.8.6 Potential New Requirements needed to support the use case 421
11.9 Use case on collaborative awareness in dynamic environments - enhancing mutual decision-making through real time data sharing 422
11.9.1 Description 422
11.9.2 Pre-conditions 423
11.9.3 Service Flows 423
11.9.4 Post-conditions 424
11.9.5 Existing features partly or fully covering the use case functionality 424
11.9.6 Potential New Requirements needed to support the use case 425
11.10 Use case on 6G localized network for vertical 425
11.10.1 Description 425
11.10.2 Pre-conditions 426
11.10.3 Service Flows 426
11.10.4 Post-conditions 427
11.10.5 Existing features partly or fully covering the use case functionality 428
11.10.6 Potential New Requirements needed to support the use case 428
11.11 Use case on in-vehicle local communication 429
11.11.1 Description 429
11.11.2 Pre-conditions 429
11.11.3 Service Flows 429
11.11.4 Post-conditions 430
11.11.5 Existing features partly or fully covering the use case functionality 430
11.11.6 Potential New Requirements needed to support the use case 430
11.12 Use case on supporting collaborative intelligence using multiple service robots 431
11.12.1 Description 431
11.12.2 Pre-conditions 431
11.12.3 Service Flows 431
11.12.4 Post-conditions 432
11.12.5 Existing features partly or fully covering the use case functionality 432
11.12.6 Potential New Requirements needed to support the use case 433
11.13 Use case on cooperative networking under extreme conditions – mining, agriculture, and more 433
11.13.1 Description 433
11.13.2 Pre-conditions 435
11.13.3 Service Flows 437
11.13.4 Post-conditions 438
11.13.5 Existing features partly or fully covering the use case functionality 438
11.13.6 Potential New Requirements needed to support the use case 439
11.14 Use case on seamless connectivity for 6G-enabled Mission Critical services 439
11.14.1 Description 439
11.14.2 Pre-conditions 440
11.14.3 Service Flows 441
11.14.4 Post-conditions 442
11.14.5 Existing features partly or fully covering the use case functionality 442
11.14.6 Potential New Requirements needed to support the use case 446
11.15 Use case on service robots in smart community 446
11.15.1 Description 446
11.15.2 Pre-conditions 446
11.15.3 Service Flows 447
11.15.4 Post-conditions 448
11.15.5 Existing features partly or fully covering the use case functionality 448
11.15.6 Potential New Requirements needed to support the use case 448
11.16 Use case on critical infrastructure monitoring 449
11.16.1 Description 449
11.16.2 Pre-conditions 449
11.16.3 Service Flows 450
11.16.4 Post-conditions 450
11.16.5 Existing features partly or fully covering the use case functionality 450
11.16.6 Potential New Requirements needed to support the use case 450
11.17 Use case on remote and automatic construction 450
11.17.1 Description 450
11.17.2 Pre-conditions 451
11.17.3 Service Flows 452
11.17.4 Post-conditions 452
11.17.5 Existing features partly or fully covering the use case functionality 452
11.17.6 Potential New Requirements needed to support the use case 452
11.18 Use case on regulated services resiliency in disaster conditions 452
11.18.1 Description 452
11.18.2 Pre-conditions 453
11.18.3 Service Flows 453
11.18.4 Post-conditions 454
11.18.5 Existing features partly or fully covering the use case functionality 454
11.18.6 Potential New Requirements needed to support the use case 454
11.19 Use case on network-requested execution of service functions in connected vehicles 455
11.19.1 Description 455
11.19.2 Pre-conditions 455
11.19.3 Service Flows 455
11.19.4 Post-conditions 456
11.19.5 Existing features partly or fully covering the use case functionality 456
11.19.6 Potential New Requirements needed to support the use case 456
11.20 Use case on network managed localized communication for verticals 456
11.20.1 Description 456
11.20.2 Pre-conditions 456
11.20.3 Service Flows 456
11.20.4 Post-conditions 457
11.20.5 Existing features partly or fully covering the use case functionality 457
11.20.6 Potential New Requirements needed to support the use case 457
11.21 Use case on Industrial IoT 457
11.21.1 Description 457
11.21.2 Pre-conditions 458
11.21.3 Service Flows 458
11.21.4 Post-conditions 458
11.21.5 Existing features partly or fully covering the use case functionality 458
11.21.6 Potential New Requirements needed to support the use case 459
11.22 Use case on spatial computing enabled dynamic material management 459
11.22.1 Description 459
11.22.2 Pre-conditions 460
11.22.3 Service Flows 460
11.22.4 Post-conditions 461
11.22.5 Existing features partly or fully covering the use case functionality 461
11.22.6 Potential New Requirements needed to support the use case 461
11.23 Use case on independent 6G local network for factory 461
11.23.1 Description 461
11.23.2 Pre-conditions 462
11.23.3 Service Flows 462
11.23.4 Post-conditions 462
11.23.5 Existing features partly or fully covering the use case functionality 462
11.23.6 Potential New Requirements needed to support the use case 463
11.24 Use case on utility direct transfer trip for distributed energy resources integration and protection 463
11.24.1 Description 463
11.24.2 Pre-conditions 464
11.24.3 Service Flows 465
11.24.4 Post-conditions 465
11.24.5 Existing features partly or fully covering the use case functionality 465
11.24.6 Potential New Requirements needed to support the use case 465
11.25 Use case on monitoring utility transmission grid assets 465
11.25.1 Description 465
11.25.2 Pre-conditions 466
11.25.3 Service Flows 466
11.25.4 Post-conditions 466
11.25.5 Existing features partly or fully covering the use case functionality 466
11.25.6 Potential New Requirements needed to support the use case 467
11.26 Use case on 6G-enabled decentralized grid power contract 467
11.26.1 Description 467
11.26.2 Pre-conditions 468
11.26.3 Service Flows 468
11.26.4 Post-conditions 468
11.26.5 Existing features partly or fully covering the use case functionality 468
11.26.6 Potential New Requirements needed to support the use case 469
W Other Use Cases 469
W.1 Use case on computing service for XR gaming acceleration 469
W.1.1 Description 469
W.1.2 Pre-conditions 471
W.1.3 Service Flows 471
W.1.4 Post-conditions 472
W.1.5 Existing features partly or fully covering the use case functionality 472
W.1.6 Potential New Requirements needed to support the use case 472
W.2 Use case on computing service enabling personal AI agent 473
W.2.1 Description 473
W.2.2 Pre-conditions 473
W.2.3 Service Flows 474
W.2.4 Post-conditions 474
W.2.5 Existing features partly or fully covering the use case functionality 474
W.2.6 Potential New Requirements needed to support the use case 475
W.3 Use case on computing service in operator managed data network 475
W.3.1 Description 475
W.3.2 Precondition 475
W.3.3 Service Flows 475
W.3.4 Post-condition 476
W.3.5 Existing features partly or fully covering the use case functionality 476
W.3.6 Potential New Requirements needed to support the use case 476
W.4 Use case on network offering information-as-a-service 476
W.4.1 Description 476
W.4.2 Pre-conditions 476
W.4.3 Service Flows 477
W.4.4 Post-conditions 477
W.4.5 Existing features partly or fully covering the use case functionality 477
W.4.6 Potential New Requirements needed to support the use case 477
X Other Considerations 477
X.1 Considerations on Lawful Interception 478
Y Consolidated Potential Requirements 478
Z Conclusion and Recommendations 478
Annex A: Additional Use Cases 479
A.1 Use Case #X 479
A.2 Use Case #Y 479
A.3 Use Case #Z 479
Annex <X>: Change history 481
Foreword
This Technical Report has been produced by the 3rd Generation Partnership Project (3GPP).
The contents of the present document are subject to continuing work within the TSG and may change following formal TSG approval. Should the TSG modify the contents of the present document, it will be re-released by the TSG with an identifying change of release date and an increase in version number as follows:
Version x.y.z
where:
x the first digit:
1 presented to TSG for information;
2 presented to TSG for approval;
3 or greater indicates TSG approved document under change control.
y the second digit is incremented for all changes of substance, i.e. technical enhancements, corrections, updates, etc.
z the third digit is incremented when editorial only changes have been incorporated in the document.
In the present document, modal verbs have the following meanings:
shall indicates a mandatory requirement to do something
shall not indicates an interdiction (prohibition) to do something
The constructions "shall" and "shall not" are confined to the context of normative provisions, and do not appear in Technical Reports.
The constructions "must" and "must not" are not used as substitutes for "shall" and "shall not". Their use is avoided insofar as possible, and they are not used in a normative context except in a direct citation from an external, referenced, non-3GPP document, or so as to maintain continuity of style when extending or modifying the provisions of such a referenced document.
should indicates a recommendation to do something
should not indicates a recommendation not to do something
may indicates permission to do something
need not indicates permission not to do something
The construction "may not" is ambiguous and is not used in normative elements. The unambiguous constructions "might not" or "shall not" are used instead, depending upon the meaning intended.
can indicates that something is possible
cannot indicates that something is impossible
The constructions "can" and "cannot" are not substitutes for "may" and "need not".
will indicates that something is certain or expected to happen as a result of action taken by an agency the behaviour of which is outside the scope of the present document
will not indicates that something is certain or expected not to happen as a result of action taken by an agency the behaviour of which is outside the scope of the present document
might indicates a likelihood that something will happen as a result of action taken by some agency the behaviour of which is outside the scope of the present document
might not indicates a likelihood that something will not happen as a result of action taken by some agency the behaviour of which is outside the scope of the present document
In addition:
is (or any other verb in the indicative mood) indicates a statement of fact
is not (or any other negative verb in the indicative mood) indicates a statement of fact
The constructions "is" and "is not" do not indicate requirements.
1 Scope
The present document aims to identify high level principles and use cases - to define potential requirements to enable the 6G system to support the needs of new and enhanced services and scenarios, based on, but not limited to, IMT-2030 usage scenarios. This endeavour includes identifying and grouping use cases with common characteristics and potential requirements for further development in the next stage of the work.
The study also includes "System and Operational Aspects" facilitating system and network operation features that underpin overall operation, covering aspects that apply across use cases and services, and those that relate to network operations. These aspects include, for example: migration, interworking, roaming, interconnection, network simplification, network sharing, security, privacy, resilience, sustainability and energy efficiency, device diversity, support of legacy services.
2 References
The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or nonspecific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
Editor's Note: all References numbers to be corrected, missing references to be added
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[45] 3GPP TS 36.211: "Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and modulation".
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[54] 3GPP TS 22.280: "Mission Critical Services Common Requirements (MCCoRe); Stage 1".
[55] 3GPP TS 22.281: "Mission Critical (MC) video".
[56] 3GPP TS 22.282: "Mission Critical (MC) data".
[57] 3GPP TS 22.262: "Message service within the 5G System (5GS); Stage 1".
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[60] 3GPP TS 22.011: "Service accessibility".
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[62] 3GPP TS 22.268: "Public Warning System (PWS) requirements".
[63] 3GPP TS 22.153: "Multimedia priority service".
[64] 3GPP TS 22.104: "Service requirements for cyber-physical control applications in vertical domains".
[65] 3GPP TS 22.185: "Service requirements for V2X services".
[66] 3GPP TS 22.186: "Service requirements for enhanced V2X scenarios".
[67] 3GPP TS 22.263: "Service requirements for Video, Imaging and Audio for Professional Applications (VIAPA) ".
[68] 3GPP TS 22.115: "Service aspects; Charging and billing".
[69] 3GPP TS 22.289: "Mobile communication system for railways".
[70] 3GPP TS 22.468: "Group Communication System Enablers (GCSE) ".
[71] 3GPP TS 22.368: "Service requirements for Machine-Type Communications (MTC); Stage 1".
[72] 3GPP TR 22.949: "Study on a generalized privacy capability".
[73] 3GPP TR 33.849: "Study on subscriber privacy impact in 3GPP".
[74] 3GPP TS 22.278: "Service requirements for the Evolved Packet System (EPS) ".
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[77] 3GPP TR 28.915: "Study on management aspects of Network Digital Twin".
[78] 3GPP TS 23.527: "5G System; Restoration procedures".
[79] 3GPP TR 29.866: "Study on IMS Disaster Prevention and Restoration Enhancement".
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[138] 3GPP TS 22.228: "Service requirements for the Internet Protocol (IP) multimedia core network subsystem (IMS); Stage 1".
[139] 3GPP TS 28.105: "Management and orchestration; Artificial Intelligence/ Machine Learning (AI/ML) management".
[140] 3GPP TS 23.501: "System architecture for the 5G System (5GS) ".
[141] 3GPP TS 23.503: "Policy and charging control framework for the 5G System (5GS); Stage 2".
[142] 3GPP TS 23.228: "IP Multimedia Subsystem (IMS); Stage 2".
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[144] 3GPP TS 26.114: "IP Multimedia Subsystem (IMS); Multimedia Telephony; Media handling and interaction".
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[164] 3GPP TR 22.847: "Study on supporting tactile and multi-modality communication services; Stage 1".
[165] 3GPP TR 26.854: "Study on Haptics in 5G Media Services".
[166] 3GPP TR 26.998: "Support of 5G glass-type Augmented Reality / Mixed Reality (AR/MR) devices".
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[202] ETSI GR ISC 001 V1.1.1 (2025-03): "Integrated Sensing And Communications (ISAC); Use Cases and Deployment Scenarios".
[203] 3GPP TS 38.104: "Base Station (BS) radio transmission and reception".
[204] 3GPP TR 38.799: "Study on additional topological enhancements for NR".
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[209] GSMA RCC.71: "RCS Universal Profile Service Definition Document".
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[212] 3GPP TS 32.298: "Charging data record (CDR) parameter description".
[213] 3GPP TS 32.299: "Telecommunication management; Charging management; Diameter charging applications".
[214] X. Lin, L. Kundu, C. Dick, E. Obiodu, TT. Mostak, "6G Digital Twin Networks: From Theory to Practice", https://arxiv.org/pdf/2212.02032.
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[233] 3GPP TR 28.829: "Study on network and service operations for energy utilities".
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[238] 3GPP TS 38.331: "NR; Radio Resource Control (RRC); Protocol specification".
[239] 3GPP TS 32.422: "Telecommunication management; Subscriber and equipment trace; Trace control and configuration management".
[240] 3GPP TS 23.273: "5G System (5GS) Location Services (LCS); Stage 2".
[241] 3GPP TS 33.558: "Security aspects of enhancement of support for enabling edge applications".
[242] 3GPP TS 26.531: "Data Collection and Reporting; General Description and Architecture".
[243] 3GPP TR 38.843: "Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR air interface".
[244] NIST CyberSecurity Framework[[SUGGESTION_START]]:[[SUGGESTION_END]] [[SUGGESTION_START]]"[[SUGGESTION_END]]The CSF 1.1 Five Functions[[SUGGESTION_START]]"[[SUGGESTION_END]], https://www.nist.gov/cyberframework/getting-started/online-learning/five-functions?ref=hackernoon.com.
[245] 3GPP TS 23.304: [[SUGGESTION_START]]"[[SUGGESTION_END]]Proximity based Services (ProSe) in the 5G System (5GS)[[SUGGESTION_START]]"[[SUGGESTION_END]].
[246] Internet Engineering Task Force (IETF), RFC 7181, "The Optimized Link State Routing Protocol Version 2". (RFC 7181: The Optimized Link State Routing Protocol Version 2).
[247] 3GPP TS 23.434: "Service Enabler Architecture Layer for Verticals (SEAL); Functional architecture and information flows".
[248] 3GPP TR 22.874: "5G System (5GS); Study on traffic characteristics and performance requirements for AI/ML model transfer".
[249] 3GPP TR 22.876: "Study on AI/ML Model Transfer Phase2".
[250] 3GPP TS 33.501: "Security architecture and procedures for 5G System".
[251] 3GPP TS 28.104: "Management and orchestration; Management Data Analytics (MDA)".
[252] 3GPP TS 28.404: "Telecommunication management; Quality of Experience (QoE) measurement collection; Concepts, use cases and requirements".
[253] 3GPP TS 28.405: "Telecommunication management; Quality of Experience (QoE) measurement collection; Control and configuration".
[254] 3GPP TS 28.406: "Telecommunication management; Quality of Experience (QoE) measurement collection; Information definition and transport".
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[257] 3GPP TS 28.538: "Management and orchestration; Edge Computing Management".
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[259] 3GPP TS 22.220: "Service requirements for Home Node B (HNB) and Home eNode B (HeNB)".
[260] Void.
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[337] World Economic Forum, [[SUGGESTION_START]]"[[SUGGESTION_END]]Top 10 Emerging Technologies of 2025[[SUGGESTION_START]]"[[SUGGESTION_END]], June 2025, [[SUGGESTION_START]]WEF_Top_10_Emerging_Technologies_of_2025.pdf[[SUGGESTION_END]].
[338] European Union Agency for the Space Programme (EUSPA), [[SUGGESTION_START]]"[[SUGGESTION_END]]Power-Efficient Positioning for Internet Of Things[[SUGGESTION_START]]"[[SUGGESTION_END]], June 2020, https://www.euspa.europa.eu/sites/default/files/external/publications/gsa_internet_of_things_white_paper.pdf.
[339] 3GPP TS 29.571: "5G System; Common Data Types for Service Based Interfaces; Stage 3".
[340] 3GPP TS 28.541: "Management and orchestration; 5G Network Resource Model (NRM); Stage 2 and stage 3".
[341] 3GPP TR 23.737: "Study on architecture aspects for using satellite access in 5G".
[342] 3GPP TR 23.700-28: "Study on Integration of satellite components in the 5G architecture; Phase 2".
[343] 3GPP TR 23.700-29: "Study on integration of satellite components in the 5G architecture; Phase 3".
[344] 3GPP TR 23.700-19: "Study on Integration of satellite components in the 5G architecture; Phase 4".
[345] 3GPP TR 23.700-27: "Study on satellite backhauling".
[346] ETSI TS 103 246-3: "Satellite Earth Stations and Systems (SES); GNSS Based Location Systems; Part 3: Performance Requirements".
[347] European Union Agency for the Space Programme (EUSPA): "Report on Maritime and Inland Waterways – User Needs and Requirements", 2024. ([[SUGGESTION_START]]Report on Maritime and Inland Waterways - User Needs and Requirements.pdf[[SUGGESTION_END]]).
[348] 3GPP TR 38.857: "Study on NR Positioning Enhancements".
[349] 3GPP TR 38.859: "Study on expanded and improved NR positioning".
[350] Avanti Space: White Paper - [[SUGGESTION_START]]"[[SUGGESTION_END]]Connectivity Through Backhaul[[SUGGESTION_START]]"[[SUGGESTION_END]] (https://www.avanti.space/wp-content/uploads/2022/05/Satellite-Backhaul-whitepaper-2022.pdf).
[351] Y. Turk and E. Zeydan, "Satellite Backhauling for Next Generation Cellular Networks: Challenges and Opportunities[[SUGGESTION_START]]"[[SUGGESTION_END]], in IEEE Communications Magazine, vol. 57, no. 12, pp. 52-57, December 2019. [[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]Satellite Backhauling for Next Generation Cellular Networks: Challenges and Opportunities | IEEE Journals & Magazine | IEEE Xplore[[SUGGESTION_END]][[SUGGESTION_START]]).[[SUGGESTION_END]]
[352] [[SUGGESTION_START]]Definition of JPEG XS: [[SUGGESTION_END]]https://en.wikipedia.org/wiki/JPEG_XS.
[353] [[SUGGESTION_START]]Rivera F., Thomas G., and Weir[[SUGGESTION_END]][[SUGGESTION_START]] B., [[SUGGESTION_END]][[SUGGESTION_START]]"Trialling live music performances in immersive 3D worlds: MAX-R[[SUGGESTION_END]][[SUGGESTION_START]]"[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] 13 September 2024, [[SUGGESTION_END]]https://www.bbc.co.uk/rd/articles/2024-09-immersive-augmented-virtual-reality-metaverse-3d.
[354] [[SUGGESTION_START]]6G for C[[SUGGESTION_END]][[SUGGESTION_START]]onnected Sky[[SUGGESTION_END]][[SUGGESTION_START]] Project[[SUGGESTION_END]][[SUGGESTION_START]]: "6G Sky" [[SUGGESTION_END]]https://www.6G-sky.net.
[355] European Union Critical Communication System (EUCCS): https://www.psc-europe.eu/the-eu-critical-communication-system-enhancing-europes-crisis-response/.
[356] [[SUGGESTION_START]]International Electrotechnical Commission: [[SUGGESTION_END]]IEC 61850-90-5:2012, Use of IEC 61850 to transmit Synchrophasors information according to IEEE C37.118.
[357] GSMA PRD IR.92: "IMS Profile for Voice and SMS".
[358] 3GPP TS 23.180: "Mission critical services support in the Isolated Operation for Public Safety (IOPS) mode of operation".
[359] 3GPP TS 23.316: "Wireless and wireline convergence access support for the 5G System (5GS)".
[360] 3GPP TS 23.222: "Common API Framework for 3GPP Northbound APIs".
[361] European Industry Construction Federation (FIEC): https://www.fiec.eu/priorities/digitalisation-construction-40-and-bim.
[362] [[SUGGESTION_START]]Jeong [[SUGGESTION_END]][[SUGGESTION_START]]Hojun[[SUGGESTION_END]][[SUGGESTION_START]]’s article on KT’s Intelligent UAM Traffic Management System at MWC 24,[[SUGGESTION_END]] [[SUGGESTION_START]]26 February 2024, [[SUGGESTION_END]]https://www.mk.co.kr/en/it/10950602.
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]363] [[SUGGESTION_END]][[SUGGESTION_START]]5G Media Action Group ([[SUGGESTION_END]][[SUGGESTION_START]]5G-MAG[[SUGGESTION_END]][[SUGGESTION_START]]) R[[SUGGESTION_END]][[SUGGESTION_START]]eport[[SUGGESTION_END]][[SUGGESTION_START]]: "[[SUGGESTION_END]][[SUGGESTION_START]]Time synchronization services for media production over 5G networks[[SUGGESTION_END]][[SUGGESTION_START]]", [[SUGGESTION_END]][[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]www.5g-mag.com/post[[SUGGESTION_END]][[SUGGESTION_START]]/time-synchronization-services-for-media-production-over-5g-networks[[SUGGESTION_END]][[SUGGESTION_START]]). [[SUGGESTION_END]]
3 Definitions of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the terms given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
6G AI Service: a service provided by the 6th Generation (6G) network where AI functionalities (e.g. AI model training, AI model management or AI inference) are made available to a subscriber/user or an authorized application on the User Equipment (UE) or on an application server (AS).
6G Computing Service: a service provided by 6G network utilizing computing resources in Service Hosting Environment, which can be used by a subscriber (via UE)/3rd party.
NOTE 1: The computing resources can refer to hardware and/or software that provides the required processing, storage capability etc. to perform computational tasks (e.g. XR rendering).
6G System Data: the data that is controlled by the 6G system and can be generated or collected by the 6G system.
NOTE 2: 6G system data is different from traditional user traffic data which is application level data being transmitted through the 3GPP system for user related services.
6G Wireless sensing: 6G system feature providing capabilities to get information about characteristics of the environment and/or objects within the environment (e.g. shape, size, orientation, speed, location, distances or relative motion between objects, etc[[SUGGESTION_START]].[[SUGGESTION_END]]) using radio frequency signals.
NOTE 3: The 6G Wireless sensing service can use data acquired with either NR-based radio signals, non-3GPP radio signals, or a combination.
A[[SUGGESTION_START]]I[[SUGGESTION_END]] Agent: an automated intelligent entity that achieves a specific goal (autonomously or not) on behalf of another entity, by e.g. interacting with its environment, acquiring contextual information, reasoning, self-learning, decision-making, executing tasks (independently or in collaboration with other AI Agents)
Cooperating UEs: a group of UEs that have registered as part of their (shared) subscription that they are allowed to be coordinated by the network to cooperate when they are in proximity of each other.
Digital Twin: a real time representation of physical assets in a digital world.
NOTE 4: This definition was taken from ITU-T Recommendation Y.3090 [113].
Energy Supply: the delivery of electricity to a physical location. This is typically realized by placing two or more wires coming from a DSO at a geographical location and connecting those wires to a metering device.
NOTE 5: This definition was taken from TS 28.318 [232].
Intelligent assistance service: a 3GPP service to help subscribers and third-party applications perform their tasks or services, e.g. using an AI Agent.
NOTE 6: For example, intelligent assistance service in the context of autonomous driving can be to support collision avoidance, parking assistance, emergency trajectory alignment, automated intersection-crossing, etc.
Intelligent Communication Assistant: the virtual intelligent communication assistant locates in operator network and interacts with the users through voice, video, text, gestures or other modalities. The assistant can be customized for each particular user by accessing user data and network data which are stored or collected in the network, with user’s consent. It can provide various communication services and support individual users based on user’s intention and requirement utilizing AI capability. One subscriber can have one or more Intelligent Communication Assistants.
Intent: expectations including requirements, goals and constraints without specifying how to achieve them. [147]
NOTE 7: Intent can be used for 6G services as well as Operations, Administration, and Management (OAM).
Editor’s note: NOTE 7 is FFS.
Editor’s note: this definition is FFS for further enhancement along the study goes on. If more detail regarding Intent is necessary to support the use cases in this TR it may be introduced in an Annex.
Maximum slice energy credit limit: a policy establishing an upper bound on the aggregate quantity of energy consumption by the 6G system to provide services for a specific slice, e.g. in kilowatt hours.
Network Digital Twin: virtual replica of (part of) a mobile network to emulate (or simulate) the behaviour of the actual network.
Editor’s Note: it is FFS to update this definition.
Network Federation: refers to the interoperability of two or more 6G networks, enabling them to share resources and services, to achieve shared objectives. Federated 6G networks maintain their autonomy but coordinate to share resources, or services, ensuring mutual benefits without compromising individual operational control or data privacy.
NOTE 8: Network federation is currently defined in TS 28.538 [257], TS 23.558 [52] and allows Mobile Network Operators (MNOs) to share edge computing resources.
non-3GPP sensing station: a device capable of emitting and/or receiving non-3GPP radio signals specified in IEEE 802.1bf [201] that can result in acquisition of non-3GPP sensing data.
NOTE 9: The non-3GPP sensing station is owned, operated and deployed by the network operator or its business partner, including scenarios in which the equipment is owned and operated by the customer of the network operator.
Personal Data: any information relating to a user or subscriber that can be used to, either directly or indirectly, identify that user or subscriber, or to distinguish that user or subscriber from others.
Satellite access: direct connectivity between the UE and the satellite.
Satellite Constellation: a set of satellites working together as a system or network. A satellite constellation can be composed of satellites in the same orbit types or different orbits (GSO, NGSO) with different characteristics.
Sensing target density: total number of objects to be sensed per geographic area. It is a measure of how many objects the 3GPP system can detect, identify and/or track within a target sensing area.
Service Hosting Environment: the environment, located inside of 6G network and fully controlled by the operator, where Hosted Services are offered from.
Serving satellite: a satellite providing the satellite access to a UE. In the case of NGSO (Non-Geostationary Satellite Orbit), the serving satellite is always changing due to the nature of the constellation.
3.2 Symbols
Void.
3.3 Abbreviations
For the purposes of the present document, the abbreviations given in 3GPP TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in 3GPP TR 21.905 [1].
2D Two Dimensions
5GAA 5G Automotive Association
6DoF Six Degrees of Freedom
6G 6th Generation
6GS 6G System
A2P Application-to-Point
AA Application Awareness
AAC Advanced Audio Coding
AAM Advanced Air Mobility
AD Autonomous Driving
ADAS Advanced Driving Assistance System
AF Application Function
AGI Artificial General Intelligence
AGL Above Ground Level
AGV Automated Guided Vehicle
AI Artificial Intelligence
AMR Autonomous Mobile Robot
AMR-NB Adaptive Multi-Rate Narrowband
APP Application
AS Application Server
ASP Application Service Provider
BVLOS Beyond Visual Line Of Sight
CA Certificate Authority
CAGR Compound Annual Growth Rate
CAPEX CAPital EXpenditure
CAPIF Common API Framework
CCRC Continuing Care Retirement Community
CO2 Carbon dioxide
CIS Common Information Services
Cobot Collaborative Robot
CPE Customer Premises Equipment
CPN Customer Premises Network
CRQC Cryptographically Relevant Quantum Computer
CVD Coordinated Vulnerability Disclosure
DAA Detect And Avoid
DAC Digital Asset Container
DER Distributed Energy Resources
DNN Deep Neural Network
DR Demand-Response
DSO Distribution Service Operator
DT Digital Twin
DTT Direct Transfer Trip
EDT Environmental Digital Twin
EEC Edge Enabler Client
EES Edge Enabler Server
eMTC enhanced Machine Type Communication
EPC evolved Packet Core Network
eRG Evolved Residential Gateway
eSIM embedded SIM
EV Electric Vehicle
eVTOL Electric Vertical Take-Off and Landing
FAA (US) Federal Aviation Administration
FBS False Base Station
FCI Fault Circuit Indicator
FL Federated Learning
FLISR Fault Location, Isolation, and Service Restoration
FWA Fixed Wireless Access
GDC Global Digital Compact
GenAI Generative AI
GEO Geostationary satellite Earth Orbit
gNB NR Node B
GNSS Global Navigation Satellite System
GPU Graphics Processing Unit
GSMA GSM Association
GSO Geosynchronous Orbit
HAPS High-Altitude Platform Station
HD High Definition
HDR High Dynamic Range
HEVC High-Efficiency video Coding
HIBS HAPS IMT Base Station
HMD Head Mounted Display
HW Hardware
IC Incident Commander
ICT Information and Communication Technologies
ID Identity
ID Intelligent Driving
IMS DC IMS Data Channel
IMU Inertial Measurement Unit
IOPS Isolated E-UTRAN Operation for Public Safety
ISAC Integrated Sensing and Communication
ISL Inter-Satellite Link
ITS Intelligent Transport System
KVI Key Value Item/Indicator?
LEO Low Earth Orbit
LiDAR Light Detection And Ranging
LLM Large Language Model
LMR Land Mobile Radio
LoS Line-of-Sight
LPP LTE Positioning Protocol
M-IoT Massive Internet of Things
MCData Mission Critical Data
MCPTT Mission Critical Push to Talk
MCVideo Mission Critical Video
MEC Multi-access Edge Computing
MEO Medium Earth Orbit
ML Machine Learning
MnS Management Service(s)
MPS Multimedia Priority Service
MR Mixed Reality
NB-IoT Narrowband IoT
NDS Network Domain Security
NDS/AF NDS Authentication Framework
NDT Network Digital Twin
NEF Network Exposure Function
NGSO Non Geo Synchronous Orbit
NLOS Non-Line-of-Sight
NPN Non-Public Network
NR New Radio
NTN Non-Terrestrial Network
NWDAF Network Data Analytics Function
OAM Operations, Administration, and Management
OEM Original Equipment Manufacturer
OPEX Operational Expenditure
OT Operational Technology
OTT Over The Top
PDB Packet Delay Budget
PDT Professional/Police Digital Trunking
PIN Personal IoT Networks
PNG Portable Network Graphics
PNT Positioning, Navigation, and Timing
PRAS Premises Radio Access Station
PSAP Public Safety Answering Point
PSBN Public Safety Broadband Network
R&D Research and Development
RAG Retrieval Augmented Generation
RCS Radar Cross Section
RCS Rich Communication Services
RGB Red-Green-Blue
RTK Real-Time Kinematic
SDG Sustainable Development Goal
SLAM Simultaneous Localization and Mapping
SLM Small Language Model
SNPN Standalone NPN
SSC Sensing Service Consumer
SSC Service and Session Continuity
SUPI Subscription Permanent Identifier
SWAP Size, Weight and Power
TBS Terrestrial Beacon System
TN Terrestrial Network
TRP Transmitter Receiver Point
TPDIS Third Party Digital Identity System
UAC Unified Access Control
UAM Urban Air Mobility
UAV Uncrewed Aerial Vehicle
UBBA Utility Broadband Alliance
UPF User Plane Function
UPT UE Perceived Throughput
USD US Dollars
USS Uncrewed Aerial System Service Supplier
UTM Uncrewed Aerial System Traffic Management
UX User eXperience
VLEO Very Low Earth Orbit
VLM Vision-Language Model
VN Virtual Network
VRU Vulnerable Road User
XR eXtended Reality
4 Overview
Editor's Note: This part will cover the high-level value and principle of 6G system.
Editor's note: To introduce 6G with some generic justification and structure of the TR.
Overview text…
4.1 Sustainability
According to the United Nations, “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” [29]
Many related target areas and actions are identified in the United Nations 17 Sustainable Development Goals (UN SDGs) [87], which are categorized into environmental, social and economic goals.
ITU-R has identified “the motivation for the development of IMT-2030 is to continue to build an inclusive information society towards contributing to support the United Nations Sustainable Development Goals (SDGs)." [27] "Sustainability is a foundational aspiration of future IMT systems. IMT-2030 is expected to help address the need for increased environmental, social, and economic sustainability”. [27]
Editor's Note: this sub-clause on sustainability may be moved as another 4.x sub-clause or as normal text of the Overview clause.
5 System and Operational Aspects
5.1 General
The 6G system shall support new 6G services and capabilities and it is assumed that the 6G system shall support existing 5G services and system requirements, unless explicitly identified.
It is expected that the 6G system may enhance existing legacy services and capabilities, maximizing backwards compatibility.
Improving user experience is an on-going concern for network operators, and service continuity during changes in network access or connectivity is especially important for services that are sensitive to disruptions. The 6G system is expected to support service continuity unless explicitly identified.
5.2 Interworking with legacy systems
Subject to operator’s policy, the 6G system shall support mobility procedures between the core network (CN) of the 6G system and a 5G core network with minimum impact to the user experience (e.g. Quality of Service (QoS), Quality of Experience (QoE)).
Subject to operator’s policy, the 6G system shall support mobility procedures between the core network of the 6G System and the evolved Packet Core Network (EPC) with minimum impact to the user experience (e.g. QoS, QoE).
NOTE 1: Complexity on introducing the above interworking requirement needs to be minimized.
Requirements in [14] clause 5.1.2.2, related to inter-RAT capabilities not to be supported by 5GS, apply similarly to 6GS with the following modification:
- voice service continuity from the radio access network of the 6G system to UTRAN CS shall not be supported.
NOTE 2: Terms referring to 5G (e.g. “the 5G system”, “NG RAN”) should be implicitly replaced by the corresponding terms for 6G (e.g. “6G system”, “radio access network of the 6G system”) in those requirements.
5.3 Support of non-3GPP access
Interoperability among the various access technologies supported by the 6G system will be imperative. For optimization and resource efficiency, the 6G system will select the most appropriate 3GPP or non-3GPP access technology for a service, potentially allowing multiple access technologies to be used simultaneously for one or more services active on the UE.
The 6G system shall be able to support a user to access network services via 3GPP and/or non-3GPP access (e.g. WLAN or Wireline).
The 6G system shall be able to support 5G system requirements for non-3GPP access as defined in [14].
Subject to operator’s policy, the 6G system shall support mobility between the 6G 3GPP access and non-3GPP access, with minimum impact to the user experience (e.g. QoS, QoE).
5.4 Support for legacy service requirements
5.4.1 Introduction
The 6G system is assumed to support most of the existing 5GS service requirements plus new requirements.
The following sub-clauses cover a list of services to be supported (or not supported) and other 5GS requirements implicitly supported (or explicitly excluded).
The requirements in these sub-clauses apply also to non-3GPP access and satellite access, which are assumed to be supported by the 6G system.
6G requirements about inter-system interworking are defined in clause 5.2.
5.4.2 Support of legacy services
The 6G system shall be able to support the following services:
- Mission Critical Services, i.e. MCPTT, MCData, MCVideo, ref TS 22.179 [53], TS 22.280 [54], TS 22.281 [55]. and TS 22.282 [56],
- Message service, ref TS 22.262 [57],
- Short Message Service (SMS), ref TS 22.101 [58],
- Multimedia communication services, ref TS 22.101 [58], TS 22.261 [14], TS 22.173 [59],
- IMS Multimedia Telephony Service, ref TS 22.261 [14], TS 22.173 [59],
- Roaming services, ref TS 22.011 [60], TS 22.101 [58], TS 22.261 [14],
- Location and positioning services, ref TS 22.261 [14], TS 22.071 [61],
- Broadcast and Multicast Services, ref TS 22.261 [14],
- Emergency Services, ref TS 22.101 [58],
- Public Warning System (PWS), ref TS 22.268 [62],
- Multimedia Priority Service (MPS), ref TS 22.153 [63],
- Lawful Interception, ref TS 22.261 [14] and TS 33.126 [312], and
- Other regulatory services, based on regional/national regulatory requirements.
The following services do not need to be supported by the 6G system:
- CS related telephony services, e.g. CS Fallback, CS based voice call[[SUGGESTION_START]].[[SUGGESTION_END]]
5.4.3 Support of other legacy requirements
The 6G system shall be able to support 5G system requirements (functional and performance requirements) defined e.g. in TS 22.261 [14], TS 22.104 [64], TS 22.011 [60], TS 22.173 [59], TS 22.071 [61], TS 22.262 [57], TS 22.185 [65], TS 22.186 [66], TS 22.125 [35], TS 22.263 [67], TS 22.115 [68], TS 22.101 [58], TS 22.153 [63], TS 22.289 [69], TS 22.468 [70], TS 22.368 [71], TS 22.156 [28], TS 22.137 [6].
NOTE: Terms referring to 5G (e.g. “the 5G system”, “NG RAN”) should be implicitly replaced by the corresponding terms for 6G (e.g. “6G system”, “radio access network of the 6G system”) in those requirements.
Requirements in this clause do not apply to requirements involving the legacy systems/RATs (e.g. E-UTRAN, UTRAN, GERAN). Specific 6G system requirements on inter-system interworking are defined in clause 5.2.
Editor’s note: further exceptions are FFS, e.g. about mobility / interoperability / interworking between 6G and 4G (e.g. voice), or other 5GS legacy requirements (e.g. inherited from 4G, or under ongoing discussion whether to be supported or simplified in 6G).
5.5 Security for 6G
5.5.1 Network security for 6G
5.5.1.1 Description
It is expected that cellular networks become "more secure" with each subsequent generation. Societies are becoming more connected, increasing reliance on mobile networks to provide crucial connectivity for all aspects of daily life. Networks are becoming more complex, with 6G also seeming to be a convergence of disparate technologies (e.g. information, operation, communication), complicating the ecosystem with their differences in approaches, threats, disciplines, and capabilities.
It is incumbent on the mobile communication ecosystem to enhance security and privacy, embracing new security paradigms, techniques, and leveraging evolving security technologies.
Moreover, it is expected that cloud deployments will play an even more important role in 6G as they do today for 5G. Network elements will be deployed in private, public, and hybrid clouds and not all network elements of one operator's network will be deployed in the same cloud or data centre. Network elements of one operator might also be instantiated in another operator's cloud environment.
This requires adapting the security mechanisms, with network elements being able to make trust/security related decisions self-sufficiently based on the owners' policy and without constant enquiries to a central authority in the home or visited network. Furthermore, protection of the network elements from being tampered with or being modified by unauthorized parties is needed.
Furthermore, it is important that trust and security is applied on the appropriate layer, i.e. not limited to the transport layer.
Despite the additional dimension of multi-stakeholder cloud environments, it is important to decrease the administrative and operational burden for establishing security within and between 3GPP networks. As seen today, many operators struggle with the bilateral administration of security to enable interconnection and roaming with other operators.
Therefore, a decentralized, yet common approach for exchanging security keys and authentication/authorization credentials is needed.
In addition, it is imperative that in 6G, considering the network elements being deployed and operated in a cloud environment, a security mechanism needs to be provided to enable a network operator to verify that when a network element is deployed, there are no unintended changes in the network element from what is expected by the network operator. In addition, during the operation of the 6G network element, this mechanism should also allow the network operator to verify that the 6G network element has not been altered in a way that is different from the expectations of the network operator.
5.5.1.2 Potential New Requirements
[PR 5.5.1.2-1] The 6G network shall provide security mechanisms for secure access to elements of the core network of the 6G system and secure communication on all 3GPP defined interfaces of the core network of the 6G system.
[PR 5.5.1.2-2] The 6G network shall support establishment of secure communication between elements of the network while protecting network related information (e.g. network element identities, topology) from disclosure to unauthorized parties.
[PR 5.5.1.2-3] The 6G network shall provide security mechanisms that enable the network operator to ensure there are no unintended changes of the elements of the 6G network.
[PR 5.5.1.2-4] The 6G network shall provide security mechanisms that offer protection of communication and data while at the same time not hindering Lawful Interception (ref TS 33.126 [312], clause 6.4).
[PR 5.5.1.2-5] The 6G network shall support a means for elements of the core network to establish security in a decentralised environment.
NOTE 1: An example of a decentralised environment, as mentioned above, includes the case where not all network elements of one operator's network will be deployed in the same cloud or data centre. Network elements of one operator might also be instantiated in another operator's cloud environment.
NOTE 2: The establishment of security in a decentralised environment may or may not include aspects such as the exchange of security material (e.g. keys) and use of authentication/authorisation mechanisms.
Editor’s Note: The term “decentralised environment” is FFS.
5.5.2 Use case on quantum-resistant security
5.5.2.1 Description
According to some public predications, Cryptographically Relevant Quantum Computer (CRQC) may appear in the 2030s. However, quantum computing technology poses significant threats to some classical cryptography, especially to widely-used cryptographic algorithms (including both symmetric and asymmetric cryptography) that secure much of today's data, rendering electronic secrets vulnerable to discovery. Hence, the migration to quantum-resistant algorithms is assumed to start as soon as possible. Meanwhile, the migration of the widely-used cryptographic algorithms in the 3GPP network to be quantum-resistant is expected by many to be carried out smoothly.
In addition, after the migration, whenever an adopted quantum-resistant algorithm has to be upgraded, removed and/or replaced, and/or a new quantum-resistant algorithm needs to be integrated, the 6G system should be agile enough to finish a quick change. This is of crucial importance for the operators to quickly, effectively and cost-effectively adapt the cryptographic algorithms used in 6G network, so that the 6G system and 3GPP/6G services remain secure to the ever-changing threats from any new and emerging types of quantum computer attacks.
Figure 5.5.2.1-1 lists the general application scenarios of cryptographic algorithms in 5G system.
Figure 5.5.2.1-1: Application scenario of cryptographic algorithms for 5G system
5.5.2.2 Pre-conditions
Alice bought a new cell phone with quantum-resistant capability and started to use the cell phone to connect to mobile networks. The network also turns on the quantum-resistant capability agilely in order to protect users from quantum attacks. The quantum attacks as shown in Figure 5.5.2.2-1 will be blocked by the quantum-resistant approaches.
Figure 5.5.2.2-1: An example of quantum attacks to the UE and the mobile network
5.5.2.3 Service Flows
Assume that an attacker has the CRQC capability, the attacker can crack the traditional cryptographic algorithms based on the CRQC function and launch attacks to the network.
1. Alice turns on the UE. The UE attaches to a base station, registers to the mobile networks, and establishes a PDU session. Alice's UE initiates some services and starts to transfer data between her phone and the network through the mobile network.
2. An attacker is doing some malicious behaviours like eavesdropping and deciphering the traffic using CRQC, blocking and inserting malicious messages, trying to break and forge some NFs, etc.
3. As the UE privacy, communication for different elements (UE\NF) and service authorization are protected by quantum-resistant approaches, the attack initiated by the attacker fails.
5.5.2.4 Post-conditions
Thanks to the quantum-resistant approaches adopted by the UE and network, the interactions between UE and network, between network elements, and between different PLMNs is protected from attackers with CRQC.
5.5.2.5 Existing features partly or fully covering the use case functionality
Quantum threats used to be studied in Release 16 TR 33.841 [40] for 5G systems.
3GPP imported 256-bit Confidentiality and Integrity Algorithms for the Air Interface in TS 35.246 [41], TS 35.247 [42], TS 35.248 [43] during Release 19. These 256-bit algorithms can be used to protect the RRC/NAS/UP traffic of the air interface from quantum attack. But how to use these algorithms (e.g. algorithm negotiation, key derivation) in 3GPP has not been specified yet. And quantum-resistance is not considered in other scenarios like the identity protection, the NDS-IP security, the NF-to-NF authentication and communication protection, the NF service authorization, inter-PLMN protection, etc.
5.5.2.6 Potential New Requirements needed to support the use case
[PR 5.5.2.6-1] The 6G system shall provide security protection for communication (e.g. subscription identifier, authentication methods) against the potential attacks posed by quantum computing.
[PR 5.5.2.6-2] The 6G system shall support the mapping of permanent and temporary identifiers of all subscribers, e.g. for Lawful Interception purposes.
[PR 5.5.2.6-3] The 6G system shall ensure the cryptography agility (i.e. post-quantum cryptography algorithms-related smooth migration, switching, update) for the 6G system and its services to remain secure against new threats.
5.5.3 Use case on UE selecting a base station after assessing its legitimacy
5.5.3.1 Description
The majority of the efforts in the mobile communication system security has been focused on delivering secure communication once connected, whereas security related to bootstrapping those connections are relatively less explored. A UE can interact with a Base Station without assessing its legitimacy, which opens way for diverse attacks. False Base Station (FBS) attacks are one such example and its potential to cause active and passive impacts are an alarming concern, worldwide. Addressing fundamental security issues is important and can entail a system level reappraisal of risk and expectations. This is most feasible to undertake at the beginning of a new mobile generation. Attacks mounted by a FBS are a major security flaw in the existing communication system. There is an important opportunity to address this concern in the next generation wireless systems. The sooner that security threats are addressed in the process of interaction between components of the system, the more risks can be reduced to the 3GPP system.
By increasing the level of security between the radio network entities and the UE during the initial network selection procedure, FBS attacks can be prevented at earlier stages of communication establishment. One such example is the introduction of an efficient Base Station authenticity verification mechanism such that the UE can verify the authenticity of the Base Station prior to starting the 6G registration procedure.
5.5.3.2 Potential New Requirements needed to support the use case
[PR 5.5.3.2-1] Subject to operator policy and regulatory requirements, the 6G system shall support a means for a UE to be able to distinguish a False Base Station from an authentic Base Station.
5.5.4 6G security requirements on trust establishment, security management and digital identity
5.5.4.1 Description
The 6G network will have the competence to adapt to new use cases and new technologies and in tandem be resilient to new attack vectors. The network should be secured and trusted to deliver any services. 6G security should be considered from the following aspects:
Efficient trust establishment and secure communication in inter-PLMNs and intra-PLMNs:
3GPP network security mainly relies on NDS/IP and NDS/AF to provide transport layer security protection mechanisms. The communication network is divided into several security domains, and digital certificates are usually used as identity identifiers and IPSec protocol is used for securing the communication between the domains. This requires both Certificate Authority (CA ) institutions to crossly trust each other. Frequent cross-domain authentication brings about network latency and management cost, as well as threats of single point of failure. What’s more, it is difficult to establish a common root of trust for cross domain communication.
The issue has been identified prominently in the inter-PLMN case, where operators face problems of managing the large scale of CAs in roaming scenarios. Thus, an efficient and decentralized manner of establishing trust in inter-PLMN connection is needed.
For the intra-PLMN case, on one hand, authentication between network elements face similar issues. 6G is expected to provide ubiquitous connectivity and following the network slicing and non-public networks (NPN) technologies provided in 5G, there will be more and more local area networks as extension of the PLMN network, which might be deployed in a distributed manner. For these intra-PLMN networks, authentication of network elements in each sub-network should be efficiently performed. Therefore, a distributed and decentralized manner of establishing trust in intra-PLMN is also needed like the inter-PLMN case, in order to ensure the secure interconnection of intra-PLMN networks. On the other hand, as the number and the type of the intra-PLMN networks is increasing, security isolation of these intra-PLMN networks is crucial to avoid cross-attack risks, fine-grained security isolation based on the security needs of different businesses requirements is needed. According to the needs from different vertical industries, the dedicated network resources should be equipped with customized security capabilities for different intra-PLMN networks, this brings the needs of security capability management to orchestrate and dispatch the built-in security capabilities, and to formulate and issue the security policies. Thus, the 6G system need to provide flexible orchestration of security capabilities to fulfil customized security needs.
Enhanced network security operation and management:
Cybersecurity is the practice of protecting systems, networks, and programs from digital attacks. It is also fit for the network domain of telecommunication network. The NIST CSF [244] outlines five core functions that serve as a navigation system for managing cybersecurity risk: Identify, Protect, Detect, Respond, and Recover. Existing network security mechanisms can only meet two parts: identify and protect, and there are no clear requirements and solutions to cover the rest part, i.e. detect, respond, and recover. So, it needs to consider introducing those three features also in 6G network to enhance network security operation.
Thus, it is necessary to consider providing the abilities: 1) to detect security attacks, security issues and security failure, 2) to analyse them to confirm threats and to make security policies to respond, 3) as well as to implement and deploy such policies to recover normal network and services.
Security as a service for digital identity based on SIM identity and authentication:
Immersive communication is an emerging digital communication method that creates an immersive communication experience for users. The user may have different identities in the virtual world, real world, and physical world. For example, the user may use its digital representatives to transmit audio and video and other multi-modal information to the communication object on the other end according to the intentions of the user. As personal intelligent assistants are expected to rapidly become popular, the user digital representatives need to have its own digital identity associated with the user to ensure their trusted access to the network. An embodied robot may have a SIM-based anchor, but there is a need to ensure the association with a controller/user and for its behaviour control. Thus, it requires a mechanism to identify and establish trust relationships among multiple parties, in order to meet the needs of users to switch and interact in different virtual environments. This requires more flexible and secure identity identification. In addition, in augmented reality (AR) scenarios, how to protect user privacy and security is also a concern.
Operators have leveraged the SIM as a trust root in the past few years to authenticate users and build secure connection for the customers. As the core component of mobile communication networks, the security and flexibility of SIM cards make them as a trust anchor, providing a solid security foundation for different forms of digital identities in both virtual world and real world. Operators can leverage the SIM identity as the root ID of user to associate the following digital identity of the user digital representatives, robots, etc., and enable users to seamlessly access various services and resources of the Internet. Accordingly, the operators can associate the user digital representatives with the digital identity and the final root of ID of the SIM. Furthermore, SIM-based self-sovereign identity for authentication and associated attributes for authorization can be used to provide flexible access control of identity access control as well. Certain 3rd party services require user/UE to meet certain eligibility criteria to consume the requested services (e.g. such criteria can be information related to the user e.g. age, location, biometrics etc[[SUGGESTION_START]].[[SUGGESTION_END]]). In such case, the MNO can act as trust anchor and can provide in addition to the digital identity necessary associated credentials e.g., proof information for the UE/User to support 3rd party service requirements compliance verification.
5.5.4.2 Potential New Requirements
[PR 5.5.4.2-1] The 6G system shall provide efficient mechanisms to support authentication and secure communication in inter-PLMN and intra-PLMN networks.
[PR 5.5.4.2-2] The 6G system shall support fine-grained security isolation based on the security needs of different businesses requirements for intra-PLMN networks.
[PR 5.5.4.2-3] The 6G system shall be able to provide flexible orchestration of security capabilities to ensure customized security requirements from customers.
[PR 5.5.4.2-4] The 6G network shall support the identification of potential security threats to communication and network elements.
[PR 5.5.4.2-5] The 6G network shall support security analysis and security enhancement policy generation to mitigate attacks and/or security issues in operator management system.
[PR 5.5.4.2-6] The 6G network shall support security response to implement enhanced security policy from management system to network elements, in order to recover network from disturbance.
[PR 5.5.4.2-7] The 6G system shall enable operators using SIM as trust anchor to provide identity and authentication service to the 3rd parties.
[PR 5.5.4.2-8] The 6G system shall support the verification for the association between the SIM identity and the digital identity of the different digital representatives.
[PR 5.5.4.2-9] The 6G system shall authorize the required services of the digital identity of the digital representatives of users and revoke the digital identity if needed.
[PR 5.5.4.2-10] The 6G system shall provide applicable subscriber/user related information to the 3rd party services provider as required for the 3rd party services to allow verification of subscriber/user eligibility to consume the services.
Editor’s Note: Further details of the above requirement PR 5.5.4.2-10 is FFS.
5.5.5 Use case on data exposure service
5.5.5.1 Description
The vast amount of data available with operators can be monetized if the data could be desensitized and exposed. The desensitization of data involves segregation of data without identities, removal of network configuration and performance related data. This anonymized data would create lot of avenues for monetization for the operator. Additionally, it can provide valuable data to the government agencies for public safety requirements.
One category of anonymized data could be the aggregated information of UEs present in an area or their mobility patterns. This could be shared to authorized third party(s) for advertising planning in crowded areas such as malls or shopping complexes. For effective utilization of such data, orchestration of information from multiple MNOs is recommended.
Similarly in tourist places, the 3rd party application could facilitate the users by providing a display board depicting the crowd density in a section wise manner to plan their visits accordingly.
Exposure of anonymized data can aid Vehicle-to-Everything (V2X) use cases where the information about mobility pattern and traffic conditions for a group of users can be exposed. An authorized 3rd party application may utilize this data to help its V2X applications to,
- create enhanced situational awareness for vehicles by indicating high pedestrian density areas,
- support emergency re-routing during incidents by providing real time crowd congestion aggregated information.
This enables the applications to perform context aware decision making in V2X scenarios.
India hosts Maha Kumbh Mela periodically every 12 years. Kumbh Mela, the world’s largest gathering, draws millions of pilgrims in one city. In 2025, this festival saw a footfall of approximately 660 million pilgrims over its entire duration [205]. From this location if the user information was anonymized and exposed to authorized 3rd party, it could be useful for planning operational staff and security personnel to avoid stampede, trespassing and other causalities. This information could also be utilized by the government for evacuation planning in emergency situations.
“Digital Twin Sangam” is an initiative by the Indian government to integrate and provide a real time virtual representation of a physical location or event. This can be specifically designed to model, simulate and manage large scale gatherings. It integrates data from multiple sources (e.g. network, sensors, video feeds) to mirror conditions on the ground, enabling authorities and service providers to visualize crowd mobility pattern, predict congestion and take proactive decisions for safety.
Anonymized energy consumption data in a particular area or time could be utilized by government agencies or policy makers in shaping policy or incentives to drive energy conservation and sustainability.
The service providing access to such data to authorized third parties could further provide information about the characteristics of the data accessible to the consumer. Such characteristics could include the overall scope of the available data (e.g. geography, time, sources), the possible type of data processing (e.g. data correlation, anonymisation), or other metadata.
5.5.5.2 Existing features partly or fully covering the use case functionality
Table 5.5.5.2-1: Gap analysis for this use case
Specifications and clauses
Examples of Existing
Requirements
Gap Analysis
TR 23.288 [114]
6.5.2 Input Data.
6.2.8.2.3 Data Collection Procedure from UE
NOTE 2: Per collective attribute, the AF may provide several collective attribute sets, if several sets of UEs with similar behaviour are identified. A similar behaviour can be identified to specific ranges if the AF performs data processing (Data Anonymisation, Aggregation or Normalization) based on NWDAF request. UEs falling in the same range per UE attribute can form a collective attribute set.
NOTE 2: If NWDAF requests the same data from multiple UEs, i.e. a determined list of UEs or "any UE" as the Target of Analytics Reporting, the AF can process (e.g. anonymize, aggregate and normalize) the data from multiple UEs according to the Event ID(s) and Event Filter(s) received from NWDAF during step 3a or 3b before notifying the NWDAF on the processed data in step 5a (if the AF is in trusted domain) or step 5b (if the AF is in untrusted domain).
NWDAF can request this information from Data collection AF however there is no framework in 5G system to decide what data to be anonymized to be shared to third party application.
5.5.5.3 Potential New Requirements needed to support the use case
[PR 5.5.5.3-1] Subject to regulation, operator(s) policy and user consent, the 6G network shall support to enable access from authorized third parties to processed data related to UEs served by the network (for example but not limited to number of UEs in a geographical location, their mobility pattern, application usage trends) without exposing UE identities and individual user data including Personally Identifiable Information or sensitive data.
NOTE 1: Processed data refers to the analysis of data to produce new data including among other statistics on the data, correlating data, aggregating data.
NOTE 2: The existing security, privacy policies and procedures for collecting and processing user data (e.g. via anonymisation) will be preserved.
5.5.6 Considerations on privacy
5.5.6.1 Description
User privacy is an integral part of the 3GPP system, with requirements on the protection for the communication content (e.g. content of emails, web pages), as well as any identities and UE location, from being exposed to unauthorised parties. This is considered as a 3GPP built-in feature by default, and solutions have been specified in previous releases that deal with the protection from unauthorised exposure of the communication content, user-related identities and UE location.
The main observations on the term Personal Data are:
1) Personal Data includes information that can directly identify a user, and also any information that can distinguish one user from others.
2) Personal Data includes information that by itself cannot identify or distinguish a user, but when combined with other information, can identify or distinguish a user.
Furthermore, Personal Data in the 3GPP context covers the following types of information related to the user:
- Communication content: data sent or received by the user via the network, e.g. contents of a text message / voice call, data downloaded from a webpage
- Non-communication content: data sent by the UE or derived by the network that is used to provide 3GPP services such as sensing, computing
- User-related information: any information (excluding communication content and non-communication content) either sent by the UE, derived by the network, e.g. user location, UE ID
5.5.6.2 Existing features partly or fully covering the functionality
SA1 studied privacy in Release 6 in TR 22.949 [72]. SA3 also studied privacy in Release 14 in TR 33.849 [73].
Requirements for privacy for 4G are defined in TS 22.278 [74]:
The Evolved Packet System shall provide several appropriate levels of user privacy including communication confidentiality, location privacy, and identity protection.
The privacy of the contents, origin, and destination of a particular communication shall be protected from disclosure to unauthorised parties.
The Evolved Packet System shall be able to hide the identities of users from unauthorised third parties.
It shall be possible to provide no disclosure, at any level of granularity, of location, location-related information, e.g. geographic and routing information, or information from which a user's location can be determined, to unauthorised parties, including another party on a communication.
In addition to the requirements above, requirements for privacy for 5G are defined in TS 22.261 [14]:
The 5G system shall support a secure mechanism to collect system information while ensuring end-user and application privacy (e.g., application-level information is not to be related to an individual user identity or subscriber identity and UE information is not to be related to an individual subscriber identity).
A well-structured consolidated requirement on privacy protection can provide clearer guidance for Stage 2 and Stage 3 development.
5.5.6.3 Potential New Requirements
[PR 5.5.6.3-1] Subject to regulatory requirements or operator policy, the 6G system shall protect from unauthorised access and disclosure to unauthorised entities any Personal Data belonging to a user and subscriber.
Editor's Note: The requirement above is FFS
5.5.7 Use Case on privacy protection of data exposure
5.5.7.1 Description
In the 5G era, proposals have been made to support user data communication tailored to various use cases such as enhanced Mobile Broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive Internet of Things (M-IoT).
Looking ahead to the 6G era, it is anticipated that there will be an increasing demand for data collection beyond just user data communication. Use cases involving artificial intelligence (AI), and network digital twins (NDT) are expected to drive this demand. It is crucial to consider the efficiency of data collection processes, as the volume of data collected can significantly escalate based on the number of data points and the frequency of collection. Such an increase may lead to congestion in internal communications within the network operator. Moreover, collected data can be utilized for internal purposes, and the data shared with third parties should be processed to ensure the protection of user privacy to follow the operator’s policies and regulatory requirements, thereby facilitating collaborative data utilization to enhance business opportunities. In light of these considerations, there is a pressing need for frameworks that enhance the efficiency of data collection while addressing critical aspects of privacy through information disclosure practices, such as the abstraction and anonymization of data within mobile network systems.
As the 6G system is expected to provide more novel services beyond connectivity as well as a plethora of new capabilities, the data collected and exposed is more versatile and could be different in nature, volume, formats, etc. When the data is provided to authorized 3rd parties via 6G core network, the privacy of the user/subscriber identities and UE’s identifiers should be protected.
5.5.7.2 Existing features partly or fully covering the use cases functionality
There are requirements for network capability exposure that are partially related, as indicated in clause 6.10.2 of TS 22.261 [14]. However, the requirement is for security logging information of UEs.
Based on operator policy, the 5G network shall expose a suitable API to provide the security logging information of UEs, for example, the active 3GPP security mechanisms (e.g. data privacy, authentication, integrity protection) to an authorized third-party.
The 5G system shall be able to:
- provide a third-party with secure access to APIs (e.g. triggered by an application that is visible to the 5G system), by authenticating and authorizing both the third-party and the UE using the third-party's service.
Subject to regulatory requirements and based on operator policy, the 5G system shall provide a mechanism to support confidentiality to prevent exposure of data exchanged between the 5G network and a third party service provider. Subject to regulatory requirements and based on operator policy, the 5G system shall provide a mechanism to support data integrity verification service to assure the integrity of the data exchanged between the 5G network and a third-party service provider.
The 5G system shall provide suitable means to allow use of a trusted and authorized third-party provided integrity protection mechanism for data exchanged between an authorized UE served by a private slice and a core network entity in that private slice.
The 5G system shall provide suitable means to allow use of a trusted and authorized third-party provided integrity protection mechanism for data exchanged between an authorized UE served by a non-public network and a core network entity in that non-public network.
5.5.7.3 Potential New Requirements needed to support the use case
[PR 5.5.7.3-1] Subject to operator policy and regulatory requirements, the 6G system shall support privacy protection for any information exposure to a 3rd party.
[PR 5.5.7.3-2] Subject to national or regional regulatory requirement, the 6G system shall provide user privacy protection, location privacy, identity protection for UEs accessing 6G network for services (e.g. communication, sensing, AI inferencing), and for the corresponding information exposure to an authorized 3rd party.
[PR 5.5.7.3-3] The 6G system shall be able to protect UE’s subscriber identities from attacks.
[PR 5.5.7.3-4] Subject to national or regional regulatory requirements, the 6G system shall provide a privacy override mechanism to enable access to retained data for law enforcement purposes and Lawful Interception purposes which may otherwise be subject to the privacy protection requirements [313].
5.5.8 Use case on security control enhancement with NDT in 6G network
5.5.8.1 Description
Currently the cybersecurity landscape is dynamic and increasingly complex due to evolving threats, technological advancements, and geopolitical factors, such as ransomware attacks and phishing and social engineering etc. In addition, 6G networks are characterized by complexity and openness, which introduce significant security challenges. As a result, comprehensive security risk analysis and robust security measures are crucial for protecting customer data privacy and ensuring service continuity.
There are a lot of existing countermeasures to mitigate security risks, including AI/ML-based mechanisms, but it is impossible to perform vulnerability scanning, security test, security risk analysis and applying countermeasures in a living mobile network without side effect, e.g. cause delay of communication or even service interruption.
For example, message tampering is a common threat that can originate from the UE side or occur along the communication path, and happen on different layers, such as 3GPP transport layer or application layer. It can be caused by exploiting misconfiguration (e.g. disable user plane integrity protection) or bidding down attack during policy negotiation, etc. Detecting such tampering typically requires a comprehensive flow analysis across all network functions in the communication path, which demands significant resources like network, CPU and memory, hence impact the existing service. Operator may deploy an NDT to simulate different threat/attack scenarios, analyze the associated behaviour, and identify the root cause, then validate corresponding countermeasure in the NDT before applying to living network.
Table 5.5.8.1-1: Potential sustainability impacts of the use case
(the UN SDGs/GDC matching goals of each aspect within 3GPP context)
Potential benefits of the use case (added value)
Potential areas of attention of the use case (risks to be mitigated)
Environmental sustainability aspects
(UN SDGs 12, 13, 14, 15 and indirectly 6, 7 & 11.
UN GDC “Develop principles for environmental sustainability of digital technologies”)
Energy resources
(UN SDG 7, 11, 12)
• Security tests and applied measures may also be measured in term of their energy consumption, allowing to find the best trade-off and most efficient security solutions at the lowest energy cost.
• The energy consumption of the NDT may be significant to enable the complex representation of the network, its functions, traffic and users, especially for large simulations. (this should be mitigated by the energy optimisation capabilities that NDT could provide e.g. energy savings on the real network.)
Material resources
(UN SDG 11, 12)
• The appropriate usage and design of NDT would prevent the need to instead test security measures in extensive labs setups, avoiding some of the material resources needed for the different labs elements e.g. emulators, RF chambers, …
• The intense deployment and usage of NDT (by adding new usages such as simulations against security vulnerabilities) will increase the need for cloud HW and data cent[[SUGGESTION_START]]r[[SUGGESTION_END]]e capacity, inducing material impact on water (manufacturing and cooling) and key material elements (e.g. copper, rare minerals…)
Inclusion & Equality
(UN SDGs 11, 10, 4, 5 and indirectly 3, 16 & 17)
• Increasing network security can lower the need for users to deploy extra means for their privacy and security, making it more inclusive and equal e.g. the users don’t need to know and afford extra protection like VPN, fake call protection apps, antivirus etc.
Trustworthiness
(UN SDGs 11 and indirectly 3 & 17)
• Increasing security, integrity, privacy of wireless networks.
• Increasing operation resilience and service availability by limiting possible vulnerabilities
• Mobile networks are a strategic infrastructure. Their outages or breaches can lead to major societal and economical impacts.
This use case enable the essential “stress testing” that is key to maintain high and adapted security.
TCO reduction
(UN SDGs 8, 9 and 12)
• Possible savings by simulating and anticipating disruptions (and recovery costs) with proactive/preventive actions against security vulnerabilities
5.5.8.2 Pre-conditions
The operator's mobile network is mirrored in the NDT environment and continuously synchronized to the NDT environment.
NDT environment can collect security events and logs from the mobile network.
5.5.8.3 Service Flows
A new Coordinated Vulnerability Disclosure (CVD) was published by authoritative organization, such as GSM Association (GSMA), related to telecom networks.
Operator launches a security test or simulation exploiting the CVD in its NDT environment.
NDT environment generates mitigation plan and corresponding countermeasure based on data collected from living network and knowledge established from previous real or simulated attacks or security test.
Operator validates the countermeasure in NDT environment.
Operator applies the countermeasure in its living mobile network.
5.5.8.4 Post-conditions
The 6G network is capable of proactively detecting and mitigating security threats, ultimately safeguarding customer data and ensuring reliable network operation.
5.5.8.5 Existing features partly or fully covering the use case functionality
There are also some AI/ML-based cyberattack protection methods, such as the ETSI SAI [75] and [76], which has study items focused on AI threat analysis and AI security mitigation strategies. These methods either use AI/ML to enhance security or exploit them to cause harm or malfunction.
In TR 28.915 [77], no security related use case or requirement has been identified. With such NDT, security-related operations (such as vulnerability scanning, security/penetration test, the application of countermeasures, etc.) could be conducted in the virtual replica before making any changes to the living mobile network, also considering real time factors.
5.5.8.6 Potential New Requirements needed to support the use case
[PR 5.5.8.6-1] Subject to operator’s policy, the 6G network should be able to support a Network Digital Twin for network and service management.
Editor’s Note: Further requirements on Network Digital Twin, including on security aspects, are FFS.
5.5.9 Use case on digital identity management for digital asset container
5.5.9.1 Description
This use case focuses on the digital asset container's functionality related to digital identity and proposes to expand the use cases related to the linkage with the 3GPP system.
The widespread use of digital identities is currently expected around the world. A digital identity is a representation of a user's credentials and attributes in a digital format. Specifically, digital identities can be issued by public institutions, such as driver's licenses and employment certificates, or by educational institutions, such as student ID cards and diplomas. These formats and issuance/presentation protocols are being standardized by standards bodies such as W3C and IETF, and digital identity wallets are being developed to store these digital identities.
As depicted in Figure 5.5.9.1-1, the technological impact of the spread of digital identity means that data formats related to users, which were previously defined in a closed manner for each industry, will be standardized, and systems can work together across sectors. In other words, user information issued by systems in different industries can be verified and used by communication systems, and vice versa.
Figure 5.5.9.1-1: Use of digital identity
The 3GPP defines a digital asset container component for the metaverse use case, which is defined as a function to manage virtual currencies, NFTs, and digital identities. However, digital identities are not limited to the metaverse use case but can be used in many other use cases.
5.5.9.2 Pre-conditions
Figure 5.5.9.2-1: Functions to manage digital identity
Figure 5.5.9.2-1 shows functionalities in this use case to manage user digital identities.
Digital asset container (DAC) interfaces with the network functions of the 3GPP system and has functions to manage digital identity. DAC is an application hosted in Service Hosting Environment, which means it is under operator control.
Third party Digital Identity System (TPDIS) interfaces with the digital asset container and has functions to manage digital identity. TPDIS is one type of third-party applications.
John has a UE with a subscription to MNO#A.
5.5.9.3 Service Flows
The following description illustrates a service flow for QoS control based on digital identities. While the example focuses on QoS control, the same procedure is also applicable to other network functions, such as charging control.
John's UE registered to the network, and internet connection gets available.
John has a contract for subscription-based streaming services, which are certified in the form of digital certificates, and enjoys subscription-based streaming services. The certificates are issued by subscription-based streaming services.
John wants to be provided with communication quality (delay, bandwidth, etc.) suitable for subscription-based streaming services, and keeps the certificates issued by subscription-based streaming services in a DAC managed by MNO#A.
Based on his consent, MNO#A uses the certificates in the DAC for service authorization combined with the subscription information stored in the network and authorizes changes the communication QoS for the subscription-based streaming services.
5.5.9.4 Post-conditions
John could get suitable communication quality for subscription-based streaming services based on the verification of the certificates by MNO#A.
5.5.9.5 Existing features partly or fully covering the use case functionality
General requirements for digital asset management are defined in TS 22.156 [28].
User identity features are defined in TS 22.101 [58].
5.5.9.6 Potential New Requirements needed to support the use case
[PR 5.5.9.6-1] Subject to operator policy and user consent, the 6G system shall support means to provide users with differentiation of QoS and charging based on users’ digital identity information issued by a third party and users’ subscription information.
5.5.10 Roaming Services
5.5.10.1 Description
Since the inception of mobile networks that supported roaming, the VPLMN has always been able to obtain the inbound roamer’s UE subscriber identity (e.g. IMSI or SUPI) and only then provide services to the UE. The UE subscriber identity (unconcealed or concealed) was provided by the UE directly to the VPLMN, which validated it with the HPLMN and provided service to the roamed in user. The capability of the VPLMN to obtain the inbound roamer’s UE subscriber identity and providing communication services to the UE only once the UE’s subscriber identity has been obtained, has been supported in 2G through 5G.
Although these capabilities were supported in those generations, no corresponding Stage 1 requirement was ever written down in any Stage 1 text. To ensure that regulatory requirements can be met, a requirement needs to be added to the Stage 1 requirements for this capability.
5.5.10.2 Potential New Requirements
[PR 5.5.10.2-1] The 6G system shall have a means to ensure that the visited network provides services to an inbound roamer only once the subscriber identity (e.g. SUPI, IMSI) is obtained from the home network.
5.6 Resilience
5.6.1 Zero-outage network
5.6.1.1 Description
Based on incomplete statistics, the global telecommunications sector has experienced numerous significant incidents over the last four years which led to extended internet outages affecting a vast number of users and causing disruptions in industry applications such as banking and government services, resulting in substantial economic losses. The root cause of network accidents may include several aspects:
-System Unavailability: including the transmission connection loss or Network outage.
-Network Attacks: external attacks that compromise the integrity and availability of the system, such as DDoS attacks.
-Force Majeure Events: including unexpected events such as typhoons, earthquakes, which are beyond human control and can cause significant damage.
-Network Operational Errors: including the incorrect network behaviors during network operation, maintenance, configuration modification or upgrade commissioning.
Restoration solutions for 5G Core (5GC) entities have been continuously studied and standardized since Rel-15. TS 23.527 [78] which provides a set of generic restoration solutions for Service-Based entities. Additionally, CT4 has studied some specific topics, such as CANARY testing, aimed at preventing unexpected traffic disruptions within the network during the introduction of new software releases. As well as the IMS disaster prevention study in TR 29.866 [79]. But still some aspects can be enhanced in 6G. E.g. lack of systematic failure perception, assessment, and the timely supervision and corresponding systematic control capability (e.g. failure isolation), can cause failures to spread and escalate into widespread system failures. And the existing restoration solutions for 5GC entities are unable to fulfil the new scenario, for instance:
- Dedicated User Plane Functions (UPF(s)) are extensively deployed for vertical users, as user plane data is sensitive and these vertical users prefer to keep these user plane data in local site. However, there is a lack of proposals addressing how to maintain the edge network’s inertial operations in the event of transmission failures between the core and the edge network.
6G will introduce some new services and capabilities, such as artificial intelligence, digital twins, and sensing, which present new challenges to existing restoration solutions. New use cases/scenarios lead to new requirements regarding network reliability to protect traditional communication service from the new service impact.
- multidimensional data will be stored in the data network, including various types such as sensing and AI modelling data, which requires high level data reliability and robust security measures for cross-domain data access.
- (AI may be embedded in some of the network elements/functions and it may participate/assist in service logic computation and provide networking policies, could introduce new challenges that might be unpredictable to the telecommunication network.
Based on the analysis of commercial network accidents, it is found that network failures, network modification operations, and network overload are the main causes. Due to the lack of system management and control capabilities and the untimely manual supervision, faults cannot be controlled and repaired in a timely manner, leading to the spread of faults and their escalation into system failures. In 6G era, the new services and capabilities will lead to new requirements for restoration, the 6G network needs to provide zero-outage service (e.g. self-healing service) to fulfil different service reliability requirement.
5.6.1.2 Potential New Requirements
[PR 5.6.1.2-1] Subject to regulatory requirements and operator’s policy, 6G network should support suitable means to detect, isolate and recover from network failure.
[PR 5.6.1.2-2] Subject to regulatory requirements and operator’s policy, 6G network should support suitable means to ensure the stability of services provided by the edge network in case of abnormal transmission occurring between the public network and the edge network.
5.6.2 Use case on fast network provisioning to improve resilience
5.6.2.1 Description
Network resilience is a critical concern for operators. They need to ensure that requested network services are always available. This involves two key scenarios: first, ensuring that requested services can be made available upon request in zero downtime; and second, maintaining the uninterrupted availability of essential network services, such as data connectivity service and voice service.
In the event of a network failure caused by unexpected incidents such as natural disasters or cyber-attacks, restoring network services in the affected area becomes a top priority. This use case considers fast network provisioning to restore services as an operator’s 6G capability.
5.6.2.2 Pre-conditions
Community X owned by landlord Xu is within the coverage of Operator A's PLMN and enjoy reliable communication services.
A severe fire accident happens in the area where Community X is located, destroying numerous utilities, including Operator A's engine room. As a result, Operator A's PLMN is partially compromised and unable to provide network services in the impacted area (i.e. Community X).
5.6.2.3 Service Flows
Landlord Xu requests that essential network services (e.g. data connectivity service and voice service) need to be recovered immediately in the impacted area.
Operator A acknowledges the request and provisions the essential network services in the impacted area. This network provisioning can be achieved by newly deployed physical Radio Access Network (RAN) and Edge Cloud resources in the close proximity of the impacted area, as well as utilizing communication equipment that has not been destroyed by the fire. The network function modules are automatically created to provide the essential network services for the impacted area using AI technologies. To expedite and simplify this network provisioning, only the necessary network function modules for providing essential network services are used based on the service level agreements (SLAs)with the landlord.
Li is a subscriber of the PLMN of Operator A before the network failure. Li’s phone (UE 1) connects to the network in the impacted area. Li can use social media and make calls to inform and update friends and families.
Li works for Company B, whose network services are provided by Operator A. Li can access the PLMN network to remotely access the enterprise server of Company B (located outside the impacted area), with traffic routed from the network in the impacted area to the PLMN.
5.6.2.4 Post-conditions
Users in the impacted area can continue to use the essential services during the PLMN network failure; will be switched back to the PLMN network services after the PLMN network is recovered.
5.6.2.5 Existing features partly or fully covering the use case functionality
Isolated E-UTRAN Operation for Public Safety (IOPS) is supported in EPS [358]. However,
- IOPS is only applicable to public safety UEs,
- IOPS only works for the no backhaul (to Macro EPC) scenario.
There are existing features, such as Network Slicing, Edge Computing, and Non-public Network, allowing to deploy network services in a local area to cater for specific requirements. These are good starting points to provision dedicated services to support local area service-level agreements, with which there are challenges on the following aspects:
- agile service delivery: a flexible and agile enough way to meet the various service requirements in short time,
- network and data autonomy: especially for sensitive services with high-level security needs.
5.6.2.6 Potential New Requirements needed to support the use case
[PR 5.6.2.6-1] Subject to operator policies and service level agreements, the 6G system shall enable operators to provision network services as part of the operator’s PLMN network on-demand, e.g. in response to an urgent event (e.g. disaster, emergency and DDoS events), with certain level of local control and specific functionalities in a given area during a specific time period.
NOTE 1: The level of local control can be based on operator policies and agreements with 3rd party. For example, the authorization and policy control of users to access the provisioned services are not affected by the failure of the operator’s PLMN network.
NOTE 2: The enabled functionalities can be based on operator policies and agreements with 3rd party. For example, data connectivity service and voice service are prioritized when an urgent event happens in a residential community; small data transfer service is prioritized when an urgent event happens in an IoT based farmland.
NOTE 3: Some situations can target the required network services to be provisioned within hours to serve certain users whose QoE is impacted by an urgent event.
NOTE 4: Local control refers to the capability of part of the operator’s PLMN network to operate autonomously and independently, e.g. management of local subscription, local traffic, without interaction with the operator’s PLMN.
[PR 5.6.2.6-2] Subject to operator policies and service level agreements, the 6G system shall enable a network operator to authorize a UE, that is subscribed to local network services, to access services from the PLMN of the same operator.
NOTE 5: This applies to scenarios where a service is not available in the local network services that have been provisioned on-demand but is available from the PLMN of the same operator.
5.6.3 Use case on resiliency for 6G
5.6.3.1 Description
ITU-R M.2160 [27] emphasizes resilience as a critical aspect of 6G networks. Resilience, in the context, refers to the network’s ability to maintain reliable and continuous service despite various disruptions, such as physical damage, cyber-attacks, and natural disasters.
The 6G Roadmap [206] further underscores the importance of resilience in 6G networks by outlining a vision where 6G must achieve trust, security, and resilience to be fully accepted by stakeholders. In the extended reference [207], resilience involves the network's ability to meet diverse service objectives and to anticipate, identify, detect, and effectively respond to network status changes such as disturbances, errors, faults, and threats, ensuring continuous operation and availability under all circumstances.
In recent technology trends, “Cloud Native” [208] refers to a way of designing and operating applications, network capabilities, and services in an open and flexible environment. In this context, cloud native infrastructure and applications are decoupled from the lifecycles of vertical monoliths, which enables them to evolve separately. 6G capabilities can be designed as a collection of workloads on top of the cloud native infrastructure and then the smart workload placement for 6G capabilities is matched to the infrastructure's capabilities and capacity, allowing deployment where it is truly needed.
In order to effectively respond to network status changes, key prioritized areas for achieving resilience include eliminating single points of failure through adequate redundancy, reconfiguring network paths and network capabilities from RAN to core. The references highlight the significance of resilience in addressing diverse service requirements and mitigating risks associated with disruptions and attacks.
Self-healing: Autonomous detection and recovery from faults without human intervention.
Adaptability: Dynamically adjusting to changing conditions, such as varying traffic and computational loads and environmental factors (e.g. physical damage, cyber-attacks, and natural disasters, power outage, etc.).
5.6.3.2 Precondition
A major fibre cut caused by ongoing construction activity has disrupted connectivity in a section of the telecom network, affecting services in a metropolitan area. The 6G network is deployed using cloud-native architecture with distributed and redundant components.
AI-based monitoring and orchestration systems are active and authorized to autonomously manage network resources and recovery actions.
5.6.3.3 Service Flows
The AI-based monitoring system detects anomalies in the network, such as node failures, latency spikes, service failure/down or security breaches.
The resiliency management function within the 6G system identifies the affected network segments and classifies the type and severity of the disruption.
Based on predefined policies and real time analytics, the system initiates autonomous recovery actions, including:
Activating redundant network functions in unaffected zones.
Rerouting traffic through alternate, healthy communication paths.
Isolating compromised or non-functional components.
The cloud-native orchestration layer dynamically reallocates workloads and services to maintain service continuity and performance.
The system continuously monitors the effectiveness of the recovery actions and adjusts configurations as needed to optimize performance.
The network continues to operate with minimal or no service disruption, ensuring uninterrupted access to critical services.
Repeat steps 1 through 6 to maintain resilience throughout the duration of the disruption or until normal operations are restored.
5.6.3.4 Post-conditions
The 6G network maintains continuous operation and service availability despite the disruption.
The network has autonomously adapted to the disruption through intelligent reconfiguration and recovery mechanisms.
5.6.3.5 Existing features partly or fully covering the use case functionality
Not applicable.
5.6.3.6 Potential New Requirements needed to support the use case
[PR 5.6.3.6-1] Subject to operator’s policy, the 6G network (OAM) should support autonomous detection and recovery of failed service with minimum human intervention, to ensure continuous operation.
5.6.4 Use case on disaster risk-based network resilience
5.6.4.1 Description
Handling disaster situations in mobile networks is a critical aspect of ensuring robust and resilient communication systems. 3GPP standards incorporate mechanisms for rapid network recovery and continuity during emergency situations, such as natural disasters or large-scale outages. These mechanisms include the implementation of emergency communication procedures, prioritization of critical services, and the deployment of mobile base stations and temporary network infrastructure to restore connectivity swiftly. Additionally, the interoperability and coordination among different network operators and emergency services can facilitate seamless communication and efficient disaster response. By adhering to these standards, mobile networks can maintain essential communication services, support rescue operations, and provide timely information to affected populations, thereby enhancing overall disaster resilience and response capabilities.
Some operators have developed risk scores that have been integrated into existing mobility planning tools. Operators use “risk scores” in their site selection process. The risk score includes for example drought, wildfire, inland flooding, coastal flooding, and wind data.
The occurrence of certain disaster conditions such as tornadoes, wildfires or floods can be predicted by the advanced weather forecasting etc. or generally be known in advance in a given geographical area. In today’s networks, emergency procedures exist to preconfigure some list of networks available in case of disaster (e.g. based on Minimization of Service Interruption (MINT) [14] and to notify UEs about upcoming or ongoing disaster (e.g. Public Warning System (PWS) / Earthquake and Tsunami Warning System (ETWS) [62] messaging).
However, various elements/sites of the network may be sensitive/exposed to different risks e.g. depending on their physical location (e.g. roof-mounted base stations may be immune to floods, indoor base stations immune to tornadoes etc[[SUGGESTION_START]].[[SUGGESTION_END]]). In case of an (upcoming) occurrence of a specific disaster, it can be anticipated upfront that part of the network may be affected in an area, while other parts of the network would not.
Potential sustainability impacts of the use case is shown in the following table:
Table 5.6.4.1-1: Potential sustainability impacts
(the UN SDGs/GDC matching goals of each aspect within 3GPP context)
Potential benefits of the use case (added value)
Potential areas of attention of the use case (risks to be mitigated)
Environmental sustainability aspects
(UN SDGs 12, 13, 14, 15 and indirectly 6, 7 & 11.
UN GDC “Develop principles for environmental sustainability of digital technologies”)
Energy resources
(UN SDG 7, 11, 12)
Optimization of network resources based on the type of resources, with increased energy efficiency
Socio-economic sustainability aspects
(UN SDGs 2, 3, 4, 5, 8, 9, 10, 11, 16 & 17 and indirectly 12.
UN GDC “Closing Digital Divides and Accelerating SDG Progress”
&
“Expanding Digital Economy Inclusion”
&
“Fostering an Inclusive, Safe Digital Space”)
Health
(UN SDG 3)
Increased availability of the network to provide service to populations affected by the disaster
Inclusion & Equality
(UN SDGs 11, 10, 4, 5 and indirectly 3, 16 & 17)
Increased availability of the network to provide service to populations affected by the disaster
Trustworthiness
(UN SDGs 11 and indirectly 3 & 17)
Increase availability & resilience of the system based on the specific impact on the network depending on the type of disaster
Infrastructure
(UN SDG 9)
Increased availability & resilience of the network to provide service to infrastructures affected by the disaster
TCO reduction
(UN SDGs 8, 9 and 12)
Reduced service downtime and unwanted traffic on impacted network nodes reducing in further maintenance or costs to handle the disaster in the network
5.6.4.2 Pre-conditions
The network (e.g. a 6G base station) is subscribed to obtain information about a disaster event or disaster prediction from an external source such as advanced weather forecasting service or public safety service.
5.6.4.3 Service Flows
The network gets information about a possible disaster in an area in the near future (e.g. 1 day in advance) and starts to broadcast announcement to UEs in that area, which indicates the likelihood of the disaster situation in advance (e.g. in day(s)) with a prediction of the impact to (some of) the network/cells based on a specific type of disaster.
The UE stores the information and performs internal mapping of the given disaster type with some action it may perform if the disaster happens. This can be based on predefined rules provided by the operator (e.g. indicating moving to some nearby cells which should not be impacted by the disaster).
As the probability of occurring the disaster increases over time, the network broadcasts an indication of the imminent disaster situation with the anticipated risk-based network assistance information such as specific cells-at-risk or preferred (i.e. likely not impacted) cells, disaster risk score/level, anticipated time of the disaster, pre-configured UE actions, etc.
For certain types of disasters where the serving network of a UE is vulnerable to those disaster types but the disaster area is served by other (at least partially available) network(s), assistance/configuration information related to other network(s) is provided.
The UE determines whether and what UE initiated action (e.g. conditional handover, cell reselection) it needs to perform based on this indication and the previously received information provided by the network. UE may decide to perform no action if it considers it as the best option. For example, if the UE is currently in a video-streaming live session to broadcast the impact of the disaster, it may anticipate a handover to a target cell of another network which will not be impacted in order not to affect the streaming (which would otherwise take several tens of seconds to reconnect to the remaining available network after its serving network went down).
NOTE: It could be possible that both information is sent at the same time (e.g. in case of short notice).
5.6.4.4 Post-conditions
The network can increase its stability by properly managing the unavailability of part of its network depending on the type of disaster, thus instructing UEs in advance on how to handle a specific situation.
UEs can better keep connectivity and service continuity even in case of disaster by anticipating the actions to remain connected to the parts of the network still available during the disaster.
5.6.4.5 Existing features partly or fully covering the use case functionality
Disaster roaming and MINT [14] techniques allow cells to provide/broadcast a configuration for disaster roaming. The UE is then configured with the list of PLMN(s) to be used in the event of a disaster. In addition, Unified Access Control (UAC) for disaster roaming UEs is also defined: a network should be able to bar UEs doing disaster roaming more aggressively than non-disaster roaming UEs. Specifically, TS 22.261 [14] clause 6.31 defines the following requirements:
6.31.2.2 Disaster Condition
The 3GPP system shall enable UEs to obtain information that a Disaster Condition applies to a particular PLMN or PLMNs.
NOTE: If a UE has no coverage of its HPLMN, then obtains information that a Disaster Condition applies to the UE's HPLMN, the UE can register with a PLMN offering Disaster Roaming service.
The 3GPP system shall support means for a PLMN operator to be aware of the area where Disaster Condition applies.
The 3GPP system shall be able to support provision of service to Disaster Inbound Roamer only within the specific region where Disaster Condition applies.
The 3GPP system shall be able to provide efficient means for a network to inform Disaster Inbound roamers that a Disaster Condition is no longer applicable.
Subject to regulatory requirements or operator’s policy, the 3GPP system shall support a PLMN operator to be made aware of the failure or recovery of other PLMN(s) in the same country when the Disaster Condition is applies, or when the Disaster Condition is not applicable.
6.31.2.3 Disaster Roaming
The 3GPP system shall be able to provide means to enable a UE to access PLMNs in a forbidden PLMN list if a Disaster condition applies and no other PLMN is available except for PLMNs in the forbidden PLMN list.
The 3GPP system shall provide means to enable that a Disaster Condition applies to UEs of a specific PLMN.
The 3GPP system shall be able to provide a resource efficient means for a PLMN to indicate to potential Disaster Inbound Roamers whether they can access the PLMN or not.
Disaster Inbound Roamers shall perform network reselection when a Disaster Condition has ended.
The 3GPP system shall minimize congestion caused by Disaster Roaming.
The 5G system and EPS shall support a mechanism for the HPLMN to control whether a UE, with HPLMN subscription, should apply Disaster Roaming when a Disaster Condition arises (in the HPLMN or a VPLMN).
3GPP system shall be able to collect charging information for a Disaster Inbound Roamer with information about the applied disaster condition.
In addition, TS 22.261 [14] has defined requirements for network sharing-based disaster condition handling (also known as “disaster sharing”) via the following requirements in clause 6.21:
Subject to regulatory requirements or operator policy, the 5G network shall support a PLMN operator to be made aware of the failure or recovery of NG-RAN and/or core network in other PLMN(s) in the same area when the Disaster Condition applies, or when the Disaster Condition is not applicable anymore.
Subject to regulatory requirements or operator policy, the 5G network shall support a PLMN operator to be made aware of the availability of other PLMN(s) as Hosting NG-RAN Operator(s) via Indirect Network Sharing in the same area when the Disaster Condition applies.
Subject to the agreement between the hosting and participating operator, the 5G system shall support a means to enable a UE of the Participating NG-RAN Operator to:
- access their subscribed PLMN services when accessing a Shared NG-RAN, and/or,
- obtain its subscribed services, including Hosted Services, of participating operator via a Shared NG-RAN.
NOTE 4: The above requirement is applicable to Disaster Condition via a Shared NG-RAN.
Based on operator policy, the 5G network shall minimize network congestion caused by Indirect Network Sharing when a Disaster Condition applies.
NOTE 5: Population density in the different areas where Disaster Condition apply needs to be considered.
The 5G network shall be able to enable Indirect Network Sharing only when the Disaster Condition applies in a specific area and disable it when no longer applicable.
NOTE 7: It is assumed operators can have sharing agreement for Disaster Condition in the area.
NOTE 8: It is assumed that during a Disaster Condition, previous network communication is temporarily disabled.
The 5G network shall be able to provide a means for a UE to return to the PLMN used prior to Indirect Network Sharing, when a Disaster Condition is no longer applicable.
The 5G network shall be able to collect charging information for a UE accessing a Shared NG-RAN using Indirect Network Sharing in Disaster Condition.
Furthermore, PWS [62] messages are designed to deliver emergency alerts to users in a specific geographic area. PWS includes systems like ETWS and Commercial Mobile Alert System (CMAS). PWS [62] messages contain essential information about the emergency, such as the type of event, the affected area, instructions for safety, and any other relevant details. The messages are typically short and concise to ensure quick delivery and easy comprehension. They may include text, symbols, or other formats depending on the capabilities of the network and devices. PWS [62] messages are broadcasted using the Cell Broadcast Service (CBS), which allows messages to be sent to all devices within a specific geographic area simultaneously. This ensures that the message reaches a large number of users quickly.
ETWS is specifically designed for rapid dissemination of warnings related to earthquakes and tsunamis. CMAS, also known as Wireless Emergency Alerts in the United States, is a broader system that covers various types of emergency alerts, including natural disasters, severe weather, AMBER alerts, and other public safety messages.
While PWS [62] addresses users, MINT [14] addresses UEs. However, MINT [14] does not provide the granularity needed to identify the parts of the network(s) (e.g. cells) which can or cannot be affected depending on the type of disaster.
5.6.4.6 Potential New Requirements needed to support the use case
[PR 5.6.4.6-1] Subject to operator policy and regulatory requirements, the 6G system shall be able to minimize service interruption or degradation during disasters taking into account the affected/not affected parts of a single or multiple network(s) serving an area impacted by a specific type of disaster.
NOTE: In case of multiple networks serving the disaster area, it is assumed that under disaster conditions, the remaining available parts of the networks in that area can serve the UEs of other networks.
5.6.5 Use Case on prevention of signalling storm
5.6.5.1 Description
In 5G, when a network element fails, explicit restoration signalling (e.g. context data, subscription data, policy data) is triggered to its backup element to maintain UE registration status or service sessions. A data-cent[[SUGGESTION_START]]r[[SUGGESTION_END]]e-level malfunction can generate signalling traffic peaks tens of times higher than normal, potentially causing network congestion or even cascading failures.
For example, a data centre malfunction in a commercial 5GC network resulted in an AMF failure. RAN and other functional network elements detected this failure and simultaneously attempted to restore all affected UEs and sessions. This resulted in a 20-fold surge in registration requests and a 69-fold increase in PDU session establishment requests to the backup AMF—far exceeding normal operational levels, as shown in Figure 5.6.5.1-1 below.
To minimize such signalling, the failure of the network and its backup’s takeover should be transparent to UE, RAN, and other functional network elements, such that no restoration signalling is needed.
Figure 5.6.5.1-1: The number of registration requests and PDU session establishment requests the backup AMF received during a data-centre level malfunction
5.6.5.2 Pre-conditions
The 6GC is deployed in Data Centre A and Data Centre B.
5.6.5.3 Service Flows
1. Data Centre A malfunctions due to unexpected incidents.
2. Data Centre B seamlessly takes over Data Centre A. Messages sent to Data Centre A are automatically routed to Data Centre B. From the perspective of UE, RAN, and other functional network elements, network elements in Data Centre A remain functional. No signalling storm of restoration messages generates.
5.6.5.4 Post-conditions
Messages sent to Data Centre A will be switched back to Data Centre A after Data Centre A is recovered.
5.6.5.5 Existing features partly or fully covering the use case functionality
5G supports restoration procedures, which handle network element failures via its backup, as specified in TS 23.527 [78]. However, these procedures require network element re-selection and session re-establishment (e.g. alternative NF re-selection, PFCP sessions re-establishment.)
Context transfer procedures specified in clause 5.21.4 of TS 23.501 [140] and clause 4.26 of TS 23.502 [30] allow a backup network element to take over the service from the failed network element.
5.6.5.6 Potential New Requirements needed to support the use case
[PR 5.6.5.6-1] The 6G network shall provide a mechanism to minimize the signalling arising from recovery of network failure.=NOTE: Massive signalling can happen e.g. when a network element fails and registrations or data sessions are re-routed to a backup network element.
5.7 6G enhancements of legacy/existing services and capabilities
5.7.1 Fixed wireless access (FWA)
5.7.1.1 Description
Fixed Wireless Access (FWA) [359] in 5G NR has transformed the Home Internet business and has become a significant revenue generator for MNOs. It is expected that FWA services will continue to grow in 6G. While FWA has been supported since LTE, 5G's speed and flexibility has enabled it to emerge as a transformative and successful 5G service. FWA has become a key strategy in expanding high-speed broadband services to previously unserved or underserved areas (i.e. a tool to bridge the Digital Divide), for in-building services, and enterprise (i.e. private network) deployments.
Some initial deployments of FWA used "fallow capacity" in markets where eMBB usage was not in high demand, but the underlying network protocols, procedures, and resource management schemes of the macro networks are designed for eMBB services and Smartphone devices that require mobility support. FWA devices have a very different mobility profile, traffic usage pattern, and are able to take advantage of higher output power improving their in-building link performance.
FWA should provide a quantitative improvement in service by using 6G services and capabilities (e.g. AI/ML).
5.7.1.2 Potential New Requirements
[PR 5.7.1.2-1] The 6G system shall provide optimized network capabilities for FWA (e.g. support stationary devices).
[PR 5.7.1.2-2] The 6G system shall support FWA in relevant bands taking into consideration the regulatory requirements for each specific band.
[PR 5.7.1.2-3] The 6G system shall enable the means to provide awareness of user service characteristics (e.g. data rate, latency) to support the RAN and CN in making real time resource allocation for FWA.
[PR 5.7.1.2-4] The 6G system shall provide FWA a comparable level of security and privacy protection to other 6G services.
[PR 5.7.1.2-5] The 6G system shall support efficient FWA connectivity.
Editor's Note: this requirement is FFS.
NOTE: For FWA, an FWA Customer Premises Equipment (CPE) is used to connect to the network, like any other UE, using a 3GPP access.
5.7.2 IMS multimedia telephony service
5.7.2.1 Description
The following is introduced in clause 5.4.2 of the present document:
The 6G system shall be able to support the following services:
- IMS Multimedia Telephony Service, ref TS 22.261 [14], TS 22.173 [59],
IMS was developed over 2 decades ago as a mechanism, to provide multimedia at a time when cellular networks were primarily being deployed for circuit switched voice and text services. It has been immensely useful in providing network-based voice over IP and messaging services, but much of its powerful flexibility designed to support robust multimedia has not been widely implemented or needed because the core network for cellular evolved to handle such applications natively with the exception of those service bound to legacy telco interfaces (e.g. VoIP for emergency services, lawful intercept, Wi-Fi calling, Rich Communication Services (RCS) messaging).
IMS requirements have been supported since 3G and TS 22.228 [138], specifically does not standardize usage of IP multimedia applications, instead it identifies the requirements to enable their support. GSMA developed VoLTE [357] to provide a minimum mandatory set of functionalities for interoperable IMS-based voice and SMS over LTE. The overly flexible design of IMS has resulted in a call set up that requires multiple round-trip exchanges of SIP messages between UEs and network entities/functions. This complexity can cause quality issues such as call drops because of a fast-changing RF channel. While IMS has added to the complexity of the network, there are possibilities for simplification (e.g. signalling flows) and modernization (e.g. AI assistance) that would be of benefit to operators and increase resiliency for IMS-supported services.
3GPP has introduced new network capabilities (e.g. Satellite, Sensing, Service Based Architecture, AI/ML), new types of services (e.g. Metaverse, Digital Twins) and new types of devices (e.g. eXtended Reality (XR) devices, Intelligent Connected Vehicles, devices with AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent etc.), which may not only bring promising improvements to IMS Multimedia Telephony Service [14], [59] but also require some additional enhancements. Currently, there is little or limited exposure of IMS capabilities. In the future, mobile network operators may be interested in offering application programming interface (API)-related services of their IMS capabilities in a coherent and coordinated manner between the IMS and the core network of 6G.
The capabilities to detect and prevent Caller Identification spoofing in the network were completed as an “add on” to the existing IMS, and while they provide some subscriber protection, the capabilities perhaps could be enhanced in 6G to ensure voice-centric and satellite access UEs or devices with an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent are adequately addressed. The expansion of multimedia service will increase the number and type of threat actors engaging in Caller ID spoofing. Additionally, existing mechanisms need to be enhanced to use modern encryption/digital signing techniques to be quantum safe. Lastly, capabilities may need enhancements to address third party identifiers (e.g. used in PBX and private networks).
This use case proposes requirements that are not included in clause 5.4.2, in the present document.
5.7.2.2 Potential New Requirements
[PR 5.7.2.2-1] The 6G and IMS systems shall provide improved system capabilities for the Multimedia Telephony Service.
Editor's Note: this requirement is FFS.
5.7.3 Enhancement of short message service (SMS)
5.7.3.1 Description
SMS is one of the most traditional telecom services offered by operators. A growing number of enterprise customers and applications (i.e. Application-to-Point (A2P) SMS senders) are using A2P SMS to send notifications, verification codes, and other messages to end users. In the 6G era, SMS is expected to remain favoured by A2P SMS senders due to its unmatched reliability and universal device compatibility.
The growing number and evolving forms of A2P SMS senders present both opportunities and challenges. On one hand, they create new business prospects for operators and enhance the value of carrier messaging channels. On the other hand, they raise end user concerns—such as identifying potential scams from fraudulent A2P SMS senders and ensuring secure interactions with legitimate A2P services via SMS, etc.
In RCS as specified in GSMA RCC.71 [209], Chatbots are introduced as enterprise users/applications to provide A2P and Point-to-Application messaging services. Within the RCS framework, Chatbots need to be verified by the operator. Users can view the associated enterprise information and verification status of these Chatbots. For verified A2P sender end users can interact with them with greater confidence. For unverified A2P senders, end users can make more informed decisions about whether to reply or engage, while exercising greater caution to avoid potential scams.
However, this verification mechanism is exclusive to RCS Chatbot services and does not apply when enterprise users/applications utilize SMS. Therefore, in the 6G era, traditional SMS is expected to evolve delivering more secure and trusted interactions between end users and A2P SMS senders by introducing similar capabilities.
There is a related feature, in TS 22.173 [59], Connected Name and Number Display. This provides information from the network concerning an incoming call, which "allows the device to display the name and/or telephone number of both current parties on a 2-party call."
5.7.3.2 Potential New Requirements
[PR 5.7.3.2-1] The 6G SMS service shall enable a network operator to verify the identity of the SMS sender.
[PR 5.7.3.2-2] The 6G SMS service shall support a means to provide information concerning operator verified SMS sender information to the recipient of a SMS.
NOTE 1: Operator-verified SMS sender information is used to inform the recipient of an SMS that the identity of the SMS sender is operator-verified and support displaying additional information (e.g. brand name, logo, etc.) of the SMS sender. Human interface aspects are out of scope of this requirement.
NOTE 2: Indication that the identity of the SMS sender is operator-verified, any additional information about the SMS sender and the message itself is assumed to be integrity protected.
NOTE 3: Based on interworking agreements and trust relationships, the requirements above apply also when the SMS recipient is roaming or receives a SMS from a sender served by other operators.
NOTE 4: The requirements above apply to A2P SMS and may apply to Person-to-Person SMS.
5.7.4 Network sharing
5.7.4.1 Description
With the continuous development of 3GPP technology, more and more stringent performance KPIs can be fulfilled. 6G frequencies contribute to 6G network performance, including transmission speed, latency, capacity and coverage. The speed of innovation in network communication technology has opportunity to break the limitations. Network capacity increases can generate commercialization opportunities, accompanied by network intelligence and flexible scheduling of resources.
With the new resources and infrastructure of 6G identified, they need to be efficiently utilized. Network sharing technology is well known as a typical technology in this area that needs to be considered by service providers. Previously, network sharing technology has been incorporated into 3GPP standards as a fundamental feature in various generations of mobile communication technologies.
Considering the time plan for industrialization of the future 6G system, the feasibility of network sharing needs to be mentioned at the beginning of the 6G network design, including:
- Like traditional 5G network sharing technologies, 6G network sharing technologies enable multiple operators to share network resources of 6G RAN.
- Extending network sharing to support on demand scenarios, for example, disaster occurrence, network failure, overloaded situation, etc.
Observations on 6G Network Sharing Motivations:
- The study of 6G network sharing use case can potentially alleviate the pressure on the deployment of future 6G networks by global operators, reduce the difficulty of 6G network planning, deployment, and network operation, and facilitate the rapid promotion of the future 6G communication network.
- At the same time, it can also enhance the utilization of 6G network through strengthening inter-operator collaboration, thickening the value of network capacity and reducing the energy consumption of 6G networks, which is in line with the design concept of low-carbon and environmental protection for future networks.
5.7.4.2 Potential New Requirements
[PR 5.7.4.2-1] The 6G system shall support network sharing, based on 5GS network sharing requirements.
[PR 5.7.4.2-2] The 6G network shall provide a mechanism to support event triggered network sharing (e.g. disaster occurrence, network failure, overloaded situation, resource constraints).
5.7.5 Network slicing
5.7.5.1 Description
3GPP has introduced network slicing as a mandatory feature for 5G system and indeed this remains one of the few features that operators around the globe are trailing and attempting to monetize for differentiated services beyond traditional mobile broadband. While the 5G system and network slicing have had a learning curve, their adoption is accelerating. Therefore, we consider that network slicing will also need to be supported in 6G system. It is therefore assumed network slicing will remain a component of the 3GPP system as we transit from the 5G system to the 6G system. From operators’ point of view, additional flexibility, automation and efficiency are possibly needed in the 6G network slice design and architecture, to deal with the increased dynamics required by verticals and for the new use cases. For example, in the 5G system, a slice creation/termination requires various network function (NF) configurations and optional rules for route selections depending on the UE platforms, resulting in delay and customized efforts to bring up a slice. These issues for deployment and potential solutions for the flexibility refer to enhancements in RAN, Core and UE.
5.7.5.2 Potential New Requirements
[PR 5.7.5.2-1] The 6G system should support potential enhancement of network slicing, e.g.:
- Create slices quickly without much overhead/complexity by leveraging automated operations
- Scale and manage the network slices efficiently
- Improve the mechanism to select and access network slice(s)
Editor’s Note: this requirement is FFS.
5.7.6 Unified Access Control (UAC)
5.7.6.1 Description
The UAC mechanism, specified in [14], enables the operators to control the access to 3GPP network in congestion scenarios by determining which access attempt should be allowed or blocked based on operator's policies, deployment scenarios, subscriber profiles, and available services.
6G will introduce new services and capabilities, such as artificial intelligence, sensing, and computing, which require enhancement of the existing mechanism in [14] to efficiently manage evolving technical demands and service requirements. Therefore, the UAC shall be enhanced to address these new services, enabling network operators to control and manage the network in congestion scenarios.
5.7.6.2 Potential New Requirements
[PR 5.7.6.2-1] The 6G system shall support Unified Access Control requirements as specified in [14].
[PR 5.7.6.2-2] The 6G system shall support suitable access categories to manage the access attempt for the new services (such as sensing, AI application, computing) in congestion scenarios.
5.7.7 Use Case on IMS Media Related Service
5.7.7.1 Description
Multiple media related services have been introduced in 5G. For instance, as defined in TS 23.228 [142], IMS data channel, AR communication, avatar communication etc. On the other hand, IMS network offers traditional media capabilities, e.g. play announcement, DTMF Collection, etc. Those media related services may cause potential conflicts for the following reasons:
1) Media related services have different control points, e.g. play announcement controlled by IMS AS, data channel controlled by DCSF, avatar communication controlled by DC AS.
2) Media related services are handled in different NFs, e.g. play announcement handled by MRF, IMS data channel handled by MF.
3) Media related services have similar triggering time, e.g. play announcement and data channel can be triggered anytime during the session, AR capabilities triggered after the session establishment.
5.7.7.2 Pre-conditions
George, as a subscriber from MNO A, subscribes to IMS data channel service and avatar communication service. IMS data channel service allows George to establish IMS data channel and use data channel application. Avatar communication service allows George to use his own avatar during the video call.
CleanClinic, a healthcare company offering video consultants, subscribes to play announcement service and IMS data channel service from MNO B. Play announcement service allows the CleanClinic to play certain announcements when the patient is waiting for his/her physician. IMS data channel service allows physicians to establish IMS data channel and use data channel application to allow patients to share pictures of symptoms.
5.7.7.3 Service Flows
1. George initiates a call session with audio/video and IMS data channel to CleanClinic. While waiting for his physician, CleanClinic plays some music to George via play announcement service. The IMS data channel service from both MNO A and MNO B, and avatar communication service from MNO A need to be paused during the waiting period.
2. When the physician picks up the call, the voice needs to re-route, and IMS data channel service from both MNO A and MNO B, and avatar communication service from MNO A need to be resumed.
5.7.7.4 Post-conditions
George hangs up the call after consulting with his physicians.
5.7.7.5 Existing features partly or fully covering the use case functionality
Editor's Note: The gap analysis is FFS.
5.7.7.6 Potential New Requirements needed to support the use case
[PR.5.7.7.6-1] The IMS shall support means to ensure user experience when multiple IMS media related services are triggered within one call session simultaneously by one user or multiple users.
NOTE: The IMS media related service can include supplementary services, IMS data channel based service, immersive communication service, etc.
Editor's Note: The requirement above is FFS.
5.7.8 Enhancement of voice service
5.7.8.1 Description
Voice service, a fundamental communication service provided by operators, is a cornerstone of telecommunication networks. It enables real time, bidirectional communication between users and is deployed through IP Multimedia Subsystem (IMS) core networks, ensuring seamless global connectivity.
Voice service utilizes codecs standardized by 3GPP for efficient digital representation of audio signals. The selection of a specific codec, e.g. Adaptive Multi-Rate Narrowband (AMR-NB) or Adaptive Multi-Rate Wideband (AMR-WB), depends on both network capabilities and terminal support, ensuring interoperability across the system. Each voice codec type has well-defined operating parameters, including bit rate ranges, sampling rates, and audio bandwidths. For example, the core parameters of AMR-NB [317] and AMR-WB [318] are detailed in Table 5.7.8.1-1.
Table 5.7.8.1-1: The core parameters of AMR-WB and AMR-NB
Codec
(note)
Sampling rate (kHz)
Bit rate(kbit/s)
AMR-NB [317]
8
4.75/5.15/5.90/6.70/7.40/7.95/10.20/12.20
AMR-WB [318]
16
6.60/8.85/12.65/14.25/15.85/18.25/19.85/23.05/23.85
NOTE: The narrowband codec corresponding audio bandwidth is 300–3400 Hz and the wideband codec corresponding audio bandwidth is 50–7000 Hz [319].
With the rapid development of telecommunications technology, the emergence of the 6G era has introduced new expectations for user experience. As a critical service, voice continuity is increasingly demanded, especially in dynamic network environments. Such scenarios include dynamic air interface environmental changes in terrestrial networks (e.g. high-speed rail or mobility between indoor and underground environments) and satellite communications (e.g. Geostationary satellite Earth Orbit (GEO)-Low Earth Orbit (LEO) inter-orbit transitions). These environments present significant challenges to maintain seamless voice service and consistent user experience.
In these dynamic environments, current voice codecs, such as AMR-NB [317] and AMR-WB [318], adjust their bit rates in response to network condition fluctuations. However, such adaptations often require voice codec switch, which can be perceived negatively by the user. For instance, when channel condition degrades (e.g. from high SNR to low SNR), the network forces UE to switch from AMR-WB (e.g. 23.85 kbit/s, 16 kHz) to AMR-NB (e.g. 4.75 kbit/s, 8 kHz) via signalling, as illustrated in Figure 5.7.8.1-1.
Figure 5.7.8.1-1: Codec switch (from AMR-WB to AMR-NB)
In the 4G and 5G era, 3GPP has specified advanced codecs such as EVS [321] and IVAS [320] to, in part, enable adaptive bitrate and audio bandwidth within the codec. This enables seamless changes between codec modes which enables adapting to changing channel conditions gracefully. However, these advanced codecs do not generally specify how to prevent user-perceptible issues when switching codecs entirely during a call.
In the 6G era, the system is expected to provide enhanced voice services to operate seamlessly even under dynamic network conditions. The optimization prioritizes service continuity and user experience during network fluctuations. For instance, 6G voice services require the prevention of user perceived audio disruptions caused by changing codecs.
5.7.8.2 Potential New Requirements
[PR 5.7.8.2-1] The multimedia telephony service [144] provided by IMS shall be able to minimise user perception of the transition during codec modification of an ongoing voice call, e.g. a codec change during communication link fluctuation.
5.7.9 Network coverage and usage verification
5.7.9.1 Description
For network operators deploying private networks and/or offering temporary, high-capacity public network access for events such as festivals and sports tournaments, the ability to provide reliable network coverage and traffic usage can be crucial e.g. for operational assurance and business accountability. Therefore, a standardized, secure, and scalable way for the network operator to deliver the expected coverage and traffic is quite important.
Mechanisms to provide reliable reporting of coverage/capacity metrics should be considered for 6G, including:
a) Proof-of-Coverage: UEs can collect and report coverage information of specific network nodes, for example radio frequency band, measured RF quality, timestamp and location of the measurements.
b) Proof-of-Usage: UEs can collect and report traffic measurement information.
A UE would be authorized and configured by the home operator to provide Proof-of-Coverage/Usage information. The authorized UE monitors and reports certain information of network node(s) based on the configuration. The UE can be either a normal UE (e.g. smartphone/IoT) or a dedicated UE for Proof-of-Coverage/Usage purpose. The following scenarios should be supported:
• The home operator selects and configures the UE to monitor coverage or the UE’s own traffic usage of network nodes (of the home network) in certain geographic areas and/or times. The home operator may also configure a UE to monitor coverage/usage information when roaming (i.e. monitor network nodes of a visited network), subject to inter-operators’ agreement.
• For proof-of coverage, the operator can configure the UE to monitor radio coverage condition of the network nodes in other non-serving networks (which have a business relationship with the home network) in certain geographic areas and/or times.
An authorized UE, in the configured geographic area and/or time, can report necessary information to support operator’s proof-of-coverage and proof-of usage purposes.
5.7.9.2 Existing features partly covering the required functionality
Table 5.7.9.2-1: Gap Analysis for Network Coverage and Usage Verification
Target Functionalities
Existing features
Potential gaps
UE Monitoring and reporting coverage information (Proof-of-Coverage)
MDT [237] is configured by, and reported to, the monitored serving PLMN (HPLMN or VPLMN).
MDT requires the UE to be camped / registered on the monitored cell.
Coverage information is configurable by, and reported to the HPLMN, including monitoring other (non-serving) networks.
The UE (if authorized by the network) may not need to camp or register on the monitored network.
UE Monitoring and reporting traffic usage data (Proof-of-Usage)
With MDT, the network can collect from a UE on the usage of its own radio resources in a serving PLMN.
QoE mechanisms are defined to report specific application information, e.g. for DASH, MBS.
In case of roaming, HPLMN may collect from an authorized UE on its data usage in a visited PLMN (e.g. in local break out scenario),
Reporting can be for specific network nodes, based on geographic area or time.
NOTE: Some gaps may be covered by MDT enhancements.
5.7.9.3 Potential New Requirements
[PR 5.7.9.3-1] Subject to operators’ policies, regulations and user consent, the 6G system shall provide means for a network operator to monitor network coverage and/or traffic usage, including collection of information from UEs (with subscription to the network operator).
NOTE 1: Monitoring and collection of information from a UE is assumed to be authorized and configured by the UE’s home operator, for certain geographical area(s) and/or time(s),
NOTE 2: The traffic usage information collected from a UE is for traffic associated with that UE.
5.7.10 Use case on network sharing on radio access network with sensing capability
5.7.10.1 Description
Given the multitude of use cases for services, some operators, based on their business model, may deploy one or more network capabilities (e.g. data processing capability, computing capability, or sensing data collection capability) in some areas to serve their services, but not in all areas. However, through multi-operator sharing collaboration, the service coverage of specific network capability can be expanded, for instance, this could be used to provide positioning and velocity sensing services for drones, smart transportation and refrigerated transport. This particularly occurs in rural areas, in long-distance logistics and in traffic management, for example, in order to reduce the cost of deployments.
5.7.10.2 Pre-conditions
Figure 5.7.10.2-1: OP#A provides sensing service for logistics UAV tracking in area A plus B
Figure 5.7.10.2-2: OP#B and OP#C provides sensing service for traffic flow and refrigerated transport tracking
For point-to-point delivery by logistics Uncrewed Aerial Vehicles (UAVs) from area A to area B, the sensing service can provide logistics customers with real time positioning and velocity data assistance through operator sensing capability based on the operator’s policy (e.g. via non-3GPP, 3GPP or hybrid sensing methods).
Mobile Network Operator #A (OP#A) can provide sensing service by deploying radio access network with sensing capability (e.g. RAN node as sensing transmitter/sensing receiver) in area A. The coverage of sensing service in OP #A is area A.
Mobile Network Operator #B (OP#B) can provide sensing service by deploying radio access network with sensing capability in area B. The coverage of sensing service in OP #B is area B.
To expand the sensing service coverage for OP#A and OP#B, operators A and B have the sensing network sharing agreement. This will extend their sensing service coverage to area A plus area B, as well as the overlapping coverage area of area A and B through sharing the radio access network with sensing capability.
Mobile Network Operator #C (OP#C) doesn’t deploy the radio access network which has the sensing capability but wants to provide its own sensing service. OP#C can have the sensing network sharing agreement with Operators A and B to share the radio access network with sensing capability of the OP#A and OP#B.
5.7.10.3 Service flows
1. Depending on customer service and regulatory requirements, OP#A provides sensing service for logistics UAV tracking for collection of sensing data related to UAV using the radio access network with sensing capability (e.g. UAV’ position, velocity and environment related data), when the UAV enters the sensing area A.
2. When the UAV enters sensing area B, based on sensing network sharing agreement between operators, the OP#A can use the shared radio access network with sensing capability to collect the sensing data which satisfy the service requirement of OP#A’s customer (e.g. logistics UAV tracking) as Figure 5.7.10.2-1, while these collected data will not be used by other operators. Logistics customers do not need to be aware of which radio access network with sensing capability are used to provide the sensing service.
3. Similarly, OP#B can provide sensing service for smart transportation for collection of sensing data related to motor vehicle using the shared radio access network with sensing capability (e.g. traffic speed and environment related data) in the sensing area B as Figure 5.7.10.2-2. When the cars enter the sensing area A, based on sensing network sharing agreement between operators, OP#B can use the shared radio access network with sensing capability to collect the sensing data which satisfy the service requirement of OP#B’s customer (e.g. smart transportation), while these collected data will not be used by other operators.
4. Similarly, OP#C which doesn’t deploy the radio access network with sensing capability, can use the shared radio access network with sensing capability of OP#A/#B to collect sensing data and satisfy the service requirement of OP#C’s customer (e.g. refrigerated transport) as Figure 5.7.10.2-2.
5. When drones, motor vehicles and refrigerated containers move across the overlapping coverage areas of Areas A and B, the sensing service transmitters and receivers can be located in a shared or non-shared radio access network with sensing capability, or a mix of both.
5.7.10.4 Post-conditions
OP#A can provide sensing services for logistics UAV customers in the area A plus area B.
OP#B can provide sensing services for smart transportation customers in the area A plus area B.
OP#C can provide sensing services to refrigerated transport customers in the area A plus area B.
5.7.10.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] clause 3 includes the definition for NG-RAN sharing and Participating Operator.
NG-RAN Sharing: the sharing of NG-RAN among a number of operators.
Participating Operator: as defined in 3GPP TS 22.101 [58].
The definition of Participating Operator in 3GPP TS 22.101 [58] is as follows:
Participating Operator: Authorized operator that is using Shared NG-RAN, Shared E-UTRAN, Shared GERAN or UTRAN resources provided by a Hosting RAN Operator.
TS 22.137 [6] clause 4.3 and clause 3 introduces that operator can provide sensing service by deploying RAN node with sensing capability (e.g., RAN node as sensing transmitter/sensing receiver)
The introduction of sensing capabilities can enable tracking of people and objects in the environment, including people not carrying UEs. Thus, additional considerations are needed to protect their rights to privacy.
Sensing receiver: a sensing receiver is an entity that receives the sensing signal which the sensing service will use in its operation. A sensing receiver is part of a RAN node or a UE. A Sensing receiver can be located in the same or different entity as the Sensing transmitter.
Sensing transmitter: a sensing transmitter is the entity that sends out the sensing signal which the sensing service will use in its operation. A Sensing transmitter is part of a RAN node or a UE. A Sensing transmitter can be located in the same or different entity as the Sensing receiver.
5.7.10.6 Potential New Requirements needed to support the use case
[PR-5.7.10.6-1] Subject to regulatory requirements or operator policy, the 6G network shall support sharing of radio access network with sensing capability among operators.
5.8 Sustainability and Energy Efficiency
5.8.1 Use case on end-to-end energy efficiency improvement for the network and UE
5.8.1.1 Description
In the ITU-R Recommendation "Framework and overall objectives of the future development of IMT for 2030 and beyond" [39], sustainability is considered as the design principles commonly applicable to all usage scenarios. Supporting end-to-end energy efficiency is an important design target for the sustainability of 6G system.
Regards to end-to-end energy efficiency of 6G system, UE energy efficiency is an important part. Power consumption of modem (including both baseband and RF) has contributed more and more to total UE power consumption due to the large number of power consuming features supported, e.g. AI and computing as well as the strong demand of high data volume transmission. Energy efficiency design for UE could further extend UE battery life while guaranteeing certain service quality. In the 6G system, it is expected that the network could assist the UE to improve energy efficiency as one of the services, e.g. UE could subscribe to energy efficiency services to realize power saving based on network assistance. The network could apply energy saving technologies to improve the UE energy efficiency.
The UE could also assist the network to improve the system energy efficiency to further reduce operation cost for mobile operators. The UE could also help the network to reduce network energy consumption or improve the energy efficiency. So, the UE and network could mutually benefit from the coordination for energy efficiency improvement.
Considering the above, the energy efficiency and energy saving should consider the end-to-end performance including both the network and the UE as shown in Figure 5.8.1.1-1. Considering the sustainability target of the 6G system, it should greatly improve the end-to-end energy efficiency compared to the 5G system.
Figure 5.8.1.1-1: Supporting end-to-end energy efficiency and energy saving
Energy efficiency shall be a quantifiable metric of sustainability and is important for the success of IMT-2030 technology. The energy efficiency KPIs should be defined to ensure that IMT-2030 is designed in an energy efficient way. From the operator's point of view, the KPIs help the operator to decide how much the network energy efficiency is improved by applying a specific energy-saving technology. Then, based on the quantified energy efficiency KPIs, the network could provide energy saving services for the subscribers. From the subscriber's point of view, the energy efficiency KPIs clearly show that the energy saving services are important for them to reduce the power consumption for the data transmission, so they are more willing to subscribe to the services from the network. This would also be a new business model for the operators. In general, energy efficiency KPIs are important and helpful to both the operators and the subscribers.
Energy efficiency should be defined taking into account both communication performance metric and energy consumption. According to the GSMA study [3] and TR 32.972 [4], several measurements means for energy efficiency are proposed by considering the energy consumption and the data transmission. Communication related performance (e.g. data volume, latency, data rate, etc.) of data transmission should be considered for the energy efficiency measurements. For example, energy saving by reducing the data rate cannot always satisfy the subscribers' requirements. It is also important for the network to realize energy saving with guaranteed QoS.
With regards to end-to-end energy efficiency, the energy related information collection from radio access network, e.g. at per UE, or per application granularity, should also be considered. Radio conditions would affect the energy consumption of the UE for the same amount of data volume transmission. For example, the UE location in the Cell affects the energy consumption, e.g. the UE located at the cell edge consumes more energy compared to the one located at the cell centre for the same amount of data volume transmission.
Considering the energy efficiency at the UE, it is closely related with the user experience. Based on different technologies in the 6G network to improve the energy efficiency, the operator could offer different energy saving services to the subscribers. Based on the UE's preference or subscription, the network may apply certain power saving technologies to improve energy efficiency at the UE to satisfy the user's requirement or preference on energy efficiency. This energy saving service for the subscribers is also a new business model for the operators in 6G system. The new business model is also the driven for the 6G system deployment. The energy saving service would further benefit the operators and subscribers to achieve the energy efficiency target in the 6G system.
5.8.1.2 Pre-conditions
The operator has offered a series of energy saving services in the 6G network for the UE to save energy and extend the battery life. Different energy saving services may be provided to the subscribers at different prices or incentives. The subscribers may order certain energy saving services.
For example, the operator A may offer the following two kinds of energy efficiency improvement services:
- Energy saving service A: Improving the energy efficiency by reducing UE energy consumption while maintaining the service experience (e.g. low latency, high throughput). Energy saving service A will improve the UE energy efficiency by extending the UE battery life (e.g. the UE battery life is extended from two hours to four hours.) without compromising the quality of use service experience.
- Energy saving service B: Improving the energy efficiency by reducing UE energy consumption for essential service access, e.g. voice call only. The essential service list is based on the agreement between the subscriber and operator. Energy saving service B will extend the UE battery life with limited communication service.
Bob buys a pair of AR glasses and wants to wear it to access AR content during the trip. Normally, the battery of his AR glasses can last for two hours. Since it is not possible to charge the AR glasses during the trip, he orders the energy saving service A for the glasses from the operator.
Bob also has a cell phone. Sometimes his cell phone is about to run out of battery, but he does not want to miss some important calls or he still wants to access certain applications (e.g. navigation map application). He prefers the network to provide network access for essential services (e.g. voice call) for his cell phone in order to improve the energy efficiency and extend the battery life. So, he orders the energy saving service B from the operator and provides his preference on the essential service list for his cell phone.
5.8.1.3 Service Flows
1. When visiting the pyramids, Bob wears his AR glasses to access specific AR tour guide information. Usually, the battery of his AR glasses can last for two hours. Considering that the tour might last for four hours, in order to save power for this AR glasses, Bob indicates to the network to apply energy saving service A to minimize the power consumption of the AR glasses while ensuring the specific QoS. Based on the indication, the network applies energy saving service A for Bob's AR glasses.
2. After the long trip, Bob needs to take the bus to go back home. It takes nearly one hour. He finds that the battery of his cell phone is below 10% and he cannot charge his cell phone on the bus. So he indicates to the network to apply energy saving service B for his cell phone. Based on the indication, the network applies energy saving service B for Bob's cell phone. The network only provides limited network access to the essential services (e.g. voice call) as predefined by Bob in order to reduce the UE energy consumption.
5.8.1.4 Post-conditions
During the visit to the pyramids, Bob is able to watch AR tour guide information with his AR glasses with good service experience. The AR glasses is used for four hours with good service experience due to the energy saving service A.
During the trip back home, due to energy saving service B, although the battery of Bob’s cell phone is below 10%, it could still access the basic services (e.g. voice call) for nearly one hour.
5.8.1.5 Existing features partly or fully covering the use case functionality
The energy efficiency requirements for 5G system have been defined in clauses 6.15 and 6.15a of TS 22.261 [14], including the general requirements, requirements for energy related information as service criteria, requirements for energy states, requirements for monitoring and measurement, requirements for information exposure, requirements for network actions leveraging energy efficiency as service criteria.
The study on Energy Efficiency as service criteria Phase 2 is under study in SA1 R20. The related use cases and requirements to the 5G system are introduced in TR 22.883 [44].
Some of the procedures and network functions proposed in TR 23.70066 [5] can be used to realize part of the 5G requirements and service flows described in TS 22.261 [14].
The 6G system requirements on the energy efficiency and energy saving are based on the existing requirements in the 5G.
The 5G only specify the monitoring and measurement for energy consumption in 6.15a.4 of TS 22.261 [14] as following:
Subject to operator's policy, the 5G network shall support energy consumption monitoring at per network slice and per subscriber granularity.
Subject to operator’s policy and agreement with 3rd party, the 5G system shall be able to monitor energy consumption for serving this 3rd party.
Subject to operator policy and regulatory requirements, the 5G system shall be able to monitor the energy consumption for serving the 3rd party, together with the network performance statistic information for the services provided by that network, related to same time interval e.g. hourly or daily.
Note the last requirement above only covers monitoring of ‘the energy consumption for serving the 3rd party’ and ‘network performance statistic information’, which are too coarse to estimate the energy efficiency of the 6G system.
The estimation of energy efficiency related information needs to be improved in 6G.
5.8.1.6 Potential New Requirements needed to support the use case
[PR 5.8.1.6-1] The 6G system shall improve energy efficiency of 6G system compared to 5G system.
[PR 5.8.1.6-2] The 6G network shall be able to support mechanisms to improve UE energy efficiency when providing services to the subscribers.
[PR 5.8.1.6-3] The 6G system shall provide means for a user to provide a preference for UE energy efficiency.
NOTE: The 6G network takes into account this preference when improving UE energy efficiency. Examples of preference are user preference on prioritizing service performance, prioritizing UE energy efficiency, or prioritizing essential service (e.g. voice call) while improving UE energy efficiency.
5.8.2 Use case on energy efficiency of 6G system with multiple access networks (TN and NTN)
5.8.2.1 Description
6G system is assumed to support both Terrestrial Network (TN) and Non-Terrestrial Network (NTN) access networks. The energy consumption associated to each access network may differ for a one-to-many service (e.g. PWS [62], broadcast, multicast) and the number of targeted users and/or targeted service area.
The objective is to minimize the overall 6G system energy consumption taking advantage of the different access networks characteristics in terms of energy consumption model.
5.8.2.2 Pre-conditions
A network infrastructure provides one-to-many services to a set of UEs over a given coverage area thanks to multiple options involving different access networks among which are TN and NTN.
5.8.2.3 Service Flows
The energy consumption of the network infrastructure for a one-to-many service can be determined depending on the number of targeted users.
Based on energy consumption criteria, the network operator may decide for a one-to-many service to change access network for the service delivery.
The most energy efficient access network is the one that requires the least energy for the delivery of the one-to-many service. We may distinguish two types of energy consumption determination:
The energy required to serve all UEs in idle mode over a given geographical area (Joule/Km2)
The energy required to establish a one-to-many service session (broadcast/multicast) with a set of UEs in connected mode. This energy will depend on the bandwidth of the service session (Joule/bps) and the number of targeted users. Both energy determination types can be used to assess the most energy efficient access network.
5.8.2.4 Post-conditions
The overall 6G system energy consumption (at RAN and UE level) is optimized by selecting the most energy efficient access network for a one-to-many service and the targeted set of UEs and/or a targeted service area.
5.8.2.5 Existing features partly or fully covering the use case functionality
None.
5.8.2.6 Potential New Requirements needed to support the use case
[PR 5.8.2.6-1] The 6G network with multiple access networks (e.g. terrestrial, satellite) shall provide means to determine (e.g. through measurement and/or calculation) the energy consumed by the access networks for the provision of a one-to-many service (e.g. PWS [62], broadcast, multicast) characterised by at least the QoS, targeted service area and/or targeted set of users.
[PR 5.8.2.6-2] The 6G network shall be able to support means to provide the network operator an indication of the energy consumed by the available access networks for the provision of a one-to-many service.
NOTE: The network operator may take into account this energy consumption information when deciding how to deliver the service.
Editor’s note: The energy consumption information is FFS.
5.8.3 Use case on supporting energy control at slice level
5.8.3.1 Description
Mobile networks consume significant amounts of energy, contributing to greenhouse gas emissions and climate change. Prioritizing network energy efficiency is paramount. Recent ongoing work in 3GPP underlines energy awareness in mobile networks by monitoring energy consumption and supporting the collection of charging information on various granularities, one of which is per network slice. Such monitoring provides valuable information for optimizing network operations and minimizing expenses. Efficient utilization of this information has the potential to make significantly more energy and cost-sensitive decisions. This allows for an efficient strategic approach to network resource allocation and ultimately leads to substantial energy savings and sustainability.
This use case describes a scenario in which upon request the MNO deploys private slice(s) for 3rd party to consume services. In this scenario, the 3rd party (service consumer) for example, an Industry, Institute, small-scale business, or start-up, desires to curtail its expenditures associated with its private slice services. Hence, the 3rd party is concerned about the access-related energy use of its private slice. This use case demonstrates that by providing the 3rd party the ability to define an upper bound on the aggregate quantity of energy consumption for its private slice(s) and a means to limit the energy consumption of its private slice(s), the 6G system supports sustainability.
5.8.3.2 Pre-conditions
The 6G system of an MNO (service provider) supports energy information exposure to 3rd parties for their private slices based on the agreement. As per [212], estimatedEnergyConsumption has been included in network slice performance and analytics container specific information. This field holds the estimated energy consumption of network slice in Joule.
5.8.3.3 Service Flows
As depicted in Figure 5.8.3.3-1, an MNO deploys a private 6G network slice for an Industry ‘A’ to support its internal services like Industrial Internet of things , predictive maintenance, file sharing, email, cloud computing, VPN, security services, etc. Industry ‘A’ is a small-scale industry and has budget limitations. Thus, it wants to reduce both the energy consumption and the charges associated with its private slice. Industry ‘A’ decides to define a private slice energy credit control limit that sets an upper bound on the aggregate quantity of energy consumption of its private slice. When this threshold is exceeded, the operator exposes this event to Industry ‘A’.
Figure 5.8.3.3-1: Enforcement of energy credit limit on per slice basis for an Industry
5.8.3.4 Post-conditions
The 3rd party is able to perform private slice energy credit control and reduce its operational cost.
5.8.3.5 Existing features partly or fully covering the use case functionality
Subject to operator's policy, the 5G network shall support energy consumption monitoring at per network slice and per subscriber granularity (TS 22.261 [14] clause 6.15a.4.2).
NOTE 1: Energy consumption monitoring as described in the preceding requirement is done by means of averaging or applying a statistical model. The requirement does not imply that some form of 'real time' monitoring is required. The granularity of the subscription policies can either apply to the subscriber (all services), or to particular services.
Subject to operator’s policy and agreement with 3rd party, the 5G system shall be able to monitor energy consumption for serving this 3rd party (TS 22.261 [14] clause 6.15a.4.2).
NOTE 2: The granularity of energy consumption measurements could vary according to different situations, for example, when several services share the same network slice, etc.
NOTE 3: The energy consumption information can be related to the network resources of network slice, NPNs, etc.
The 5G core network shall support collection of all charging information on either a network or a slice basis (TS 22.261 [14] clause 9.1).
The 5G core network shall support collection of charging information based on the slice that the UE accesses (TS 22.261 [14] clause 9.1).
Subject to operator’s policy, the 5G system shall support subscription policies that define a maximum energy credit limit for services without QoS criteria (TS 22.261 [14] clause 6.15a.2.2).
Subject to operator’s policy, the 5G system shall support a means to associate energy consumption information with charging information based on subscription policies for services without QoS criteria (TS 22.261 [14] clause 6.15a.2.2).
Subject to operator’s policy, the 5G system shall support a mechanism to perform energy consumption credit limit control for services without QoS criteria (TS 22.261 [14] clause 6.15a.2.1).
NOTE 4: The result of the credit control is not specified by this requirement.
NOTE 5: Credit control [213] compares against a credit control limit. It is assumed charging events are assigned a corresponding energy consumption and this is compared against a policy of energy credit limit. It is assumed there can be a new policy to limit energy consumption allowed.
Subject to operator’s policy, the 5G system shall support a means to define subscription policies and means to enforce the policy that define a maximum energy consumption (i.e. quantity of energy for a specified period of time) for services without QoS criteria (TS 22.261 [14] clause 6.15a.2.2).
Subject to operator’s policy, the 5G system shall support a means to expose energy consumption to authorized third parties for services, including energy consumption information related to the condition of energy credit limit (e.g. when the energy consumption is reaching the energy credit limit) (TS 22.261 [14] clause 6.15a.5.2).
Based on operator’s policy and agreement with 3rd party, the 5G system shall be able to expose energy consumption information and prediction on energy consumption of the 5G network per application service to the 3rd party (TS 22.261 [14] clause 6.15a.5.2).
Subject to operator’s policy and agreement with 3rd party, the 5G system shall be able to expose information on energy consumption for serving this 3rd party (TS 22.261 [14] clause 6.15a.4.2),
NOTE 6: Energy consumption information can include ratio of renewable energy and carbon emission information when available. The reporting period could be set, e.g. on monthly or yearly basis and can vary based on location.
NOTE 7: The energy consumption information can be related to the network resources of network slice, NPNs, etc.
Subject to operator’s policy, regulatory requirements and user consent, the 5G network shall support subscription policies that include alternative (i.e. degraded) service performance (e.g. QoS parameters, maximum bitrate) of services with QoS criteria for energy saving reasons.
NOTE 8: This requirement implies that the policies could disallow some network energy saving action to be performed for some service with QoS criteria e.g. based on applicability conditions (e.g. slice, application).
5.8.3.6 Potential New Requirements needed to support the use case
[PR 5.8.3.6-1] Subject to operator’s policy, the 6G system shall support network slice-specific energy management policies that define a maximum slice energy credit limit for services without QoS criteria.
[PR 5.8.3.6-2] Subject to operator’s policy, the 6G system shall support a mechanism to perform slice energy credit limit control for services without QoS criteria.
NOTE: The result of the credit control is not specified by this requirement.
5.8.4 Use case on joint energy saving for network and UE with various loads
5.8.4.1 Description
In the ITU-R Recommendation "Framework and overall objectives of the future development of IMT for 2030 and beyond" [28], sustainability is considered as the design principles commonly applicable to all usage scenarios. Supporting end-to-end energy efficiency is an important design target for the sustainability of 6G system.
Network energy saving is important to reduce the carbon emissions for the industry sustainability, and in the same time reduce the Operational Expenditure (OPEX) of the operators. For network energy saving, since the traffic distribution over the time and geography is very unequal, the base station will work in really different load conditions, so the energy saving in different conditions should be considered, for both idle/low loaded condition and the high loaded condition.
UE energy saving is also very important for sustainability, as well as user experience. For example, in 5G network, lots of UE energy is wasted consumed due to downlink monitoring when there if no traffic. On the other hand, in the IMT-2030 vision [27], there are lots of new applications such as mobile AI, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents etc., which will significantly increase the amount of traffic, especially uplink traffic. Therefore, the UE energy saving for both idle/low loaded condition and the high loaded condition should be considered.
The evaluation on the system can provide guidance to support the end-to-end energy efficiency design for the 6G system. The current evaluation on the communication system is based on the UE Perceived Throughput (UPT), which requires the UE to be served as soon as possible when the service triggers. However, from the user perspective, this is not perceived by the user, i.e. the user experience stays almost the same when transmission QoS is satisfied. This means that for some services, the UE is over provisioned by the network, which doesn’t improve user experience but wastes network power. Thereby, when evaluating the energy consumption, throughput/UPT should not be pursued as the target, instead, what the user actually perceives should be used for evaluation. In other words, the energy consumption should be evaluated under the same user QoS satisfaction, e.g. reliability of provided service, where the reliability is defined in TS 22.261 [14] as percentage value of the packets successfully delivered to a given system entity within the time constraint required by the targeted service out of all the packets transmitted in the context of network layer packet transmissions.
Joint consideration of network and UE energy saving should be considered from day one, not only for the joint cooperation to improve the energy efficiency and to reduce the power consumption for both sides, while meeting the service requirement requested by the UE.
5.8.4.2 Pre-conditions
There are two UEs (UE#1 and UE#2) under a network#A, where UE#1 and UE#2 are with different service (i.e. different traffic patterns).
UE can work via two modes. Mode#1 is for saving power at no traffic or low traffic conditions, where the UE can reduce the power consumption via working at a mini-kernel capability, for example, working with small number of antennas (e.g. 2RX) or narrow bandwidth (e.g. 20 MHz). Mode#2 is for supporting middle or high traffic conditions, where the UE works using the full communication capability, e.g. large number of antennas and wide bandwidth.
Similarly, network also has two modes. In mode#1, the network can also use limited bandwidth and antennas to save power during at no traffic or low traffic conditions. While mode#2 is used for middle or high traffic conditions. Under both modes, the network can serve the UE satisfying the QoS, while saving both UE and network’s power, e.g. stay sleep as much as possible by aggregating both UEs’ traffic packets in time domain.
5.8.4.3 Service Flows
There is no traffic or low traffic for both UE#1 and UE#2, so both of the two UEs can stay in mode#1 to save energy. At the same time, since neither of the UEs has traffic, network can also be in mode#1.
The high traffic of UE#1 arrives or the NW predicts the arrival of the high traffic of UE#1, while the traffic of UE#2 does not arrive yet or low traffic of UE#2 arrives. To better serve UE#1, network switches to the mode#2 to reduce its sleep time and/or increase the transmission capability, and UE#1 switches to mode#2. When staying in mode#2, network can also stay sleep as much as possible by aggregating UE#1’ traffic packets exploiting the traffic pattern information in time domain, as long as the service requirement requested by the UEs can be satisfied. For example, the service requirement requested by the UE can be evaluated by the reliability of the provided service, i.e. percentage value of the packets successfully delivered to a given system entity within the time constraint required by the targeted service out of all the packets transmitted in the context of network layer packet transmissions, defined in TS 22.261 [14]. After that, the data of UE#1’s traffic is transmitted efficiently. UE#2 keeps in mode#1.
The transmission of UE#1’s traffic is finished. The high traffic of UE#2 arrives or the NW predicts the arrival of the high traffic of UE#2. To serve UE#2, network keeps in mode#2. UE#2 switches to mode#2, while UE#1 switches back to mode#1.
After the transmission of UE#2’s traffic is finished, UE#2 switches back to mode#1, and network also switches back to mode#1 for energy saving.
5.8.4.4 Post-conditions
Both UE and network can save energy consumption when there is no traffic or low traffic when UE is at mode#1. Once traffic arrives, UE and network can trigger data transmission/reception via mode#2 to transmit data efficiently, while power consumption can still be optimized by exploiting the traffic patterns of the service while guaranteeing the service requirement requested by the UE.
5.8.4.5 Existing features partly or fully covering the use case functionality
Table 5.8.4.5-1: Gap Analysis for joint energy saving for network and UE with various loads
Specifications and clauses
Examples of Existing
Requirements
Gap Analysis
TS 22.261 [14]
6.15 Energy efficiency
6.15a Energy Efficiency as a Service Criteria
The 5G access network shall support an energy saving mode with the following characteristics:
- the energy saving mode can be activated/deactivated either manually or automatically;
- service can be restricted to a group of users (e.g. public safety user, emergency callers).
NOTE: When in energy saving mode the UE's and Access transmit power may be reduced or turned off (deep sleep mode), end-to-end latency and jitter may be increased with no impact on set of users or applications still allowed.
The 5G system shall support mechanisms to improve battery life for a UE over what is possible in EPS.
The 5G system shall optimize the battery consumption of a relay UE via which a UE is in indirect network connection mode.
The 5G system shall support UEs using small rechargeable and single coin cell batteries (e.g. considering impact on maximum pulse and continuous current).
The 5G system shall support different energy states of network elements and network functions.
5G system shall support dynamic changes of energy states of network elements and network functions.
NOTE: This requirement also includes the condition when providing network elements or functions to an authorised 3rd party, the dynamic changes can be based on pre-configured policy (the time of changing energy states, which energy state map to which level of load, etc.)
The 5G system shall support different charging mechanisms based on the different energy states of network elements and network functions.
In the 5G requirements, the load is proposed for mapping the energy state of the network element and network functions, which is a fixed mapping. Considering the various loads change, the UE and the network’s energy saving are not considered it.
Also, in the 5G evaluation on the energy saving, the over previsioning is not noticed, which results in the power waste of both the network and UE. This gives the chance to save the energy with the target that the service requirements requested by the UE is fulfilled.
5.8.4.6 Potential New Requirements needed to support the use case
[PR 5.8.4.6-1] Subject to local regulation, the 6G system shall be able to optimize the network energy saving considering the current traffic conditions of the network.
[PR 5.8.4.6-2] Subject to local regulation and user consent, the 6G system shall be able to optimize the UE energy saving considering the current traffic conditions of the UE.
[PR 5.8.4.6-3] Subject to local regulation and user consent, the 6G system shall be able to optimize network and UE energy saving jointly, while meeting the service performance requirements.
5.8.5 Use case on UE energy efficiency for XR rendering/AI tasks
5.8.5.1 Description
User requirements for data rates and QoS are growing exponentially. Running processing-intensive applications (e.g. XR rendering application, AI inference application) at the UE is constrained by limited battery capacity and energy consumption of the UE. High battery consumption creates serious obstacles that limit users to fully enjoy such applications on their devices. For example, the higher the battery consumption is, the shorter the battery life is. On the other hand, even if the high battery consumption is only within a short time duration (e.g. several minutes), it leads to the thermal issue, which significantly impact the user experience. Therefore, to extend the battery life, as well as to avoid the thermal issue, the UE energy consumption should be reduced.
In order to reduce UE’s energy consumption for processing capabilities, offloading the XR rendering/AI tasks from UE to the core network or Service Hosting Environment is a promising trend since the XR rendering/AI tasks (e.g. rendering, AI inference) do not have to be executed locally. However, as depicted in Figure 5.8.5.1-1, the UE still spends certain amount of energy for communication in order to 1) transmit uplink data for XR rendering/AI tasks to the core network or Service Hosting Environment, and 2) receive results of the XR rendering/AI tasks from the core network or Service Hosting Environment.
Normally, the offloading decision is based on data transmission status and processing resources availability in the cloud in order to maintain deterministic execution latency. Different XR rendering/AI tasks offloading decision will bring different energy consumption of UE for both data transmission and processing.
Therefore, how to minimize the combined energy consumption of UE needs to be considered in 6G.
Figure 5.8.5.1-1: Combined energy consumption of UE for XR rendering/AI tasks
5.8.5.2 Pre-condition
1. Processing resources are deployed in the core network or Service Hosting Environment.
2. The UE#1 and UE#2 support to map the XR rendering/AI tasks from the application to the local and/or the remote (in the core network or Service Hosting Environment).
5.8.5.3 Service flow
Figure 5.8.5.3-1 depicts the deployment scenario and flow for XR rendering/AI task offloading.
Figure 5.8.5.3-1: UE energy efficiency for XR rendering/AI tasks
Both UE#1 and UE#2 are successfully registered with the 6G network. Both UE#1 and UE#2 are visiting the same applications (e.g. XR rendering application, AI inference application).
At T#1, the UE#1 performs the XR rendering/AI tasks locally since the energy consumption of local execution (Eprocessing) is significantly lower than the energy consumption of data transmission/reception required by the task offloading (Edata transmission).
At the same time, the UE#2 offloads data to the Multi-access Edge Computing (MEC) server as the energy consumption of data transmission/reception required by the task offloading (Edata transmission) is significantly lower than energy consumption of local execution (Eprocessing).
At T#2, the data transmission link changes. the UE#1 offloads tasks to the MEC server as the energy consumption of data transmission/reception required by the task offloading (Edata transmission) is significantly lower than energy consumption of local execution (Eprocessing).
At the same time, the UE#2 decides to perform the XR rendering/AI tasks locally since the energy consumption of local execution (Eprocessing) is significantly lower than the energy consumption of data transmission/reception required by the task offloading (Edata transmission).
In other words, the combined energy consumption for both data transmission and processing are minimized for each UE.
5.8.5.4 Post condition
The XR rendering/AI tasks offloading decision minimizes the energy consumption at the UE while satisfying the execution delay of the XR rendering/AI tasks.
5.8.5.5 Existing features partly or fully covering the use cases functionality
Table 5.8.5.5-1: Gap analysis for this use case
Specifications and clauses
Examples of Existing
Requirements
Gap Analysis
TS 22.261 [14]
6.15 Energy efficiency
The 5G access network shall support an energy saving mode with the following characteristics:
- the energy saving mode can be activated/deactivated either manually or automatically;
- service can be restricted to a group of users (e.g. public safety user, emergency callers).
NOTE: When in energy saving mode the UE's and Access transmit power may be reduced or turned off (deep sleep mode), end-to-end latency and jitter may be increased with no impact on set of users or applications still allowed.
The 5G system shall support mechanisms to improve battery life for a UE over what is possible in EPS.
The 5G system shall optimize the battery consumption of a relay UE via which a UE is in indirect network connection mode.
The 5G system shall support UEs using small rechargeable and single coin cell batteries (e.g. considering impact on maximum pulse and continuous current).
Normally, the offloading decision is based on data transmission status and processing resource availability in the cloud in order to maintain deterministic execution latency.
Different XR rendering/AI tasks offloading decision will bring different energy consumption of UE for both data transmission and processing.
During task offloading decision, the requirements to optimize the battery consumption of UE are not considered in 5G.
5.8.5.6 Potential New Requirements needed to support the use case
[PR 5.8.5.6-1] Subject to user consent, the 6G system shall support mechanisms to optimize the energy consumption of a UE via enabling task offloading (e.g. XR rendering/AI inference) from the UE to the Service Hosting Environment, considering battery life and thermal issue of UE.
5.8.6 Use case on energy saving for network in industry park
5.8.6.1 Description
In the 5G phase, UE could select specific energy saving actions, and the associated network activities with the energy saving actions are defined by operator, e.g. modify the network policies, for example, QoS modification, or enable/disable related network function etc. In 6G, considering there will be not only communication service, but also new sensing capability, AI and computing related capabilities, the mechanisms for energy saving could be enhanced and offer better energy efficiency for operator, trusted 3rd party and users.
For example, in one specific port industry area, in the daylight, 6G system not only provides communication service but also higher accuracy sensing service, AI and computing service, to support all delivery robot UEs moving to transfer goods which cause significant energy consuming; in the evening, most of delivery robot UEs are static and the sensing service is not in use, thus the related energy consumption could be saved via disabling the sensing operation. It is also the same for the AI and computing services.
In conclusion, the energy saving mechanism is provided by 6G network and adopted by operator, allowing operator to modify the network policies (e.g. QoS modification), enable/disable network function, to provide on demand 6G services with balance between energy efficiency and service performance.
And it is also expected that such energy saving method could be done automatically by 6G network itself based on operator’s policy, according to the energy state and service performance.
5.8.6.2 Pre-conditions
In a port industry park, MNO MM has deployed 6G network to provide communication, sensing and AI and computing services for the customer. Also, some passive network nodes are deployed at this industry park to enlarge the coverage of wireless signal in an energy efficient way.
6G network provides several energy saving methods to MNO MM as following:
- Energy saving method#A: Enable passive network nodes in this port industry park to assist communication and sensing service for energy saving and large coverage.
- Energy saving method#B: Inactive AI and computing operation
- Energy saving method #C: Inactive sensing operation of 6G system in this port industry park.
According to state of network, 6G network could be able to estimate the performance characteristics and energy-related characteristics related to specific energy saving methods, and provide them to MNO MM. MNO MM could adopt some energy saving methods to fulfil the requirement of energy consumption and service performance. What’s more, the operator has allowed the 6G network to automatically adopt energy saving methods.
Alex is a delivery robot operator, working in this industry park. His work is controlling several robots to transfer goods according to working requirement from port to the industry nearby.
5.8.6.3 Service Flows
In the working time of daylight, a lot of goods have been placed in the port. Alex controls 10 delivery robots to transfer these goods to the industry nearby. The 6G network provides large-bandwidth and low-delay communication service for Alex’s robots. The base stations in this park start to transmit and receive the sensing signal to monitor the position and environmental information of Alex’s robots.
According to working requirements, Alex needs to control robot to some place without strong signal coverage temporarily. Noticing that the current network coverage is not sufficient for today’s requirement on high accurate sensing and stable communication, but still considering its network energy efficiency, MNO MM adopts Energy saving method#A to enable passive network nodes in this port industry park to assist communication and sensing service. Benefiting from low energy consuming of passive node, the performance of sensing and communication of these robots in this place could be ensured.
Some delivery jobs are assigned to Alex, and Alex needs to control robot to go to port and back to industry repeatedly.
In the evening, the delivery work has been finished. The robots keep standing by statically. No sensing results are needed to assist robots’ work and no frequent handing over is requested. Noticing the current the state of network, the 6G network automatically adopt Energy saving method#C and #B for saving both energy and computing capacity.
5.8.6.4 Post-conditions
Thanks to the 6G network, MNO MM saves energy consuming by base stations deployed in this port park, while ensuring that Alex enjoys stable and efficient sensing and communication service.
5.8.6.5 Existing features partly or fully covering the use case functionality
The energy efficiency requirements for 5G system have been specified in clause 6.15 of TS 22.261 [14]. The study on Energy Efficiency as service criteria Phase 2 is under study in SA1 R20.
In TS 22.261 [14], there exist the following requirements:
Subject to operator’s policy, regulatory requirements and user consent, the 5G network shall be able to collect, and expose to authorized 3rd parties, the carbon equivalent emissions resulting from the use of the communication service, related to one or more specific UEs of home network subscribers (e.g. fleet of vehicles, IoT devices, company phones etc[[SUGGESTION_START]].[[SUGGESTION_END]]), over a specific time period (e.g. month etc.).
Only energy consuming information from the use of communication services are discussed. As far as sensing service, it would also be important to collect and expose the energy consuming information related to the use of beyond services e.g. sensing operation, AI and computing operations etc.
5.8.6.6 Potential New Requirements needed to support the use case
[PR 5.8.6.6-1] Subject to operator’s policy, regulation and user’s consent, the 6G network shall provide suitable energy saving methods targeting different scenarios (e.g. different working time).
[PR 5.8.6.6-2] Subject to regulation and operator’s policy, the 6G network shall be able to expose to a trusted third-party the network energy consumption information including the energy consumption related to sensing, AI, and computing services, over a specific time period (e.g. month etc.).
5.8.7 Use case on green communications and computing optimisation using network digital twin
5.8.7.1 Description
Network Digital Twins are real time digital counterparts of physical networks or in other words an NDT is a digital replica of the full life cycle of a physical network, gaining recognition as a transformative technology for designing, diagnosing, simulating, conducting what-if analyses, and enabling AI/ML-driven real time optimisation and control in 6G wireless networks [214]. It could be capable of generating perceptive and cognitive intelligence based on collection of historical and on-line network data. It may also be capable of continuously seeking the optimal state of the physical network in advance and enforcing management operations accordingly. [39]
However, realizing the full capabilities of NDTs in 6G requires overcoming several design challenges, particularly in areas like data management, modelling, and defining new interfaces.
Digital twin technology is set to become prevalent across all industries. More and more, products and systems will first be simulated in a virtual environment before being produced in the physical world, with a digital twin being developed in the process. Additionally, in the future, all moving machines are expected to operate autonomously, and they will undergo training and learning within virtual environments. Digital twins have the potential to provide ubiquitous tools and knowledge platforms for the modelling, monitoring, managing, analysing and simulating of physical assets, resources, environments and situations. [27]
Energy Efficiency in 6G Networks:
Energy efficiency is a key focus for 6G, as networks are expected to support exponentially higher data rates and device densities. NDT can play a crucial role in reducing the carbon footprint of networks, in particular the compute infrastructure. The 6G system should leverage NDTs to enable real time optimisation of energy consumption across the network. This involves creating a digital replica of the network that continuously monitors energy usage, predicts demand, and dynamically adjusts network resources (e.g. turning off unused components, scaling down capacity during low traffic) to minimize energy consumption while maintaining service quality.
NDT can optimise energy efficiency in both network communications and computing workloads. The goal is to minimize carbon footprint by dynamically adjusting network and cloud resource usage based on real time and predictive AI-driven models. The NDT continuously monitors and simulates network energy consumption, IT infrastructure workload distribution, and application energy efficiency. It helps operators achieve carbon neutrality targets by optimising data centre operations, edge computing resource allocation, and green network routing.
NGMN has also identified Digital Twin (DT) as one of the key enabling services for 6G [219].
5.8.7.2 Pre-conditions
This clause is not applicable.
5.8.7.3 Service Flows
1 Operator A's network is mirrored by an NDT. The NDT collects real time energy consumption data from cloud computing resources (centralized data centres and edge nodes), and AI workload distribution across compute servers in the cloud. Utilizing this data, AI models within the NDT generate a digital replica of the network’s power usage, source, renewable energy usage, carbon emissions etc., enabling precise analysis and optimisation of energy efficiency.
2 The Green Computing Engine within the NDT simulates various strategies for optimising energy consumption, compute allocations, and workload migrations. These simulations enable dynamic load balancing, allowing workloads to be shifted to more energy-efficient data centres based on real time power availability. To further enhance efficiency, the NDT supports adaptive compute workload scheduling, reducing peak-hour energy usage by intelligently distributing processing tasks across low-demand periods.
3 Once optimal configurations are identified, the NDT dynamically adjusts various parameters to enhance energy efficiency, cloud workload distribution to maximize the use of renewable energy, and energy-aware edge computing to minimize unnecessary data transfers and processing overhead. These real time adjustments ensure that network and computing resources operate sustainably while maintaining performance and reliability.
4 NDT continuously monitors computing performance after implementing energy optimisation strategies, ensuring that efficiency gains do not compromise service quality. Leveraging real time data, AI models analyse observed energy savings and performance impacts, refining future sustainability strategies to further enhance efficiency. This continuous feedback loop allows the 6G system to dynamically adapt to changing network conditions, improving energy management over time while maintaining optimal performance and reliability.
5.8.7.4 Post-conditions
The 6G network compute resources operate at optimal energy efficiency while maintaining service performance, dynamically adjusting resource allocation through real time feedback from the NDT. AI-driven sustainability strategies ensure adaptive workload distribution, prioritization of renewable energy, and proactive power management, enabling continuous optimisation with minimal carbon footprint.
5.8.7.5 Existing features partly or fully covering the use case functionality
While SA5 (TR 28.915) [77] studies NDT for network management, this use case specifically focuses on sustainability and energy efficiency optimization across computing layers. This aligns with SA1’s responsibility for defining new service requirements in 6G.
5.8.7.6 Potential New Requirements needed to support the use case
NOTE: These requirements only apply if the 6G network supports a Network Digital Twin.
[PR 5.8.7.6-1] The 6G network shall provide suitable means for communicating with the NDT of operator controlled compute resources that are in the Service Hosting Environment or the cloud, for the purpose of energy consumption analysis and optimisation.
5.8.8 Use case on end-to-end energy saving by cooperating UEs
5.8.8.1 Description
Users own and carry an increasing number of UEs of different types, each of the UEs with their respective (shared) subscriptions so that each of the UEs can connect to the cellular network. Examples of those UEs include smart phones, smart watches, or smart glasses. Similarly, users when travelling, working, or enjoying their hobbies rely on their UEs for both entertainment and work. Oftentimes, users may reside or move together as a group,
In current cellular systems, those UEs work independently of each other, even if carried by the same user, and thus, each being subject to the same communication overhead. Similarly, users that reside or move together as a group are subject to similar radio environment parameters, mobility and/or user interactions. This is a lost opportunity to reduce the end-to-end energy consumption as a whole.
For instance, a user, Dave, has a smart phone and smart glasses. Dave may be carrying out a call via his smart glasses, but (1) Dave may not be able to finish the call due to a low battery level of the smart glasses because the smart glasses require too much power to communicate (directly) with the base station and/or (2) the base station may need to use increased transmission/reception power to communicate with the smart glasses compared with the transmission/reception power required to communicate via the smart phone.
Thus, the system performance can be improved by exploiting the fact that Dave is carrying two UEs and they can be coordinated by the cellular system to cooperate with each other optimizing the end-to-end energy savings. For example, the devices may use cooperative transmission or by transmitting (a subset of) the data via direct and/or indirect network connection. This can lead to significant energy savings for the UEs involved (which can be over 30%) as described in [311], [322], [307], [308], [309], [310]. In order to support this, the network can configure (e.g. through policies) the devices accordingly, e.g. which cooperative communication schemes can be used under which circumstances, or how much data is sent by one device and how much of the data is sent by the other device or by both devices.
5.8.8.2 Pre-conditions
Dave subscribes to the communication services of Operator A and registers his UEs, namely his mobile phone and smart glasses, as cooperating devices. This registration may be added as subscription data to a subscription of the cooperating devices and may be a shared subscription. UEs registered as cooperating devices are allowed to perform certain operations in a coordinated manner to save energy. This may also be a group of UEs for which the owners have an interest for their UEs to cooperate, such as for public safety.
Both his mobile phone as well as his smart glasses can work independently from each other. They can act as cooperating devices if they are in proximity of each other. The proximity can be detected e.g. through the use of location or ranging services or through non-3GPP connection between the two devices). Detection of proximity by the UEs or the network may be done with low frequency (e.g. once per 5 minutes) to save energy, or proximity changes may only be reported by the UE(s) when the proximity is persistent, e.g. when the devices are stationary or are detected not to be close anymore.
Operator A uses a cellular system that provides enhanced energy savings by coordinating the cooperation between UEs.
5.8.8.3 Service Flows
1. Dave decides to go for a long walk. He uses his smart glasses to show him navigation instructions whilst doing some sightseeing. He uses his mobile phone to run some other applications e.g. to keep in touch with his friends and receive some phone calls. Although he charged his mobile phone last night, his smart glasses were charged only two days ago. Luckily, the network knows that are the UEs are in close proximity with each other and can act as a group of cooperating UEs. Given that the UEs are close to each other, the measurements to be performed on wireless signals are expected to be quite similar. Therefore, in order to save energy, the network requests measurements from only one of the UEs. Using similar mechanisms, the signalling can be reduced for paging and during handover.
2. Dave, after walking 2 Km, is getting a bit tired. When taking a break, he would like to share a video that he made with his smart glasses with his friend Luigi. However, the battery of his glasses is at a very low level and the video is quite big. However, the smart phone still has enough battery; and the smart phone can cooperate in performing the uplink data transfer, e.g. using cooperative transmission or by transmitting (a subset of) the data via direct and/or indirect network connection to reduce the energy consumption of the glasses. Also, the base station would need to use less transmission power and perform less retransmission because of the good quality connection with the smart phone.
5.8.8.4 Post-conditions
Our user Dave enjoys high quality of service, and Dave’s devices have a longer battery life so that he can access the 6G network services for a longer period of time without recharging his devices. The network (e.g. base stations) also benefits energy-wise of the device cooperation since signalling, e.g. measurements are reduced.
5.8.8.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] has several requirements related to optimizing performance for multiple UEs, however, these requirements are more related to broadcasting downlink data and relaying, not related to cooperation/coordination between UEs for energy savings. Also, the requirements cover indirect connections, however the configurations/policies for using those indirect connections is for coverage extension, not for energy saving. Energy saving is likely to require different configuration/policies to be used.
For example, the following requirements have been specified in clauses 6.2.2, 6.4.2.2, 6.9.2, 6.40.2.1, 6.43.2 of TS 22.261 [14]:
The 5G network shall allow operators to optimize network behaviour (e.g. mobility management support) based on the mobility patterns (e.g. stationary, nomadic, spatially restricted mobility, full mobility) of a UE or group of UEs.
…
The 5G network shall optimize the resource use of the control plane and/or user plane to support bulk operation for high connection density (e.g. 1 million connections per square kilometre) of multiple UEs.
The 5G system shall support a timely, efficient, and/or reliable mechanism to transmit the same information to multiple UEs.
…
The 5G system shall support the relaying of traffic between a remote UE and a gNB using one or more relay UEs.
The 5G system shall support same traffic flow of a remote UE to be relayed via different indirect network connection paths.
The 5G system shall support different traffic flows of a remote UE to be relayed via different indirect network connection paths.
…
The 5G system shall be able to expose aggregated QoS parameter values for a group of UEs to an authorized service provider.
…
The 5G system shall enable an authorized 3rd party to provide policy(ies) for flows associated with an application. The policy may contain e.g. the set of UEs and data flows, the expected QoS handling and associated triggering events, other coordination information.
The 5G system shall support a means to apply 3rd party provided policy(ies) for flows associated with an application. The policy may contain e.g. the set of UEs and data flows, the expected QoS handling and associated triggering events, other coordination information.
NOTE: The policy can be used by a 3rd party application for coordination of the transmission of multiple UEs’ flows (e.g., haptic, audio and video) of a multi-modal communication session.
Furthermore, as specified in TS 38.300 [221] clause 16.21, a non-3GPP connection may exist between UEs in case of an indirect connection path, and the presence of such non-3GPP connection is conveyed to the base station. This implies that the base station is aware that the two devices are in close proximity of each other without having to rely on 3GPP location or ranging services.
5.8.8.6 Potential New Requirements needed to support the use case
[PR 5.8.8.6-1] Subject to user consent and operator policy, the 6G system shall support a means for a group of cooperating UEs to reduce the energy consumption for communication of the group of UEs whilst meeting requested service performance.
5.9 Network Aspects
5.9.1 Use case on support of femtocells for localized deployment
5.9.1.1 Description
The legacy 3GPP systems defined capabilities/functionalities to support small cells (Femtocells) to enable small access points deployed in customer premises (on a campus or at home) for access to operator network, Internet and local services. Such deployments extend and improve the indoor coverage for more capacity, better voice quality and better support for enterprise mobility, which enable the mobile operators to provide higher user-experienced data rate, new services, and to improve the user experience.
The 6G system is expected to support this type of access and to provide means to support the Femtocells for local deployments by facilitating the RAN nodes to operate in higher frequency bands (e.g. in mmWave and/or cmWave).
5.9.1.2 Existing features partly or fully covering the use case functionality
The service requirements are defined in:
- TS 22.220 [259]
There are features already defined in other 3GPP WGs that reflect 5G NR Femtocell support in 5G:
- SA2 in TS 23.501 [140] clause 5.50.
- RAN4 in TS 38.104 [203] clauses 4.3.3 and 4.4.
- RAN3 in TR 38.799 [204], clauses 5.4 and 6.2.
Therefore, it is proposed to introduce a requirement to enable the network to support small-sized cells.
5.9.1.3 Potential New Requirements
[PR 5.9.1.3-1] Subject to operators' policy, the 6G system shall provide a mechanism for the support of Femtocell deployment.
5.9.2 Efficient data collection and consumption for 6G system
5.9.2.1 Description
To better serve the users and manage the network, as well as provide non-connectivity service (e.g. Location Services (LCS)), 3GPP system needs to conduct collection of data associated with the network and services provided by the 3GPP system. The data collection in the 5GA network for network/service management and non-connectivity service is defined for specific use cases, resulting in varying solutions across different use cases, different layers and different domains. Here is a brief summary of different data collection work done in RAN/SA2/SA4 and corresponding security mechanism in SA3:
- In RAN domain, data collection from UE is based on MDT (Minimization of drive testing) method as defined in TS 37.320 [237] and TS 38.331 [238]. The user consent of UE location data collection for MDT is defined in TS 32.422 [239].
- For data collection for AI/ML in core network, the data collection feature permits the Network Data Analytics Function (NWDAF) to retrieve data from various sources (e.g. NF such as AMF, SMF, PCF, NSACF, GMLC, and Application Function (AF), OAM, etc.), as a basis of the computation of network analytics as defined in TS 23.288 [114]. The operator can leverage these network analytics to optimize the management of network operations. For instance, as outlined in clause 6.22 of TS 23.288 [114], the operator can collect network data (such as OAM data and CP signalling) to detect or predict a signalling storm and use the analytics to mitigate or prevent its impact. The data collection is performed via SBI between CN NFs, DCCF (Data Collection Coordination Function) is introduced as logical functionality to coordinate data collection in CN, and Analytics Data Repository Function (ADRF) is defined to store the collected data. The user consent of UE data collection for AI/ML is defined in TS 33.501 [250].
- For UE positioning, the input data is collected from UE/RAN to CN via CP/UP path as the basis of the computation of UE location as defined in TS 23.273 [240]. The privacy check of UE location collection is also defined in TS 23.273 [240].
- To collect data and to expose data between CN domain and AF domain, the network exposure mechanism in core network has been defined in clause 5.20, TS 23.501 [140] and clause 4.15, TS 23.502 [30], where the Network Exposure Function (NEF) is used as the termination node to isolate both sides. Especially for edge, exposure of UE data is defined in TS 33.558 [241].
- If the data collection is triggered by an over the top (OTT) server in application layer from UE, the UP path may be used with the assistance of DCAF, as defined in TS 26.531 [242].
The aforementioned mechanisms are defined for different network functions to support data collection in different scenarios, leading to high standardization overhead. There is lack of coordination between these network functions, leading to isolated data collection and duplicated data collection. Meanwhile, the data transmission based on the control plane are not suitable for transmitting large amounts of data, leading to inefficient data transmission. Therefore, the data collection and control mechanism need to be improved in 6G.
Table 5.9.2.1-1 lists several potential use cases for data collection in 6G, and illustrates the corresponding data type, data provider, data consumer and data volumes for each use case. Take AI/ML data as example, there is a large amount of data types defined in 3GPP apart from those non-standardized ones, and the scale of data volume is usually large (from 10k to 100M).
Table 5.9.2.1-1: Heterogeneous 6G System Data
Use case category
Data types
Data provider
Data Consumer
Data volume
Sensing data collection/sensing result exposure
(e.g. measurement)
Sensing measurement data requires further study and discussion. The potential sensing measurement can be channel estimation results, delay, Doppler, angle, signal strength, etc.
UE, RAN
Sensing Function
Related to use cases: e.g. data rate of measurement is about 10Mbps for objects creating hazards on roads
AI/ML data collection / analytics exposure
(e.g. AI/ML for air interface, AI/ML for core network)
- For RAN data collection for Air interface, see TR 38.843 [243]
- For core network data collection, see the data collection framework in clause 6.2, TS 23.288 [114], and the detailed data type are defined from clause 6.3 to clause 6.21, TS 23.288 [114].
CN, RAN, UE, AF
CN, RAN, UE
Typically, the internal data collection for AI model training yields tens of thousands of samples.
Related to AI model: e.g. the data volume is about 10k bytes to 100M bytes for AI model of beam management
Energy Efficiency (EE information exposure to AF)
still under discussion in SA2 R19 EnergySys WID, see SP-250069.
CN, OAM
EIF (Energy Information Function)
Related to use cases: e.g. could be similar to data volume for charging
Positioning,
See clause 6, TS 23.273 [240]
UE, RAN
LMF, UE
Related to use cases
Network Exposure in core network
See clause 5. 20, TS 23.501 [140], and clause 4.15, TS 23.502 [30]
5GC NF, AF
5GC NF, AF
Related to use cases
…
…
…
…
All the above use cases for data collection and control share the following commonality:
The data collection and control mechanisms need to support procedures such as data collection, data storage, data exposure and data deletion.
The collected data could be requested or consumed by multiple data consumers within 3GPP system to support multiple services; for example, a single spatiotemporal environmental dataset can be concurrently utilized by multiple services, including IoT-enabled pervasive sensing service, latency-sensitive XR applications requiring <10ms motion-to-photon delays, and the Intelligent Transportation System (ITS) with centimetre-level geolocation precision.
The upper bond of the collected data volume is very high, e.g. on the order of 100 M byte.
Latency requirements for data collection and transmission exhibit variations across diverse use cases.
The collected data transmission path between different entities within 3GPP system can vary widely, encompassing scenarios such as UE to RAN node，UE to CN node, RAN node to CN node, and CN node to CN node.
All these use cases that involve collecting UE related data need to support security, privacy protection and user consent. In addition, security requirements need to consider different data types.
All these use cases that involve data exposure need to be subject to operator policy and user consent.
Figure 5.9.2.1-1: Use case specific data mechanisms in 5G common data mechanism in 6G
As shown in Figure 5.9.2.1-1, use case specific mechanisms are defined for each service scenario in 5G, leading to system complexity and inefficient data collection. As 6G networks integrate advanced capabilities progressively, such as AI, enhanced computing power, multi-dimensional sensing and digital twin network, they will need to process massive amounts of multi-source, heterogeneous data (from diverse formats and data resource) generated by 6G system. Consequently, 6G system needs to support efficient data collection and control mechanism capable of addressing both universal requirements and scenario-specific demands across heterogeneous use cases such as AI/ML, sensing, XR service and digital twin network. The efficient data collection and control mechanism needs to support various data providers and consumers, which maintains service-agnostic characteristics while embedding extensibility for emerging service.
In conclusion, the 6G system needs to support efficient data collection and control mechanism, which includes the following characteristics:
- Common data framework that can serve the need of multiple use cases, to improve data collection efficiency and avoid duplication data collection
- Support data collection from various data providers and data exposure to various data consumers
- Support user consent for data collection; and
- Support data exposure subject to operator policy and user consent.
5.9.2.2 Potential New Requirements
Editor’s Note: user consent in this section is FFS.
[PR 5.9.2.2-1] The 6G system shall support mechanisms for 6G System Data collection and consumption minimizing the impact to 6G services.
[PR 5.9.2.2-2] Subject to operator’s policy, regulation and user consent, the 6G system shall support processing of 6G System Data.
NOTE: Examples of data processing are use case dependent, e.g. data fusion, data anonymization and data analysis.
[PR 5.9.2.2-3] Subject to user consent, regulation and operator's policy, the 6G system shall support secure means to expose 6G System Data to authorized trusted third-party, authorized network function or authorized UE.
[PR 5.9.2.2-4] The 6G system shall support security and privacy protection of the 6G System Data.
[PR 5.9.2.2-5] The 6G system shall be able to provide charging and accounting mechanisms for 6G System Data.
[PR 5.9.2.2-6] Subject to regulation, user consent and operator policy, the 6G system shall support storage and retrieval of 6G System Data.
[PR 5.9.2.2-7] Subject to regulation and operator policy, the 6G system shall support transfer 6G System Data between different data providers and data consumers within the 6G system.
5.9.3 Use case on network digital twin in the 6G network
5.9.3.1 Description
Network Digital Twin (NDT) are digital virtual representations of physical networks, as an environment potentially enabling AI/ML- for delivering better services to users of 6G wireless networks [21[[SUGGESTION_START]]4[[SUGGESTION_END]]]. However, realizing the full capabilities of NDTs in 6G requires overcoming several design challenges, particularly in areas like data management, modelling, and defining new interfaces.
Digital twin technology is set to become prevalent across all industries. More and more, products and systems will first be simulated in a virtual environment before being produced in the physical world, with a digital twin being developed in the process. Additionally, in the future, all moving machines are expected to operate autonomously, and they will undergo training and learning within virtual environments. For 6G networks and their operations, NDT will play a key role in Autonomous Networks supporting a wide range of use cases such as network simulation and planning, network optimization and management and others.
The term Autonomous Networks describes the telecom system (including management system and network) capabilities that can be self-operating with minimal to no human intervention [216]. One of the trends of IMT-2030 as described in [27], also indicates the autonomous management of networks by AI/ML to achieve performing self-monitoring, self-organization, self-optimization and self-healing without human intervention. But one of the big challenges of introducing AI/ML in 6G network is that AI/ML models are regarded as black-box, it is very hard to ensure the reliability of AI-based optimization solutions [217].
Currently, many articles have proposed the innovation and research direction of the combination of digital twin and 6G network. For example, [218] proposes an architectural framework for 6G system enabled by digital twin to support Internet of Everything (IoE) applications such as human-computer interaction, AR and XR which will be running in 6G network.
To accommodate the increasing diversity of network services and the dynamic characteristic of both service demands and environmental conditions, the network should aware the real time service state and the capability for adaptive adjustment and prompt response based on QoE. This dynamic adaptation mechanism faces two key challenges: First, the rapid variability of service types and network environments often renders predefined optimization strategies ineffective. Second, while 6G introduces AI technologies to intelligently generate and optimize network policies, their 'black box' characteristic makes it difficult to evaluate and ensure the reliability and rationality of these policies, potentially leading to unforeseen negative impacts. Particularly in scenarios of network congestion or emergencies, maintaining service quality for high-priority users while preventing complete service unavailability for low-priority users becomes a critical challenge in dynamic resource allocation.
By introducing the capabilities of NDTs, two critical aspects can be enabled for network optimization. First, the service awareness achieved by digital twin to provide comprehensive network traffic restoration. This enables sufficient QoE evaluation and analysis to support better service offering. Second, the simulation-based validation via digital twin enables solution (e.g. adjusting user QoS policies) to be tested before real-network deployment, achieving full closed-loop network autonomy.
Additionally, based on prior scenarios, the operator can prepare an intent and provide it to NDT and NDT can deduce optimal configurations to satisfy the intent.
NGMN has also identified Digital Twin as one of the keys enabling services for 6G [219].
5.9.3.2 Pre-conditions
As Figure 5.9.3.3-1 shows, Network Operator ‘VV’ provides the 6G network services. And it is ready to provide different kinds of services (e.g. voice call, video, live streaming, XR etc.) for different users (e.g. in a moving vehicle, VIP user, etc.). Based on different QoS requirements of different services and different user QoE promise, Network Operator ‘VV’ provides high-quality communication services to its subscribers.
The ‘VV’ 6G network has network digital twin capability. A network digital twin instance can be created dynamically based on the network requirements. Whenever a network digital twin instance is created, the virtual-physical mapping between the network digital twin and the physical network is also established.
There are two subscribers, UE A and UE B, who have accessed to ‘VV’ 6G network. UE A is on a business trip, and in a moving car. UE A is holding a video conference with high quality using his mobile phone to introduce the latest progress of the trip to his colleagues. UE A is also a VIP user of ‘VV’ 6G network.
UE B is a user who has purchased an assurance package of on-line game and is experiencing the game on a mobile phone right now.
5.9.3.3 Service Flows
NOTE: This figure is for illustration only, no intention to illustrate all data in physical network need to be sync with NDT.
Figure 5.9.3.3-1: User experience optimization based on network digital twin
Operator VV's network is mirrored by a NDT that continuously receives synchronized data at proper granularity from the 6G Core. This allows the NDT to replicate and assess network performance metrics, such as the status of certain Network Function (NF) and Network Element (NE) efficiencies, as well as congestion patterns. The NDT in the core network employs AI/ML-based algorithms for “what-if” analyses and performance assurance.
As depicted in the figure above, there will be an event in the stadium. The management officer of the stadium reported the event to Operator ‘VV’ several days ago and hoped that Operator ‘VV’ could guarantee the live broadcast of the event and the audience's experience. The Operator ‘VV’ prepares a contingency plan (e.g. the plan could be dynamically adjusting QoS policies for different users around the stadium or limiting the maximum bandwidth of a specific service) for the communication assurance of the stadium in advance. The plan is deduced by using an NDT (i.e. a network digital twin instance is created to simulate and predict the network behaviour and users QoE when the game takes place).
UEs A and B happen to pass by the stadium where the football match is being played. There are many audiences (UE C) in the stadium and many fans outside the stadium who have not bought tickets. Operator ‘VV’ provides the mobile network services for the stadium. As the game progressed, audience inside and outside the stadium began to use mobile phones to share the wonderful moments of the game with their families and friends. The network around the stadium begins to become congested. As UE A and UE B get closer to the stadium, they also encounter network quality deterioration, e.g. video freezing, long game delay etc.
Because of uncertain factors, like UE A and UE B happen to pass by the stadium, their service experience also needs to be guaranteed by the network. Even with a plan, it is difficult to verify whether the optimization solution can take effect or not. The network needs to be dynamically adjusted to provide better services for users. Therefore, before implementing the pre-defined solution in the real network, Operator ‘VV’ core network implements the solution first to the Network digital twin environment (i.e. a network digital twin instance is created to simulate the current network around the stadium, which includes network traffic information (e.g. QoS) of different services, user location, user experience information around the stadium and etc.). The Network digital twin leverages AI/ML-based prediction and decision algorithms to generate adjustment strategies and then verify the feasibility of this solution.
After verification, the network digital twin feedback potential consequence to Operator ‘VV’ core network. For example, the Network digital twin could report that after applying the adjustment solution, although the experience of the audience inside and outside the stadium is improved, the experience of UE A will be even worse, and the game on UE B’s mobile phone may be interrupted.
With the integrated AI and Network digital twin capabilities, a new solution is generated via multiple iterations. Finally, after several rounds of iterations, the final optimization solution can meet the communication requirements of most of the subscribers, including users inside and outside the stadium (UE C), UE A (e.g. the Video quality may be degraded, but the conference experience is not affected) and UE B (e.g. the game is running well). After the final solution is determined, Operator ‘VV’ core network implements it on the real network.
The entire process, including data collecting, network digital twin creation, verification and implementation can be completed within a short period of time (e.g. 1 or 2 minutes), preventing user experience from being deteriorated for a long time.
Also, in the event of service disturbance, such as congestion or a large-scale outage for important events, the network transmits the relevant data to the NDT in real time. The NDT replicates the problem and with the help of AI in the 6G core network performs analysis, analyse the results, fine tune the proposed solutions iteratively and provide optimal solution via a feedback loop to the actual network to solve the problem.
Based on prior experience in step 7, a network operator specifies an intent for the 6G system for future, the network responds by performing necessary configurations and optimizations, subsequently relaying the results to the NDT. The AI/ML framework within the NDT then further analyses and fine-tunes these configurations based on AI/ML-driven insights. The refined configuration is fed back into the actual network, ensuring optimal performance aligned with the operator’s intent.
5.9.3.4 Post-conditions
The 6G network operates at optimal performance with minimized downtime, aligned to user-defined intents through real time feedback from the NDT, enabling proactive issue resolution and adaptive configuration. It also enables Operator ‘VV’ network to automatically verify and optimize the network adjustment solution and dynamically resolve the user’s QoE problem in an agile manner.
5.9.3.5 Existing features partly or fully covering the use case functionality
SA5 studies, as documented in TR 28.915 [77], the management aspects when introducing Network Digital Twin. Other key aspects are still open, such as how digital twin enables the autonomous networks and its impact on the network structure (e.g. data synchronization between network digital twin and the physical network).
How to support network simulation and verification on service experience optimization by using NDT is not involved in this study.
Currently, SA2 (e.g. NWDAF) has specified AI related capabilities, such as network performance analysis. However, it lacks end-to-end service experience visibility to identify root causes of QoE issues. Network digital twins solve this by correlating spatiotemporal network data to reconstruct complete service trajectories, enabling QoE diagnosis for better optimization. Furthermore, 6G needs to establish self-optimization mechanisms, current AI-generated network policies lack validation, risking reliability. Digital twins provide a verification environment to test policies (e.g. QoS adjustments) in simulated real-world conditions before deployment, a critical capability requiring standardization in 6G.
5.9.3.6 Potential New Requirements needed to support the use case
NOTE 1: These requirements only apply if the 6G network supports a Network Digital Twin:
[PR 5.9.3.6-1] The 6G system shall support the digital twin in the 6G network to enable autonomous networks (including network and OAM) for better service offering, e.g. through simulating in the network characteristics and behaviours provide services to users (e.g. user traffic running on the virtual replica of mobile network or part of it).
NOTE 2: The network characteristics can be NFs configuration, UE throughput in NFs, the number of current subscribers, UE QoS, user experience data, fault prediction and non 3GPP data such as user complaint data etc.
[PR 5.9.3.6-2] The 6G system shall expose an API for the 6G network to transfer network function related information (e.g. NF configuration, UE context related data such as UE QoS etc.) to NDT to replicate 6G network in NDT.
[PR 5.9.3.6-3] The 6G system shall expose an API for the 6G network to receive feedback (e.g. optimal QoS parameter values, NF configuration etc.) from NDT.
[PR 5.9.3.6-4] Subject to operator’s policy, the 6G system shall be able to transfer the data (e.g. related to service outage) from 6G Network to NDT.
5.9.4 Network simplification on 6G system
5.9.4.1 Description
The 5G system supports various features to accommodate a wide range of use cases. The increased number of features is characterized by flexibility and programmability, such as diverse mobility management. This increase may lead to operational challenges, further introducing complexity in system management and possibly causing delays in identifying the root causes during network failures.
A 6G system is expected to improve resilience by reducing operational workloads on the network and enabling faster recovery during network failures by reducing complexity compared to the 5G system. In reducing complexity, it is necessary to consider essential requirements for mobile communication from users. By prioritizing data session services, enhanced user experience can be provided to customers.
5.9.4.2 Potential New Requirements
[PR 5.9.4.2-1] The 6G system shall be able to support means to simplify network operation and service delivery compared to 5G system, e.g. in order to reduce signalling, minimise connection setup time.
NOTE: This simplification should not mean reducing service quality provided.
5.9.5 Use case on network simplification for rolling out new services
5.9.5.1 Description
After years of planning, 5G became a reality in 2019, and as of January 2024, globally 261 operators had launched commercial 5G mobile services in 101 countries [220]. Instead of focusing only on justifying the 5G business case or timing the network launch, many operators are paying more attention to issues such as finding ways to fine-tune network deployments, monetising existing, and planning new use cases, while optimising network costs. Operators are gaining precious lessons learnt not only on optimising the cost of 5G deployment but also on identifying the essential enhancements to the design of the beyond 5G Stand-Alone (SA) networks, among which is the costly and rigid rollout of new services on top of the existing 5G SA networks. The main drawbacks include:
1) slow deployment of service innovations since it is difficult to verify the end-users’ experience during network upgrade;
2) hard to achieve service-lossless network update due to lack of effective isolation methods, e.g. for enterprise customers sharing network resources virtually;
3) network adjustment (NF scaling in/out) for new services is difficult across network functions from multiple vendors when these network functions share the same hardware resources.
Built on multiple technology layers, domains, protocols, operational silos, and mixture of standard and some proprietary components that have been stacked over years or decades, mobile networks became overly complex, resulting in inefficient use of valuable capital and operational resources. According to GSMA Intelligence research [255] [256], "the increasing service complexity" and "the increasing network complexity" are among the top challenges of network evolution for operators. Introducing new network features can take weeks or even months to go live. Network simplification via leveraging innovations in technologies along with novel network architectures can deliver breakthrough long-term benefits and enable operators to continue supporting exponential traffic growth and emerging demands for service agility while reducing the cost of services and power. Compared with the previous generations (mainly providing mobile connectivity services), 6G is expected to be even more versatile, which is projected to provide more advanced services, such as sensing services, AI related services and computing related services. It becomes even more crucial to enable agile and zero-downtime rollout of new services via network simplification.
5.9.5.2 Pre-conditions
A 3GPP network with a modularized function design is already deployed to provide Internet services and Calling services.
The MNO receives new business contracts to support AR/Virtual Reality (VR) applications; and decides to deliver the new XR service to the users in a progressive manner to assure rapid service delivery with minimal impacts on the existing services.
5.9.5.3 Service Flows
1. A new network instance is created for the initial rollout of the new XR service, where the functionalities from multiple vendors to support the new service are deployed on top of the current version of the 3GPP network.
2. 5% of the users are chosen (randomly selected or a specific group of users) for XR service test running, while the rest of the users are not affected. The user data (context data, subscription data, policy data etc.) of these selected users in the current network are synchronized in the related functionalities in the new network instance, thus to enable seamless shift of the traffic of these selected users from the current network to the new network instance. Now in addition to the Internet services and Calling services, the selected users can enjoy the XR service to access the AR/VR applications. The test running continues for a couple of days, which can work in many ways, e.g. running some automated tests, performing manual testing, or even keeping the server running to see if any problems are encountered by the end users.
3. If test running is successful, i.e. the new network instance reports no error for that 5% users, more users (25%, 50%, 75%, 100%) are gradually shifted to the new network instance for the new XR service. If any problem occurs during test running, the selected users are shifted right back to the current network without interruption of the ongoing Internet services and Calling services, while the rest of the users are not even aware of any problems.
4. As confidence increases, the new network instance is scaled up and more users are allowed to enjoy the new XR service. Eventually, all live traffic goes to the new network instance, and thus the new network instance becomes the new version of the 3GPP network and the old version of the 3GPP network is released.
5.9.5.4 Post-conditions
The ongoing Internet and Calling services (apart from XR services) are running with minimal impact from this progressive deployment of the XR service, and the 3GPP network is now further able to deliver the new XR service to the users.
5.9.5.5 Existing features partly or fully covering the use case functionality
Typically, operators release updates during off-peak time — when users are not active — to minimize disruptions. There have been 3GPP efforts mainly in SA5 to improve the network operation efficiency, with which the result cannot meet the growing expectation and demand from operators and customers (both end consumers and business customers). This is mainly due to the reason that release updates have impacts on many existing NFs. Most of the NFs are not decoupled in the functional design, for example, to deploy the positioning feature on top of a basic 5G network deployment, five existing NFs including AMF, NRF, NEF, UDM and UDR need to be upgraded. Today, mobile users and network operators want services available 24/7 in all time zones, which makes it challenging to find convenient time for network update. Therefore, how to assure rapid service delivery with minimal impacts on the existing services is one of 6G challenges to be addressed from not only OAM aspect but also architecture aspects.
5.9.5.6 Potential New Requirements needed to support the use case
[PR 5.9.5.6-1] The 6G system shall support on-demand rollout (e.g. within hours) of new or updated services/capabilities with minimal disruption to existing services, including the ability to efficiently rollback those services/capabilities, as needed (e.g. in case of failures or demand from other services).
[PR 5.9.5.6-2] The 6G network shall provide means to minimise the impact to the user experience during the rollout and rollback (if needed) of new and updated services/capabilities.
5.9.6 Use case on 6G Local Area Networks
5.9.6.1 Description
Local mobile communication networks have emerged as a viable and attractive deployment model for vertical scenarios like factories or campuses since the 4G era. Characterized by scenario-specific requirements, limited coverage and well-defined user groups, these local networks have empowered diverse local stakeholders to build and manage their customized networks - whether private or public - for distinct business needs [210]. The 5G era continues the exploration of expanding vertical markets beyond consumers for new revenue through more standardized technical enablers such as network slicing, NPN, 5G-LAN type services. However, the current realization remains constrained by a holistic “one-system-fits-all” philosophy, which gradually leads to an increase in the complexity of 5G systems. Thus, it is difficult for the network tenders e.g. private service providers to select the best option and also impossible for new and smaller players to bring in their innovations [211]. The mismatch between technical complexity (coupled with high investment and operational cost) and fragmented demands of diverse verticals has caused 5G local mobile communication networks to lag behind initial market expectations. The 6G system is expected to address the above issues through simplifying network, natively supporting deployment options with smaller granularity and modular solution tailored for vertical use cases.
6G, envisioned as a general-purpose technology platform integrating communication with sensing, positioning, AI, and computing capabilities via multiple accesses, will unlock a vast array of new use cases as mentioned in [39]. The transition from dedicated communication systems to versatile and intelligent infrastructure will fundamentally reshape the business models and associated design paradigms of local area networks in,
- Business value evolution from connectivity to capabilities as service offerings: for example, besides the connectivity, the local area networks are promising to play an important role in AI-driven application (e.g. collaborative robots of smart factories, intelligent hospital) such as providing on-demand computing and sensing capabilities for edge AI, or coordination of the communication and computing for edge-cloud collaborative AI. Thus, those local area networks need to be able to close to the end devices as much as possible for optimized latency and relatively better security protection in some cases while it is able to support interoperability with remote devices or platform in some cases.
- Growing divergence in the demands of local stakeholders: 6G communication performance requirements will become increasingly diverse across the scenarios like immersive communication, ubiquitous connectivity, massive communication and intelligent application in each dimension such as data rate, latency, reliability, availability, etc. The integration of non-connectivity capabilities such as sensing, computing, AI will gradually unlock new use cases and further accelerate the diversification of their service expectations. Thus, the differentiated and changing demands on network services of various local stakeholders will intensify challenges in network complexity and flexibility, which must be addressed in the early stage of system design to enable the customized, adaptable and agile deployment with optimized cost.
This use case illustrates how 6G local area networks serve corresponding local users with simplified and flexible deployment.
5.9.6.2 Pre-conditions
Network Operator OpX has deployed a PLMN in country A and supports the subscribers to access the PLMN via multiple accesses such as terrestrial access, satellite access as Figure 5.9.6.3-1 shows.
OpX is also capable of offering customized LAN service, therefore,
- The Manufacturer MfX has signed SLA with OpX to deploy private local area network (e.g. LAN#1) for communication service, AI services and computing services.
- The event organizer has signed SLA with OpX to deploy local area network (e.g. LAN#2) during the event period. The offered network services need to be activated on demand.
- The dock operator has signed SLA with OpX to deploy public local area network (e.g. LAN#3) for basic communication services (e.g. voice, limited-data-rate data) and sensing services. All the users entering this area can get network services with the authorization of the dock operator.
Alice and Bob have subscriptions with OpX for communication services.
5.9.6.3 Service Flows
Figure 5.9.6.3-1: Differentiated 6G Local Area Network
Scenario1: Private local area network in cost-efficient way
Regarding the request of smart manufacture plant, OpX deploys a local area network (LAN#1) with dedicated functionalities to support low latency communications and edge AI services in cost-efficient way.
The Autonomous Mobile Robots (AMRs) and industrial robots can execute various tasks in form of groups under the monitoring of central control platform. The collaborative robots can be authorized to communicate within individual groups but not allowed to communicate to the members of other groups without access permission. When a new production line is introduced with strict requirements on deterministic latency, LAN#1 will autonomously initiate the upgrading with provisioning new functionalities and/or policies once the robot of new production line accesses for the service or based on OAM’s request.
Scenario2: Temporary local area network
A sports event will be held in the local stadium, which has a capacity of more than 50,000 audiences. At the moment, there will be around hundreds of thousands of audience watching the live remotely via VR/AR devices, small screens (e.g. smartphones) or large screens. The entire event will be digitally operated and managed– from monitoring athletes’ conditions to collecting and disseminating event information.
OpX deploys a local area network (LAN#2) based on existing base stations and computing resources of PLMN and non-3GPP access with configured validity period (e.g. duration of the event). The staff were authorized to interact with each other within individual work groups via LAN#2 while the audiences (e.g. Alice) within the stadium were authorized to connect to LAN#2 to get latest event information, watch holographic presence and communicate with remote friends (e.g. Bob). The users outside the stadium can be only allowed to access PLMN via the base stations as usual, but cannot access LAN#2. Meanwhile, the remote audience (e.g. Bob) enjoy the live in immersive experience as if they attend the event in person. When the event finishes, the network resources of LAN#2 will be automatically returned to PLMN without any impact on other services.
Scenario3: Service Continuity between PLMN and local area networks
OpX deploys a local area network (LAN#3) on cargo dock with dedicated functionalities for high reliable communication, massive IoT communication and 3GPP sensing service. When a truck is authorized to enter this area for landing with goods, it is also authorized to access LAN#3 to report the information to the local authority platform as well as remote application platform simultaneously. When it leaves this area, the truck is not allowed to access LAN#3 so continue reporting sensor data to remote application platform through PLMN.
Bob, the fleet manager on truck is authorized to answer the voice call from Alice via LAN#3 during the truck is unloading the goods. Meanwhile, based on the dock operator’s policies, his UE is not allowed to connect to the live of the sports event via LAN#3 so still thorough PLMN during the truck enters in and out of this area.
The stadium mentioned in Scenario2 is not far from the dock. When the truck finishes the unloading, Bob is navigated to the stadium following Alice’s instruction in the ongoing voice call via PLMN until meet each other in the stadium and watches the rest of the sport event in live via LAN#2.
5.9.6.4 Post-conditions
Thanks to 6G local area networks, the smart manufacture plant and the cargo dock can operate efficiently in an orderly manner while the staff and the audiences relevant to the sports events are happy with network services.
Alice and Bob can communicate successfully via local area network and PLMN.
5.9.6.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] introduces several features to serve the diverse needs of regional/local area networks, such as
(1) 5G LAN
TS 22.261 [14] has defined 5G LAN-type service and 5G LAN-virtual network as below,
5G LAN-type service: a service over the 5G system offering private communication using IP and/or non-IP type communications.
5G LAN-virtual network: a virtual network capable of supporting 5G LAN-type service.
And clause 6.26.1 describes the characteristics of 5G LAN as
There are multiple market segments in the realm of residential, office, enterprise and factory, where 5G will need to provide services with similar functionalities to Local Area Networks (LANs) and VPN’s but improved with 5G capabilities (e.g. high performance, long distance access, mobility and security).
5G LAN-VN can be scaled up/down, created/removed and managed by the network operator on demand. Also, the UEs of 5G LAN-VN can be dynamically added or removed.
However, 5G LAN-VNs heavily rely on the robustness and resilience of PLMN infrastructure. Any failure or disruption of PLMN could have a widespread impact on the users of local area network - particularly those with stringent requirements for service isolation and data security such as industrial enterprises. Moreover, the sharing functionalities hinder the potential simplification by some-level customization or function tailor.
(2) Network Slice
TS 22.261 [14] has defined network slice as below,
network slice: a set of network functions and corresponding resources necessary to provide the required telecommunication services and network capabilities.
And clause 6.1.1 describes the characteristics of network slice as
Network slicing allows the operator to provide customised networks.
A network slice can provide the functionality of a complete network, including radio access network functions, core network functions (e.g. potentially from different vendors) and IMS functions. One network can support one or several network slices.
However, a network slice is a logical network that will share the similar infrastructure and network functionalities with other network slices. Once an instance of network slice is created and deployed, the offered capabilities and services cannot be changed. Also, the granularity of network slice is still too big for smaller operators to deploy for limited service demands of specific vertical needs.
(3) NPN
TS 22.261 [14] has defined non-public network as below,
non-public network: a network that is intended for non-public use.
And clause 6.25.1 indicates that:
Non-public networks are intended for the sole use of a private entity such as an enterprise, and can be deployed in a variety of configurations, utilising both virtual and physical elements. Specifically, they can be deployed as completely standalone networks, they can be hosted by a PLMN, or they can be offered as a slice of a PLMN.
Standalone NPN (SNPN) can provide specific services and good security to dedicated users via customized functionalities and physical isolation. But when SNPN users intend to access a PLMN or other SNPNs, they still face challenges in the authentication (using the isolated credentials) and service continuity (due to inconsistent policies, QoS), which has not been well solved in 5G.
5.9.6.6 Potential New Requirements needed to support the use case
[PR 5.9.6.6-1] Subject to operator’s policy and agreement with 3rd party, the 6G network shall support a mechanism to start and stop offering certain network service(s) in a local area network adapting to the demand of e.g. the users, 3rd party or the network operator.
[PR 5.9.6.6-2] Subject to operator policies, and agreement between the PLMN operator and authorized 3rd party, the 6G network shall support a mechanism to
- authorize PLMN’s users to access a subscribed service provided by an authorized 3rd party via a local area network (deployed by the PLMN operator), and
- minimize service interruption when the serving network changes between the local area network and the PLMN network.
5.9.7 Use case on flexible traffic routing in 6G
5.9.7.1 Description
Some operators can provide nationwide services in a country. And in the country, taking into account the vast territory of some country, the operator always have the provincial basis or regional basis management of network, and it may cause that the operator may deploy multiple core network in different regions, see example in Figure 5.9.7.1-1 below. And in order to connect each core network together to support mobility, each core network in each region shall connect to the national backbone network (can also say national transmission network). But this may cause the issue of intra PLMN roaming, that when UE moves from a region of PLMN that UE registers into, moves to another region of the same PLMN that is different to the location that UE registers into, the UE still receives service from the PLMN of the same country as that of the PLMN.
Figure 5.9.7.1-1: Example of provincial basis or regional basis deployment of core network
Clarification of intra PLMN roaming:
The user sometimes may have subscription with operator in one region, which can be seen as the core network that UE registers into. But if the UE moves to another region, which is different from the location that UE registers into (sometime a different province), this may cause intra roaming issues. When the UE moves to the new coverage of another core network of the same PLMN that is different from the location of UE registers into, this may cause the intra PLMN roaming or inter region roaming internal PLMN.
For intra PLMN roaming, there exists some inconvenience of traffic rule configuration and application traffic routing:
- For the current UE, the UE doesn’t support Service and Session Continuity (SSC) mode 3, as indicated in clause 5.6.9 of TS 23.501 [140]. And if the UE performs intra PLMN roaming with power on status, although the UE connects to the core network in roaming location but the UP anchor still remains unchanged in the core network that UE registers into. So, in order to configure the UE in new location, frequently interaction between two core networks of different region is needed. The same case as that UE move back to registration region with UE power on status.
- For the UE that initially powers on in the location that roaming, the UE firstly connects to the core network in roaming place. But in order to support the local offloading in intra PLMN roaming area, still two core networks interactions are needed. It is the situation that to insert the new user plane that cannot controlled by the core network that UE registers into, like the ETSUN cases in clause 5.34 of TS 23.501 [140]. Still exists two core networks frequently interactions for configuration of traffic routing rules.
Here are some detailed descriptions of the traffic rule configuration and application traffic routing.
Firstly, for the current UE design, only SSC mode 1 is supported. The commercial maturity of SSC mode 3 is low. So, if the UE performs roaming to region B, the user plane anchor still in region A. It means the traffic to Internet service still needs to route back to region A and increase the inter-region traffic volume. See Figure 5.9.7.1-2 for details. In region A, the core network configures the routing rules to route the traffic to Internet via Gateway 1. But when UE moves into the scope of region B, due to the UE doesn’t support the SSC mode 3, the IP anchor still on Gateway 1 and the traffic to Internet should be routed back to Gateway 1. And the result is the UE cannot directly communicate with server via Gateway 2 using short routing in region B. So, the operator should have multiple traffic routing between Gateway 2 and Gateway 1, that brings traffic burden on backbone network.
Figure 5.9.7.1-2: Example of traffic routing back to home region
Secondly, due to the UE moves into region B and the IP anchor doesn’t change, if to route the traffic from UE to Internet locally, may have other configurations. But, always core network in region A doesn’t know the topology details in region B, so the two core networks may have multiple interactions. And considering that only the region B has the topology and routing information so it is not efficient that always the region A trigger the local traffic routing in intra PLMN roaming cases, as shown in Figure 5.9.7.1-3. And from deployment point of view, sometimes the local access configuration in different region is not the same.
Figure 5.9.7.1-3: Example of two core network interactions
At last, when UE moves into region B, the UE may still have the requirements to have two traffic routing, one that locally offload to Internet and another one is traffic to data network owned by enterprise in region A.
5.9.7.2 Pre-conditions
Alice purchases a SIM-card of operator X-Mobile in region A. So, Alice has the subscription with operator X-Mobile. And Alice works for Company XXX and Company XXX deploys the enterprise network in region A.
When Alice is in region A, the operator X-Mobile can route the traffic from Alice’s UE either to Internet or to the enterprise network.
Now, Alice plans to go to region B for sightseeing. But, Alice still needs to visit the enterprise network for some of the working. In region B, a museum now has an exhibition and the museum deploys a dedicated network in museum to provide guide service for tourists.
5.9.7.3 Service Flows
1. As shown in Figure 5.9.7.3-1, Alice takes the train from region A to region B for sightseeing. Alice uses her phone to watch a video on YouTube®. And when Alice moves into region B, intra PLMN roaming performs.
2. When Alice moves into region B, Alice is not aware of this roaming and Alice’s phone always keep power on. And the core network in region B finds a new UE of the operator X-Mobile moves into and also finds that this UE doesn’t have the subscription in region B. Due to Alice’s UE doesn’t support SSC mode 3, that the IP anchor is still in region A, so currently the video traffic is still be routed to the gateway in region A.
3. When region B aware that a new UE moves in, in order to improve the user experience and save the traffic not routing back, the region B can configure the network and support to route the application traffic to YouTube® server locally in region B.
Figure 5.9.7.3-1: Operator in region B configures the traffic from Alice’s phone local offloading to
Alice knows that in region B, there exists an excellent museum that has exhibition. So, tomorrow, Alice opens her phone and buys a museum ticket to see the exhibition. Alice uses her phone to connect to the network in museum and consumes the guide services that are provided by the museum such as XRM, holographic image.
Figure 5.9.7.3-2: UE in dedicated location of region B can be directly offloaded to certain network
As shown in Figure 5.9.7.3-2, the operator in region B directly routes the traffic from Alice’s phone to the network of museum without has any interaction with core network in region A, and Alice has a high UL/DL rate and has a good experience. During the procedure of Alice consuming the service provided by museum, the operator can perform the authentication and authorization to the phone, that to authorize the UE to consume the services.
Suddenly, Alice receives a call from her colleagues Bob that needs Alice to login the enterprise network to handle the work. Without any configuration on Alice’s UE, Alice starts the enterprise dedicated application on UE, and with the help of operator X-Mobile, Alice logins in successfully and handles the work.
Alice thoroughly enjoys the entire trip and then takes the train back to Region A. And when Alice roaming back to Region A, Alice is not aware of this roaming and also the phone keeps power on. The operators in Region A will consider routing the Internet traffic accessed by Alice's mobile phone through Gateway A to the Internet.
5.9.7.4 Post-conditions
Alice in Region B has a good user experience of OTT service that the traffic from OTT application can be routed to data network locally.
Alice can access the dedicated network of museum and enjoy the service such as XRM, holographic image provided by museum. The museum can also authorize and authenticate Alice’s phone to check whether Alice can consume the service or not.
In Region B, Alice can also login the enterprise network in Region A without any changes on configuration on Alice’ phone.
During all procedure of intra PLMN roaming, Alice doesn’t make any changes or configurations on her phone and also doesn’t aware of this intra roaming.
5.9.7.5 Existing features partly or fully covering the use case functionality
In TS 22.261[14], some of the requirements that related to roaming and traffic offloading may have some relationship with the service flow.
1) Roaming:
In clause 5.1.2.1 of TS 22.261 [14]:
The 5G system shall support a UE with a 5G subscription roaming into a 5G Visited Mobile Network which has a roaming agreement with the UE's 5G Home Mobile Network.
This requirement clarifies the roaming between HPLMN and VPLMN, not the roaming internal HPLMN.
2) Local offloading:
In clause 6.2.1 of TS 22.261 [14], it clarifies the mobility management:
With the ever-increasing multimedia broadband data volumes, it is also important to enable the offloading of IP traffic from the 5G network onto traditional IP routing networks via an IP anchor node close to the network edge. As the UE moves, changing the IP anchor node can be needed in order to reduce the traffic load in the system, reduce end-to-end latency and provide a better user experience.
Also, in clause 6.5.2 of TS 22.261 [14], it clarifies the efficient user plane:
Based on operator policy, the 5G network shall be able to support routing of data traffic between a UE attached to the network and an application in a Service Hosting Environment for specific services, modifying the path as needed when the UE moves during an active communication.
The 5G network shall be able to interact with applications in a Service Hosting Environment for efficient network resource utilization and offloading data traffic to the most suitable Service Hosting Environment, e.g. close to the UE's point of attachment to the access network or based on usage information.
NOTE: To accomplish offloading data traffic, usage information might be exposed to the Service Hosting Environment.
Based on operator policy, the 5G system shall provide a mechanism such that one type of traffic (from a specific application or service) to/from a UE can be offloaded close to the UE's point of attachment to the access network, while not impacting other traffic type to/from that same UE.
This requirement clarify the offloading data traffic to the most suitable Service Hosting Environment that in HPLMN. But for intra PLMN roaming situation, two core network belongs to two regions needs coordination. Because always the core network that UE roaming to has the topology of routing information, but now always the core network that UE registers into controls the UE. And this intra roaming cases are not considered in this requirement.
5.9.7.6 Potential New Requirements needed to support the use case
[PR 5.9.7.6-1] Subject to operator’s policy and regulation, for an operator with multiple 6G core networks, when there is UE mobility from one 6G core network to another 6G core network of the same PLMN, the 6G network shall support efficient traffic routing for the traffic from UE to data network.
NOTE 1: The above term does not imply any architectural assumption, e.g. whether 6G CN is a new or evolved CN (compared to 5G).
NOTE 2: This requirement only impacts core network.
5.9.8 Enhanced Network Service Awareness
5.9.8.1 Description
The 5G system provides capabilities (e.g. QoS, network slicing) that enable a network operator to offer differentiated services to their customers. These service awareness capabilities provide the network with the means to identify applications, services, or flows, service characteristics (e.g. latency sensitivity, bandwidth needs, security requirements), and the ability to dynamically adapt network settings to meet the needed/requested service requirements. This is a key capability to comply with SLAs.
6G services or applications (e.g. immersive communications or integrated sensing and communication services) may include multiple data streams (e.g. video, voice, text, sensor data, beyond communication services such as AI and sensing) that exhibit varied traffic patterns, and each component may have significantly different QoS requirements (e.g. latency, jitter, reliability, and throughput). The diversity and dynamism of such traffic patterns challenge traditional static or coarse-grained network management approaches.
Static slicing or coarse per-application (e.g. eMBB, URLLC) QoS management does not account for intra-application diversity, leading to suboptimal performance or over-provisioning of network resources. The 6G system needs to enrich handling intra-application diversity (e.g. 5G XRM’s multiple different media components) to provide expanded service awareness capabilities within the network to differentiate traffic types in a more granualar way and apply the most appropriate policies and resources according to the individual media component).
The 6G system is also expected to increase user security and privacy, and these requirements should not be viewed in opposition to the requirements for increased network capabilities (e.g. slicing, traffic steering, QoS mapping) intended to improve network optimization, such as enhanced service awareness across the network. New mechanisms may be needed to satisfy both the security/privacy requirements while simultaneously enhancing service awareness.
The use of AI/ML is expected to improve the 6G system’s ability to enhancing service awareness, enabling (near) real time detection of changes in service usage and assisting in proactive service assurance, dynamic handover optimizations, and spectrum reallocation based on detected service behaviour.
5.9.8.2 Potential New Requirements
[PR-5.9.8.2-1] Based on operator policy, the 6G network shall support the ability to allow an authorized 3rd party service provider to provide information of the service characteristics for each traffic flow component of its service/application to the 6G network.
[PR-5.9.8.2-2] Based on operator policy, the 6G network shall support mechanisms to dynamically adjust and optimize network resources based on the service characteristics, including their predicted changes, provided by the service or application.
[PR-5.9.8.2-3] The 6G system shall provide appropriate charging support for differentiated services per media component (e.g. to meet SLA requirements).
5.10 Device Support
5.10.1 Continued support for diverse UE types
5.10.1.1 Description
It is envisioned that a next generation system will continue to support a population of UEs with varying capabilities that would support different use cases. For example, simple UEs with limited capabilities would be supported by the 6G network, alongside more sophisticated UEs that support more advanced features and capabilities.
To illustrate this point further, simple UEs could have lower throughput, lower bandwidth, lower power consumption and lower processing power capabilities. Other more sophisticated UEs could have higher throughput, higher bandwidth, higher power consumption and higher processing power capabilities.
To better support the various UEs types that represent different market segments, the 6G system needs flexibility to optimally support these different UEs types.
While different UEs types already exist in 5G, it is essential that this requirement is included from the first release of 6G.
It is also important to highlight that while there is expected diversity in the types of UEs, each UE type should have at its core a common set of basic capabilities, to maximise the commonalities and thus reduce market fragmentation.
5.10.1.2 Existing features partly or fully covering the use case functionality
TS 22.261 [14] indicates in various clauses, the intent of a 5G system to support "diverse UEs and services" in the informative text, for example:
Introduction clause:
The need to support different kinds of UEs (e.g. for the Internet of Things (IoT)), …
Clause 6.2.1:
A key feature of 5G is support for UEs with different mobility management needs. 5G will support UEs with a range of mobility management needs …
Clause 6.4.1:
5G introduces the opportunity to design a system to be optimized for supporting diverse UEs and services.
Also, a number of requirements in TS 22.261 [14] mention "UE capabilities":
The 5G system shall support a mechanism for a UE to select and access network slice(s) based on UE capability, ongoing application, radio resources assigned to the slice, and policy (e.g., application preference).
The 5G system shall allow the operator to assign a UE to a network slice, to move a UE from one network slice to another, and to remove a UE from a network slice based on subscription, UE capabilities, the access technology being used by the UE, operator's policies and services provided by the network slice.
The 5G system shall support UEs with multiple radio and single radio capabilities.
There are features already defined in other WGs in 5G that reflect the diverse device support in 5G. However, there are no requirements defined in Stage 1 in 5G. Therefore, it is proposed that a requirement to enable the network to support different device types / capabilities is defined.
5.10.1.3 Potential New Requirements
[PR 5.10.1.3-1] The 6G system shall support UEs with different characteristics such as data rate, latency, etc.
Editor's Note: additional considerations on the above requirement is FFS.
5.10.2 Diversity of UEs for satellite access
5.10.2.1 Description
In the context of the 6G system with satellite access, the following is expected:
• enhanced connectivity to smart phones and IoT user equipment with enhanced performances compared to 5G (data rate, coverage, throughput, ..) in frequency bands below 7 GHz
• enhanced connectivity to vehicle/building mounted user equipment (i.e. with Flat Panel Antenna) in frequency bands above 10 GHz. Vehicles from the automotive, public safety, transport (aeronautic, railways, drone, maritime), utilities, agriculture and media & entertainment sectors are assumed.
In order to meet a variety of consumer, enterprise and vertical use cases, the 6G system with satellite access shall be able to provide connectivity to a wide range of user equipment and their usage conditions.
In TS 22.261 [14], the Table 7.4.2-1: Performance requirements for satellite access in clause 7.4 “KPIs for a 5G system with satellite access” defines performances associated to a set of UEs and usage conditions.
However, these performance requirements need to be updated in the context of 6G.
5.10.2.2 Potential New Requirements needed to support the use case
[PR 5.10.2.2-1]: The 6G system with satellite access shall support UEs with the following performances:
Table 5.10.2.2-1: Performance requirements for 6G satellite access
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
(note 1)
Area traffic capacity
(UL)
(note 1)
Overall user density
Activity factor
UE speed
UE type
Others
Pedestrian
[1] Mbit/s
[100] kbit/s
1,5 Mbit/s/km2
150 kbit/s/km2
[100]/km2
[1,5] %
Pedestrian
Handheld
-
Vehicular connectivity
[50] Mbit/s
[25] Mbit/s
TBD
TBD
TBD
50 %
Up to 250 km/h
Vehicle mounted
-
High speed train connectivity (note 2)
TBD Mbit/s
per wagon
TBD Mbit/s
per wagon
TBD
TBD
TBD
N/A
Up to [500] km/h
Train mounted
-
Large Vessel connectivity
TBD
TBD
TBD
TBD
TBD
TBD
Up to [50] km/h
Vessel mounted
-
Drone connectivity (note 2)
TBD Mbit/s
TBD Mbits
TBD
TBD
TBD
TBD
Up to [250] km/h
Drone mounted
-
NGSO Satellite connectivity
TBD
TBD
TBD
TBD
TBD
TBD
Up to [7] km/s
NGSO Satellite mounted
-
Note 1: Area capacity is averaged over a satellite beam.
Note 2: Assuming a UE with maximum size antenna size of 20 cm x 20 cm
Editor’s note: The table above will be filled with new scenarios compared to 5G and with legacy scenarios for which the performance values need to be updated. The Table 7.4.2-1: “Performance requirements for satellite access” in TS 22.261 [14] is considered as a basis.
6 AI
6.1 General
As described in ITU-R recommendations under in [27], AI is considered as a foundational pillar for 6G systems capable of providing services for intelligent applications, and serving the environmental, social and economic 6G drivers. In the context of AI-driven 6G systems, it is anticipated that two concepts will be important for shaping the future systems: AI for 6G System and 6G System for AI.
AI for 6G System refers to the use of AI capabilities to support the network and devices in providing 3GPP services.
6G System for AI focuses on how the system supports and enables AI applications by leveraging 6G system functionalities to provide different services.
Use cases under this subclause are aiming to identify the service requirements for these two concepts.
6.2 Use case on optimizing 6G infrastructure utilization via resource exposure in 6G
6.2.1 Description
With the advent of advanced technologies such as Generative Artificial Intelligence (Gen AI), Large Language Models (LLMs), and AI-accelerated xPUs, more applications based on AI can utilize wireless network of operators which are now equipped with substantial computational and storage infrastructures that often remain underutilized in the deployed edge data centres. These computation resources may be hosted at edge sites in a distributed manner, as illustrated in Figure 6.2.1-1. It is in the strategic interest of these operators to provide and expose availability of these dormant resources to a third-party, allowing them to leverage the computational power and AI capabilities when required. In exchange, operators can monetize these assets by charging nominal fees for offered computing resources (e.g. computing cycles) from the edge data centres at the 6G infrastructure. For instance, AI applications may demand extensive computing resources for processing the training data as well as for inferencing (for instance for task offloading, while performing LLM training or inference) and storage utilization by third parties.
This approach not only generates additional revenue streams for network operators but also ensures the optimal utilization of existing infrastructure. Consequently, it justifies the need for new requirements in 6G networks to expose and manage these resources effectively. By incorporating standardized interfaces, robust security protocols, and advanced resource management frameworks, 6G can facilitate efficient and secure access to these resources. This transformation would position 6G networks as comprehensive platforms for hosting in operator’s edge data centres distributed computing and AI services for third-parties, aligning with the evolving demands of the technology landscape and providing resources as a service. This unique network-aware resource exposure capability is enabled by the 6G system's centralized policy framework.
The 6G system will be built on the cloud, optimized for ubiquitous computing, and natively provide computing services (e.g. in forms of infrastructure services, platform services, and software services) [2].
NOTE: The figure above is just an illustration to depict where computation resources could be deployed e.g. at an edge site.
Figure 6.2.1-1: Illustration of edge data centre resource usage and exposure to 3rd party
6.2.2 Pre-conditions
Network Operator A is equipped with significant compute, storage, and AI processing resources in deployed edge data centres. Some of these resources are used by NFs, OAM, hosted services etc. However, resource utilization varies based on the actual utilization by the hosted applications, network load, and during periods of low demandoften at certain times of day or nightup to 40 % of these resources remain idle. These idle resources represent untapped potential, lying unused with no opportunity for additional utilization.
Meanwhile, Operator A's customer, a gaming company called X, experiences high demand for computational resources during nighttime hours when many gamers are online on both computers and mobile devices. As games become increasingly complex and distributed, companies like X are integrating LLMs into their platforms to enhance gameplay. Offloading some of this processing to external infrastructure would be beneficial, particularly if X could access these idle resources at a competitive rate. Being an existing customer of Operator A, company X could potentially secure these resources at favourable rates. However, some time and area-based restrictions may apply, so if users are requesting these computational resources during a time of day that compute resources are more scarce or outside a designated area, the offloading may not take place.
Company X (or another company Y) also offers lightweight augmented reality glasses, and would also like to make use of the above mentioned computational resources of Network Operator A to be able to perform higher quality image processing and real time analysis of the camera images capturing the scene, and provide a responsive experience to the users wearing these lightweight augmented reality glasses, and offload some AI and/or other computational tasks in order to save battery power (assuming running workloads on UE will drain the battery much faster). Each of these UEs have subscription from 6G network to use these computational resources.
6.2.3 Service Flows
1. A third-party requests access to specific xPU (GPU, CPU etc.) or AI resources via an API provided by Operator A. These requests may include information about a desired time period of the day (e.g. a few hours at night). an area in which its devices are deployed, and a desired QoE. Similarly, an (application on the) UE can request making use of computational resources to offload computational tasks.
2. The 6G network verifies the request against defined policies and authorizes access if conditions are met.
3. The 6G network checks with the policy framework, which in turn may confirm with orchestration system if sufficient resources are available for the expected duration based on the third-party request, by potentially leveraging the available information on the history of network usage. Upon approval, the 6G system allocates the requested resources and establishes secure access for the third-party. This unique network-aware resource exposure capability is enabled by the 6G system's centralized policy framework.
4. The operator could also add its own rules and policy to manage the resource allocation with the help of policy framework.
5. The system continuously monitors resource usage and records data for charging and reporting purposes.
6. The 3rd party is also allowed to analyse the usage in real time.
7. The 6G system schedules the available computational resources amongst UEs (e.g. AR glasses) of the different gamers taking into account the requested time period, area, QoE, and the mobility of the UEs.
8. Given that some of the UEs (e.g. AR glasses) are moving around, the 6G network offers efficient ways to transfer/handover a computational task from one computational resource to another computational to meet the requested QoE.
6.2.4 Post-conditions
1. Third-party entity completes its tasks using the allocated resources.
2. The 6G network releases the resources and updates the availability status in real time.
3. Usage data is logged, and relevant charges are generated and sent for billing.
4. Policy function is in control of entire operation.
6.2.5 Existing features partly or fully covering the use case functionality
None.
6.2.6 Potential new requirements needed to support the use case
[PR 6.2.6-1] Based on operator policy, the 6G network shall provide suitable means to allow authorized third parties and/or UEs to retrieve availability information about computational resources (e.g. storage, AI processing units, xPUs information etc.) inside the Service Hosting Environment and to utilize these computational resources for running workloads on demand.
[PR 6.2.6-2] The 6G network shall support charging for the usage of network resources by third parties.
[PR 6.2.6-3] The 6G network shall support detailed monitoring and reporting of computational resource usage in the Service Hosting Environment.
[PR 6.2.6-4] Based on operator policy, the 6G network shall support efficient ways to transfer a computational task from one computational resource of the Service Hosting Environment at one location to another computational resource of the Service Hosting Environment at another location, based on the mobility of UEs and the desired performance requirements.
6.3 Use case on end-to-end AI for connected cars
6.3.1 Description
The proposed use case aims to enhance the in-vehicle user experience by leveraging AI technologies to provide contextually aware and seamless services for drivers and passengers. Current AI systems such as LLMs and AI Agent have their advanced capabilities to understand driver intentions by considering various situational contexts, including external environmental factors, subjective user inputs such as tone of voice, conversation history, and the ability to make inferences and take actions autonomously. Through this sophisticated understanding, LLMs have the potential to significantly improve vehicle operations and enhance user interactions.
The deployment of AI based services within the connected car environment presents several challenges due to the substantial computational, memory, and storage resources they require. To address these demands efficiently, a tiered approach is proposed (Figure 6.3.1-1), utilizing in-vehicle AI, edge AI, and cloud AI collaboratively:
In-vehicle AI: Deployed directly within the vehicle, in-vehicle AI systems are designed to manage essential operations and simple conversations and interactions concerning driving or vehicle settings. This localized AI operates independently of network connectivity, ensuring zero latency and real time responsiveness for basic user requests.
Edge AI: AI servers deployed in 3GPP operator’s edge data networks such as Service Hosting Environment offer to AI systems more substantial computational capabilities. These systems are suitable for deploying more powerful models, such as multi-modal LLMs, which require greater processing power and can deliver advanced features with minimal latency. Edge AI serves as an intermediary by balancing resource availability and minimizing latency while handling more complex AI tasks than in-vehicle AI systems.
Cloud AI: Cloud AI systems has vast computing resources, enabling them to execute highly sophisticated and resource-intensive tasks. Although cloud AI is subject to higher communication latency, it is ideal for providing non-real time services and executing collaborative functions within a retrieval augmented generation (RAG) architecture, thereby expanding the scope and depth of available services.
Figure 6.3.1-1: End-to-End AI for connected cars
In-vehicle AI, edge AI, and cloud AI are orchestrated to operate in harmony, adapting dynamically to user requests, network connectivity, network congestion, latency conditions and etc. This collaborative, end-to-end AI framework ensures that users receive seamless and contextually enriched services, harmonizing AI deployment to optimize resource utilization and ensure high-quality user experience in diverse driving scenarios.
This integrated approach suggests the criticality of intelligently managing resource distribution across the three AI layers to ensure the AI services are provided in the most timely and context-appropriate manner.
6.3.2 Pre-conditions
User A is driving a car equipped with the necessary hardware and software to support AI-based services.
The vehicle hosts a local agent service capable of interacting with User A through voice, enabling hands-free operation and communication.
The car is registered with Operator A's network.
6.3.3 Service Flows
1. While driving, User A utilizes the in-vehicle local agent by posing a question about a newly encountered landmark, such as "What is this mountain in front of me?"
2. Recognizing the need for image analysis, the local agent assesses its capabilities and identifies that the in-vehicle AI lacks image recognition functionality. Consequently, it finds an Edge AI system (with multi-modal LLMs or AI Agent deployed/hosted) via Operator A's network which can provide image recognition service.
3. The on-board camera data capturing the image of the landmark is transmitted to the AI system in Edge AI. Utilizing its advanced image recognition capabilities, the Edge AI system processes the data and returns a result to the local agent with minimal latency, identifying the landmark as "Mount Fuji."
4. Equipped with the recognition result, the local agent delivers a concise and informative introduction about Mount Fuji to User A, enhancing his driving experience.
5. Intrigued by the brief introduction, User A expresses a desire to arrange a tour of Mount Fuji to the local agent.
6. The local agent proceeds to collaborate with the cloud AI system to facilitate the tour arrangement. The cloud AI system accesses and synthesizes various data sources, including tour availability and User A's schedule, while interacting seamlessly with both the Edge AI system and local agent to gather additional context, such as User A's position history and preferences. The cloud AI system identifies a two-hour free window in the afternoon suitable for the tour and assembles the necessary itinerary and travel details.
7. The local agent conveys the proposed tour itinerary and details to User A. Based on the information provided, User A confirms their decision to attend the tour of Mount Fuji in the afternoon.
6.3.4 Post-conditions
User A enjoys the tour of Mount Fuji.
6.3.5 Existing features partly or fully covering the use case functionality
General edge functionalities have been specified in TS 23.548 [137] and TS 23.558 [52]. However, AI-driven dynamic interactions and seamless integration across different AI deployment are not considered.
AI/ML related requirements have been discussed during R18 and R19. Current AI/ML specification (TS 22.261 [14] clause 6.40) focuses on AI/ML operation splitting between AI/ML endpoints and AI/ML model/data distribution and sharing over 5G system. So, the related requirements are mainly about the transfer of AI-ML models and data. The requirements of this use case are about providing computational resource for AI services which is not covered in current specification.
6.3.6 Potential New Requirements needed to support the use case
[PR 6.3.6-1] The 6G network shall be able to provide computing resource in the Service Hosting Environment for AI services.
6.4 Use case on system performance optimisation using AI
6.4.1 Description
This use case address optimization of communication between terminals and network through implementing and using the AI capabilities for the communication system network functions (AI-enabled). These AI-enabled communication system functions are responsible for the enhancement of communication system operations and functionalities, such as traffic prediction, resource allocation and planning, based on the available knowledge about the individual end users’ behaviour.
End users’ behavioural context may be gathered both at the device and at the network and used in a complementary fashion to optimize communication. For example, the network may use historical information from location updates and location measurements to infer the user’s current position and environmental conditions. Additionally, by leveraging AI capabilities in the UE, the UE may identify scenarios where current and future location information are available and useful may provide this information to the network. The combined user location information from the UE and network can then be used to improve communication efficiency and deliver user experience for 6G services.
For instance, in the context of registration and mobility management, the usage of AI capabilities by the communication system functions involved in performing these processes, will enable:
The prediction of UE behaviour, based on UE type, historical data and mobility patterns, enables NFs to allocate/plan resources more efficiently, and enforce appropriate policies (e.g. control the frequency of periodic registration updates), which will result in optimizing the process
set the location tracking parameters based on UE mobility patterns (e.g. reduce signalling for low mobility)
help to resolve issues related to service operations and improve network reliability
analysing the real time data, to make decisions and adjust the registration and paging parameters (e.g. the user changed the expected location).
6.4.2 Pre-conditions
Recently, the number of activities that do not involve movement, such as studying and working remotely, has increased. For such scenarios, the network should optimize the location registration when the UEs do not change location and stay in a specific area for a long time.
In scenarios where the user location may be different from its typical pattern and predictable ahead of the time, the UE could provide such information to the network.
6.4.3 Service Flows
In order to reduce resources and traffic for such users, by using AI-enabled NFs in the core network, the optimization can be achieved as follow:
the AI-enabled network functions analyse the behaviour of the UE registered to the network using the historical available data.
based on the UE locations during the day, e.g. if the user works from home, the network functions can decide to reduce the frequency of sending the periodic location updates for this UE.
the network applies the new parameters selected/set to communicate with the UE
Even though the user typically works from home, today, the user has a customer meeting at 3 p.m. at a downtown location and needs to take the metro train to get there. With on-device AI, the UE identifies this as an atypical event (and likely not reflected in historical location information available at network per step 1), so the UE notifies the network. By taking the user's future location plans into account, throughout the day, the network can set location tracking parameters appropriately.
In certain cases, the UE may not be able to share user location information with the network due to privacy considerations or rapid changes in location. However, the UE can still collaborate with the network in setting location-related parameters. This can be done via the UE recommending parameter settings to the network, or the network may provide a range of parameters to the UE and based on its AI capabilities and user context, the UE may select a desired option. Ultimately, the network and UE would collaborate to select the appropriate location parameters that support the communication service efficiently.
6.4.4 Post-conditions
By leveraging AI capabilities at the network and UE, the communication system operations and functionalities are optimized without compromising promised quality or impacting services.
6.4.5 Existing features partly or fully covering the use case functionality
none.
6.4.6 Potential New Requirements needed to support the use case
[PR 6.4.6-1] Based on operator policy, the 6G system shall support AI capabilities.
NOTE: Example of AI capabilities is the system ability to predict the UE behaviour (based on UE type, historical data, mobility patterns, etc.) and use that for the allocation and planning resources efficiently.
[PR 6.4.6-2] Based on operator policy, the network entities supporting AI capabilities shall be able to collaborate upon request.
[PR 6.4.6-3] Based on operator policy and user consent, the 6G system shall be able to support mechanisms (e.g. AI capabilities in the network and UEs) allowing the network and UEs to negotiate communication parameters for a communication service.
6.5 Use case on personalized AI for health monitoring[[SUGGESTION_START]].[[SUGGESTION_END]]
6.5.1 Description
A wearable health monitoring device (e.g. a smartwatch or fitness band) collects biometric data, such as heart rate, oxygen saturation, and body temperature. The device needs to analyse this data and provide real time health insights. This analysis may be based on an AI model personalized for the user, which is tailored to the user's medical history and current activity level.
6.5.2 Pre-conditions
A user possesses a health monitoring device (UE) capable of collecting real time biometric data, e.g. hear rate, oxygen saturation, body temperature, etc. The device is also capable of communicating with the 6G network.
6.5.3 Service Flows
1) A user engages in a workout session while wearing the health monitoring device (UE).
2) The UE collects real time biometric data and notices irregular patterns in the user’s heart rate.
3) The UE creates a compute task to analyse the irregular heart rate. The compute task could comprise a personalized AI model that will predict whether the irregularity indicates stress, dehydration, or a potential medical issue and will generate recommendations for the user, e.g. "Take a break" or "Drink water" or "Consult a doctor".
4) The compute task will take a long time if executed locally in the UE, or it may be even infeasible to be locally executed due to limited resources in the UE. Hence, the UE requests from the 6G network to execute the compute task.
For this purpose, the UE may provide the executable code, recent (anonymized) heart-rate measurements, and execution requirements, such as latency requirements indicating how fast the results should be provided. Such requirements can be used by the 6G network to allocate the appropriate resources for the compute task execution.
NOTE: If the user heart-rate measurements are provided to the network, they should be anonymized to fulfil privacy requirements. In addition, the UE might request user’s consent before sharing these measurements.
6) The UE receives the results of the compute task execution from the 6G network. The results may contain actionable insights, such as "Consult a doctor" or "Take a break" or "Drink water."
6.5.4 Post-conditions
The UE alerts the user using the received actionable insights.
6.5.5 Existing features partly or fully covering the use case functionality
N/A
6.5.6 Potential New Requirements needed to support the use case
[PR 6.5.6-1] Subject to user consent, the 6G system shall support mechanisms to execute compute tasks in the Service Hosting Environment upon service request from UEs.
[PR 6.5.6-2] Subject to user consent, the 6G system shall support mechanisms that enable a UE to specify when a compute task should be executed (e.g. immediately, periodically, etc.) in the Service Hosting Environment.
[PR 6.5.6-3] Subject to user consent, the 6G system shall support mechanisms that enable a UE to specify requirements for a compute task in the Service Hosting Environment, such as an overall latency indicating how fast the results of the compute task should be provided.
6.6 Use case on 6G AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent collaboration with third-party AI using LLM
6.6.1 Description
AI Agents play a crucial role in modern telecommunications by enabling intelligent automation, decision-making, and adaptive network management. These agents are software-driven entities that leverage artificial intelligence, including machine learning and natural language processing, to interact with users, applications, and network components. In a 6G environment, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents enhance network efficiency by dynamically optimizing resources, predicting network conditions, and facilitating providing seamless communication between services. By integrating LLMs, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can understand complex requests, translate them into actionable insights, and orchestrate 3GPP services (e.g. communication service, sensing service, AI-related) network capabilities and functions autonomously, ultimately improving user experience when consuming the 3GPP services, operational efficiency, and service innovation. [80]
In this use case, a third-party application (e.g. a smart city traffic management system) AI Agent sends a text-based request or query to the 6G network. The request is processed by an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in the 6G network that leverages LLMs and the network's advanced capabilities (e.g. sensing, real time data processing, telemetry, analytics, and others) to provide a response or perform an action. This interaction mimics how users interact with chatbots like ChatGPT®, but it is tailored for network-specific tasks and applications.
NOTE: ChatGPT® is an example of a suitable product available commercially. This information is given for the convenience of users of the present document and does not constitute an endorsement by 3GPP of this product.
The 6G network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent acts as an intelligent intermediary, interpreting the text-based request, gathering necessary data, and returning a response or executing a task. Several unique aspects are highlighted on how the 6G network AI Agent responds to third-party AI Agents’ intents. In particular, the 6G network AI Agent can invoke multiple internal capabilities (e.g. sensing, analytics, and exposure) based on operator policies. Unlike traditional service activation, the 6G network AI Agent may fulfil the request by generating insights or recommendations (e.g. traffic mitigation strategies) and securely exposing them to the users.
6.6.2 Pre-conditions
The third-party application (e.g. smart city traffic management system) has access to 6G network’s AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent services.
The 6G network has deployed LLM-based AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents capable of understanding and processing natural language queries.
The 6G network has access to real time data sources (e.g. traffic sensors, weather data, network performance metrics) to fulfil the requests.
The third-party application and the 6G AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent have established secure communication channels (e.g. authenticated APIs or message brokers).
The 6G network supports edge computing to process requests locally and reduce latency.
6.6.3 Service Flows
Operator R, which operates a 6G network, provides sensing-based services to authorized third parties, such as a smart city authority. Instead of deploying its own sensors (e.g. cameras and other monitoring equipment) to detect traffic conditions, the smart city can procure these services from Operator R.
Additionally, Operator R can enhance these sensing-based services by integrating advanced analytics, such as UE mobility patterns. This enriched information is then made accessible to authorized third parties through AI Agent(s), enabling optimized urban management and smarter decision-making.
Moreover, the 6G network under the administration of Operator R is able to interpret a received intent (e.g. road traffic congestion diagnosis, road traffic flow optimization). Depending on operator policy, the network can invoke multiple service components leveraging conventional network procedures or APIs—such as sensing to retrieve real time road traffic data, connectivity to collect information from user equipment, data processing (e.g. through analytics or AI-based reasoning to determine the cause of congestion and generate recommendations), and providing the results to the third-party in a suitable format.
The third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent (e.g. Smart city traffic controller) sends a request to the 6G network AI Agent. For example:
"Why is the traffic congested on Highway A, and what adjustments can be made to optimize traffic flow?"
The 6G network's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent receives the request and uses an LLM to interpret the query. The LLM breaks down the request into two parts:
Part 1: Retrieve the current traffic congestion level on Highway A.
Part 2: Provide recommendations for optimizing traffic flow.
The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent queries real time data sources (e.g. sensing infrastructure, etc.) to gather the current traffic congestion level on Highway A.
The data gathered is used by the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to infer and thus generate analytics insights to determine that Highway A is experiencing high congestion due to an accident. The data may also be used to update (improve) the LLM at the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent, e.g. self-learning capability of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent.
Based on the analysis, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent generates traffic optimization recommendations, such as:
Increasing green light duration on alternate routes.
Reducing green light duration on Highway A to divert traffic.
Deploying dynamic signage to inform drivers of alternative routes.
These recommendations are sent to the third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent managing the smart city infrastructure.
The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent generates a response to the third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent:
"Highway A is currently experiencing high congestion due to an accident. Recommended actions include adjusting traffic light durations on alternate routes and deploying dynamic signage to inform drivers. Expected congestion reduction in 10 minutes."
The response is sent back to the third-party application, which displays it to the user (e.g. a traffic management controller or city official) or uses it for further decision-making. The final implementation of the recommendations remains under the control of the third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent or human operators.
In addition, the third-party application sends another request to the 6G network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. For example, “I need the most reliable network service that you can offer in order to connect all traffic lights in the city.”
For such request the 6G network's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent queries the information on available mobile network capabilities and identifies the mapping between the request and available mobile network services that can be configured or updated (e.g. network slices dedicated for connectivity between traffic lights). Furthermore, once the new related services become available (e.g. due to the network upgrade) that match smart city requirements on connectivity, the 6G network's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can send an intent related to newly available service by advertising such services and offering them for trial.
Then the 6G network’s AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent indicates to the third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent managing the smart city infrastructure that the reliable connectivity request could be fulfilled and under which monetary implications. If the third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent answers positively (including automatically based on a cost threshold) the requested connectivity is established. The same can apply to any connectivity service matching the third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent needs that is advertised by the 6G AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent, once it receives the confirmation from the third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to enrol in the service or its trial.
The interaction between 6G network’s AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and the third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent is illustrated in Figure 6.6.3-1.
Figure 6.6.3-1: Illustration of 6G network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent communication with 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent
6.6.4 Post Conditions
The third-party application receives the response and takes appropriate action (e.g. notifying city officials or updating a public traffic dashboard).
The traffic light optimization reduces congestion on Highway A.
The smart city gets always the best possible connectivity services matching its needs.
The 6G AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent logs the request, response, along with any additional contextual meta data for future reference (e.g. memorization capability of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) and continuous improvement of the agent’s underlying LLM.
6.6.5 Existing features partly or fully covering the use case functionality
None.
6.6.6 Potential new requirements needed to support the use case
NOTE: The mention of AI capabilities such as AI Agent doesn’t imply or preclude any architecture assumption or solution.
[PR 6.6.6-1] Based on operator policy, the 6G network shall be able to support secure means to expose its services to the authorised third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent based on its intent.
[PR 6.6.6-2] Based on operator policy and user consent, the 6G network shall be able to take into account information related to user mobility context, subscription information when invoking 3GPP services based on received intent(s) from the user.
[PR 6.6.6-3] Based on operator policy and user consent, the 6G network shall support mechanisms (e.g. AI capabilities such as AI Agent) to send intent(s) related to 3GPP services towards a third-party AI Agent (e.g. proactively or in response to a received intent), also taking into account information related to user mobility context, subscription information.
[PR 6.6.6-4] Based on operator policy, the 6G network shall support mechanisms (e.g. AI capabilities such as AI Agent) to provide multiple 3GPP services, in response to received intent(s) (e.g. from a third-party AI Agent).
6.7 Use case on AI-agents communication
6.7.1 Description
Autonomous agents (AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents) have long been recognized as a promising approach to achieving artificial general intelligence (AGI), which is expected to accomplish tasks through self-directed planning and actions [81]. In recent years, these agents, leveraging the capabilities of LLMs, are expected to effectively perform diverse tasks in social science, natural science, and engineering, among others. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can take on various forms, such as embodied intelligent robots, virtual assistants, and autonomous systems (e.g. drones).
AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents have different attributes, such as different capabilities, resources, etc. For example, paper [148] classified AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents based on their techniques and capabilities; paper [149] classified AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents based on their performance, generality, and autonomy.
Multiple AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents could collaborate through natural language conversations to complete the tasks [82], which abstract multiple roles to supervise task process.
As communication serves as a common mechanism for sharing information, we can foresee that in the future, there will be more and more users and their AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents that need to be supported. A group could be established for users and their AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents to communicate with each other. To complete a complex task involving multiple users and triggered by a user, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent or application, communication domain for multiple groups could be established, the users and AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents working for the same task can be explicitly identified by the task requirement or implicitly identified based on location area or relative distance. Communication domain could be dynamically created for users and AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents from multiple groups to communicate with each other for a specific task during a specific time. Only the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents in the same domain can communicate with each other. If authenticated/authorized, users and AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents could join this group via various access technologies, including the cellular network, WiFi and Ethernet, etc.
6.7.2 Pre-conditions
As shown in Figure 6.7.3-1, Grandpa Bob plans to clean the room and host a gathering at home with his daughter Alice’s family and his son Charlie’s family. So, he issued the command "Clean the room and help me prepare for the family gathering tomorrow afternoon" to his AI-assistant.
1) Bob has just purchased a home robot and has not connected to the internet.
2) Except for the home robot that Bob just bought, all other AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents of users, have been assigned with digital identities associated with the user's identity.
3) The users’ AI-agents involved in this scenario include AI assistant, drone and intelligent vehicle, etc. Intelligent vehicles and drones are connected through cellular networks, while other AI-agents are connected through home WiFi.
4) All AI-agents registered to 6G system, so that their attributes are visible for some of the members, based on permission and authorization.
6.7.3 Service Flows
Figure 6.7.3-1: Task-Oriented Multi-Group Communication Network for multiple user AI-agents communication
Bob, Alice, and Charlie each request the operator to create AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent groups for them. They then invite their respective AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents, such as their AI assistants, smart cars, drones, etc., to join their individual groups. In other words, there are separate groups for Bob, Alice, and Charlie, allowing the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents within the same group to communicate with each other. In addition to owner information (e.g. related user, etc.), examples attributes for Bob’s AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in this use case are as follows:
Table 6.7.3-1: Illustrative attributes of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents
Service Features
Capabilities
Permission
AI assistant
Service description: (Voice and Text-Based Interaction, Information Retrieval, Personalized Recommendations)
Service area: wide
Moving speed: N/A
WiFi & cellular connection;
Tools (Perception + Action);
Reasoning & Decision making;
Memory + Reflection;
InvitationAllowed: true;
DiscoveryAllowed: true
ShareAllowed: true;
CreatGroupAllowed: true
Drone
Service description (e.g. Radar, camera, Grasping, navigation);
Service area: wide
Moving speed: Medium
WiFi & cellular & V2V connection;
Tools (Perception + Action);
InvitationAllowed: true;
DiscoveryAllowed: true
ShareAllowed: true;
CreatGroupAllowed: true
Intelligent vehicle
Service description (e.g. Radar, camera, driver assistance, and improved navigation);
Service area: wide
Moving speed: High
WiFi & cellular connection;
Tools (Perception + Action);
Reasoning & Decision making;
Sensing;
InvitationAllowed: true;
DiscoveryAllowed: true
ShareAllowed: true;
CreatGroupAllowed: false
Home robot
Service description (e.g. voice interaction, cleaning, security monitoring);
Service area: Local
Moving speed: Low
WiFi connection;
Tools (Perception + Action);
InvitationAllowed: true;
DiscoveryAllowed: true
ShareAllowed: true;
CreatGroupAllowed: true
As the table illustrates, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents vary in their service features, capabilities and permission, etc. This results in different network requirements. For example, an AI assistant might require cloud storage for extensive data storage, while a drone may need network-provided sensing and computing capabilities for obstacle identification.
The newly purchased home robot joins Bob’s group after authentication and is associated with Bob’s identity. Then, the home robot reports its capability to the group.
Bob sends the request to his AI-assistant and requests the AI-assistant to arrange the gathering as task coordinator. The AI-assistant received Bob’s request. Based on the local or cloud-based knowledge base, Bob’s AI-assistant retrieves the history of past family gatherings, determines the list of sub tasks and distributes the sub tasks to corresponding AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents.
Bob’s AI-assistant communicates with the discovered cleaning robot based on its capabilities and sends the request to the cleaning robot to carry out a full house cleaning. When the cleaning robot needs collaboration from other robots, e.g. to move some heavy furniture, it will setup a cleaning group with others to enable efficient communication within the cleaning group. After the cleaning, the communication group will be released accordingly.
Bob’s AI-assistant communicates with the discovered and selected restaurant’s AI assistant by matching the requirements and AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents’ attributes. Bob’s AI-assistant orders food for tomorrow and negotiate the food delivery time and location with restaurant’s AI assistant. The restaurant’s AI assistant selects a drone based on its capabilities from restaurant’s group, to do the food delivery. Then the restaurant’s AI assistant sends the determined time and location to the food delivery drone and sends the information of selected drone to Bob’s AI-assistant, so that Bob’s AI-assistant can communicate with drone.
During food delivery, the drone offloads a portion of the computational tasks to the network, to assist it in obstacle identification, by assisting in the process of modal transformation, for example, by pre-processing the images captured by drones, and obtaining alert data. When drone arrives at the pick-up point, they will setup a temporary communication group with the home robot to enable secure and cooperative food delivery. Considering the drone and home are from different vendors, they support different protocols and information modalities, the communication is supported even if the drone and home robot transmit different modality information, e.g. video/text: the 6G network transform the video taken by drone and pick-up point address in text format to semantic maps with guideline marks that the robot can recognize to retrieve the delivery, etc. The communication group will be released after the delivery.
Bob’s AI-assistant communicates with Alice’s AI assistant and intelligent vehicle. Bob’s AI-assistant negotiates with Alice’s AI assistant for the time and place to pick up Alice’s and sends the request to the intelligent vehicle to pick up Alice’s with negotiation result (i.e. the pick-up time and place).
Bob’s AI-assistant communicates with Charlie’s AI assistant and intelligent vehicle. Bob’s AI-assistant negotiates with Charlie’s AI assistant for the time and place to pick up Charlie’s and sends the request to the intelligent vehicle to pick up Charlie’s with negotiation result (i.e. the pick-up time and place).
6.7.4 Post-conditions
1. The cleaning robot cleans Bob’s house.
2. The drone sends the foods from the restaurant to Bob’s house.
3. The intelligent vehicle picks up Alice’s and Charlie’s and sends them to Bob’s house.
4. Alice, Charlie and Bob have a happy gathering party.
6.7.5 Existing features partly or fully covering the use case functionality
The identification requirements can be partially covered by Personal IoT Networks (PINs) and Customer Premises Networks (CPNs) in TS 22.261 [14], clause 6.38. However, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents have different attributes (e.g. users, capabilities), the 3GPP system is expected to support new identification mechanism to enable the association between AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and user, as well as the association of the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent's own attribute information, enabling more flexible discovery, communication, and collaboration.
The 5G system shall support mechanisms to identify a PIN, a PIN Element, an eRG and a PRAS.
Subject to regulatory requirements and operator policy, the 5G system shall support an efficient data path within the CPN for intra-CPN communications.
The efficient communication and collaboration can be partially covered by 5G LAN-type service, as defined in TS 22.261 [14]. However, the group is generally defined by subscription and managed by operators or 3rd authorized party. Therefore, it cannot satisfy the dynamic requirement from AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents which require a certain level of flexibility and autonomy. For instance, for a short term or emergency task, the communication service provided to the group of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents should be established in a relatively short time to match time-efficiency of the task. However, the high time-efficiency requirement is hard to meet by 5G-LAN-type service since it requires lots of manual operations, e.g. LAN configuration, subscription, Data Network Name/Single Network Slice Selection Assistance Information configuration, etc. In addition, it cannot address the interoperability issue between diverse AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents. The 3GPP system is expected to enable seamless communication between these diverse AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents supporting different protocols with various capabilities.
5G System supports management of 5G VN Group identification and membership (i.e. definition of 5G VN group identifiers and membership) and 5G VN Group data (i.e. definition of 5G VN group data). The 5G VN Group management can be configured by a network administrator or can be managed dynamically by AF.
The requirement on supporting the intelligent can be partially covered by AI/ML model transfer in TS 22.261 [14] clause 6.40. The main difference is that AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can operate autonomously, allowing them to communicate with each other without human instructions, resulting in new requirements on security mechanism. These communications are imperceptible to users and can occur between AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents on the device side, cloud side, or edge side, resulting to new requirement on flexible communication and coordination mechanism.
Subject to user consent, operator policy and regulatory requirements, the 5G system shall be able to expose information (e.g. candidate UEs) to an authorized 3rd party to assist the 3rd party to determine member(s) of a group of UEs (e.g. UEs of a FL group).
Based on user consent, operator policy and trusted 3rd party request, the 5G system shall support a means to authorize specific UEs to transmit data (e.g. AI-ML model data for a specific application,) via direct device connection in a certain location and time.
Based on user consent, operator policy, and trusted 3rd party’s request, the 5G system shall be able to provide means for an operator to authorize specific UEs who participate in the same service (e.g. for the same AI-ML FL task) to exchange data with each other via direct device connection, e.g. when direct network connection cannot fulfil the required QoS.
Based on user consent, operator policy and trusted 3rd party request, the 5G system shall be able to dynamically add or remove specific UEs to/from the same service (e.g. a AI-ML federated learning task) when communicating via direct device connection.
6.7.6 Potential New Requirements needed to support the use case
[PR 6.7.6-1] Based on regulatory requirements, operators’ policy and user consent, 6G network shall support trusted network access for 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and support a mechanism to expose 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent’s attributes (e.g. related users, sensing capabilities, AI capabilities, service features) to other 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents.
[PR 6.7.6-2] Based on regulatory requirements, user consent, operators’ policy and agreement with authorized 3rd party, the 6G network shall be able to support security identification for 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents provided by authorized 3rd party associated with a user (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents belonging to a customer).
[PR 6.7.6-3] Based on regulatory requirements, operators’ policy and user consent, 6G network shall support mechanisms for 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents to provide/register their attributes (e.g. sensing capabilities, AI capabilities, service features, associated authorized users) to 6G network, and discover other authorized 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents to achieve collaborative tasks.
[PR 6.7.6-4] Based on regulatory requirements and operators’ policy, the 6G network shall provide means to support efficient and secure communication (including multi-modality exchange) between multiple 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents on UEs over a target area.
NOTE: This requirement can apply to 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents of same users or different users. It is expected that the required communication service would be provisioned in the range of minutes to days, depending on use case. Lower for temporary task[[SUGGESTION_START]]s[[SUGGESTION_END]] and higher for long term task[[SUGGESTION_START]]s[[SUGGESTION_END]].
[PR 6.7.6-5] Based on operator policy, the 6G network shall be able to support secure means to expose different services, e.g. computing offloading service in Service Hosting Environment, to the authorized third-party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent.
6.8 Use case on 6G system assisted AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent service
6.8.1 Description
AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on devices will be popular in 6G era, due to the fast development on device-based computing power and model light-weighting:
- More powerful computing in device side: The near-future processor is expected to support local running of AGI models with up to tens of billions of parameters.
- Light-weight AGI model becomes available: AGI models have the characteristics of "cloud-side training and device-side deployment".
As Figure 6.8.1-1, shown below, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent device interacts with environment, where the device entity empowered by AI technology (e.g. AGI model) can collect environmental information and local information, make decisions itself, and perform action to affect the environment. It is expected, the 6G system can assist AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent device for awareness, decision making and actions in a couple of aspects.
Figure 6.8.1-1: Closed-loop operation for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent service
Below is the gap analysis that what services provided by 6G system can assist the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent device:
Apart from the local data in AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent device, the information from environment is supplementary for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to make a highly accurate inference. In 6G system, an efficient way to support environmental information exposure to UE, including:
- Sensing information will be useful for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent service. For example, the information surrounding objects is useful for a vehicle to realize the traffic situation in real time and based on which the vehicle can do the corresponding actions such as changing lane and speed.
- The real time QoS change information. For example, this planed QoS information can be used for vehicle to change autonomous mode to manual mode when the QoS cannot fulfil the expected latency.
Though the computing in device will be more powerful, it is still difficult to run the whole AGI model merely relying on the device itself. Thus, task offloading will be needed to realize a “device-network collaboration”, in order to make a suitable decision and action. By doing so, 6G network needs to provide some 3GPP services (e.g. Sensing, positioning, text-voice converting, language translation) from 6G network
In order to let an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent device perform action without human intervention, the 3GPP network need to authorize the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent device to communicate with other device and/or AS. By doing so, the user centric grouping is needed: one or more AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents may belong to the one user, family or an enterprise while the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent functionalities. The SUPI/IMSI may not be properly used to identify an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent, given the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent functionality can be moved from one entity to another. Hence, a digital representation and User ID as well as the corresponding authentication/authorization is needed.
Moreover, with the emergence of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent devices, the communication between devices supporting AI capabilities, and the communication between AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent device and a network server will be popular. As an example, the communication can be performed in term of “token”, which is understood as a “word vector” transformed by AI model based on the human readable data (such as text, image, video) and being embedded into AI model supported in AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. The traffic enables an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to collaborate with other AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents intra/inter an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent group, and/or with the application.
6.8.2 Pre-conditions
Bob has a robot nanny as his personal AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent, bought from vendor-A. Bob’s cell phone is his other personal AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent bought from vendor-B. Those AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents on device, as personal assistants, can assist Bob with suggestions and actions based on information the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents are aware of.
6.8.3 Service Flows
1) Bob, living in his home in Beijing, decided to go to Sanya for his winter vacation. He asked his robot nanny (AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) to book a 5-star hotel with lowest price.
2) The personal assistant began to check the well-known brand hotels in Sanya. As Bob is a VIP in some hotels, and the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent needs to get the VIP price, when the robot nanny tried to login the hotel website, the application checked with 6G network that the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent (Robot nanny) is Bob’s valid personal device and the application provided the VIP price to the Robot nanny.
3) By collecting a couple of 5-star hotels’ price, the Robot nanny finally selected and booed a hotel room with the lowest price for Bob.
4) Later-on, when Bob has left home for the airport, he remembers there is a delivery at home that needs to be pick up. Thus, Bob asked the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on cell phone, via 6G network, to notify robot nanny who is at home to pick up the delivery. Given the two AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents are from different vendors, they support different protocols and information modalities, the 6G network helped to interconnect the cell-phone agent with the robot nanny and then transform the “pick-up delivery” command from the cell phone to the pictures with guideline marks that the robot nanny can parse.
5) Robot nanny managed to pick up the delivery.
6.8.4 Post-conditions
Thanks to the 6G network, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on device can assist Bob to find the VIP price for booking hotel, and the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can communicate with each other without “language” barrier.
6.8.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] clause 6.40.2.1 AI/ML model transfer in 5GS
It provides the requirements about some information exposure to 3rd party applications (such as resource utilization, Planed QoS parameters, status information of an AI-ML session.
TS 22.101 [58] clause 26a User Identity
It provides the requirements about User Identities with related User identifier for a user. The User Identifier is different to SUPI/IMSI/MSISDN. It supports user authentication with User Identifiers from devices that connect via the internet.
TS 22.156 [28] clause 5.2.2 Avatar-based real time communication
It provides requirements about digital representation for an Avatar. It is unclear if it can be correlated to an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent.
6.8.6 Potential New Requirements needed to support the use case
[PR 6.8.6-1] Based on user consent and operator policy, the 6G system shall provide a suitable means for an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent application on UE to invoke some 3GPP services (e.g. IMS service).
[PR 6.8.6-2] Based on user consent, operator policy and regulatory requirements, the 6G system shall provide an efficient way to expose information (e.g. change of QoS) to the application on the UE.
[PR 6.8.6-3] Based on user consent, operator policy and regulatory requirements, the 6G network shall be able to provide a suitable means to support the multi-modal data exchange between the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent applications considering data characteristics.
6.9 Use case on collaborative AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents
6.9.1 Description
AI Agents can perform tasks for or represent e.g. devices, persons, drones, or cars. These AI Agents may be either implemented in the UE or in the network. By offloading tasks to the network, devices can save on complexity and energy consumption. Furthermore, an AI Agent in the network can still represent a device, person, drone or car, when that device, person, drone or car is not reachable, e.g. because of radio conditions or battery outage. Offload can happen towards a local/edge network but can also be to a nearby other device with more processing capabilities.
AI Agents can be used for many different purposes. In this use case we include an example of an AI Agent for a car, which is communicating with e.g. AI Agents representing the owners of the car and applications for the energy grid. What specific application the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent performs (e.g. personal agent, collision avoidance, booking parking/travel) is not standardised. What should be standardised is the basic functionality needed to support these AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents and the collaboration between AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents.
Collaboration between AI Agents requires interoperability between different AI Agents. This interoperability between the AI Agents even needs to be provided when the collaborating AI Agents are implemented in different networks. Supporting interoperability across different networks is a case for standardisation. However, with a huge number of different AI Agents, only the basic interoperability aspects can be standardised. E.g. how to identify an AI Agent, how to authorise access to a different AI Agent and how to establish and maintain secure association between AI Agents.
AI Agents will follow the device/person/car/drone/etc. they are associated with to nearby edge network locations. One reason is to reduce latency, another reason is to avoid large amounts of traffic travelling long distance through the network. AI Agents may select relevant other AI Agents to collaborate with based on where they are located.
Sustainability impact analysis:
Material resources: AI Agents will require compute resources. On the other hand, offloading device tasks to AI Agents in the network can lead to less complexity in devices.
Energy resources: Processing AI Agents will imply an increase in the use of energy resources. On the other hand, intelligent applications may incur energy savings in other sectors.
Inclusion & Equality: Collaborating AI Agents are expected to form an important part of the digital society, e.g. interactions with government, public transport, commerce. It is therefore of great importance that AI Agents are accessible to all.
Trustworthiness: Privacy and trustworthiness are key for the public acceptance of AI Agents.
6.9.2 Pre-conditions
Husband and wife John and Ann own an electric car. The electric car has an AI Agent that can organise various things for the car. This includes that it can communicate with applications from the energy grid to optimise charging the car. There is a ‘spot-price’ for electricity that fluctuates with locally available electricity. The price can even be negative if there is more renewable energy produced than can be used. The intelligent agent for the car is provided by the car company. The local networks the AI Agent runs on are determined by contracts the car company has.
John has a personal AI Agent that amongst others manages his calendar. John gets the subscription for the AI Agent through his corporate employer.
Also, Ann has a personal AI Agent that manages her calendar. As Ann is a self-employed consultant, she obtains a subscription for her personal AI Agent from her telecommunications provider.
The car AI Agent has been authorised by John and Ann to access their personal AI Agents to obtain information about their calendars.
6.9.3 Service Flows
1. John is on a business trip abroad with his car. While he is asleep in a hotel, the car is connected to a charger. The AI Agent for the car runs in an edge network near the car.
2. The car AI Agent communicates with a local application for the local energy grid and notices that the price for electricity is particularly high that night in the area of the hotel. There is the possibility to make a profit if the car can actually provide energy from its battery back to the grid.
3. To determine whether it is a good idea to provide energy from the car battery back to the grid, the car AI Agent needs to check whether the car needs to travel far the next day. Rather than calling John and Ann, and waking them up, to get that information, the AI Agent checks the AI Agents from John and Ann to see if any large trips are planned. The AI Agent for John has ported to an edge location at the hotel. The AI Agent for Ann runs in her telecommunications network back at home.
4. The personal AI Agent from John indicates that the next day John plans to travel back home, a 900 km journey. It is not a good idea to use the car battery to sell energy back to the grid.
5. In the morning John sees a message from a friend asking him to meet some friends in the pub. The friends (or their personal AI Agents) are not authorised to access calendar information from his AI Agent.
6. As John is driving back home, the AI Agent for the car is moved to nearby edge networks following the car’s mobility. On the way back, John would like to switch the car to self-driving mode. John’s AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent checks with the car’s AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on when self-driving is possible. The car’s AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent retrieves data from the network on the areas in its trajectory where the network communication service performance (e.g. latency, bandwidth) allow the agent to safely operate the car.
6.9.4 Post-conditions
Information was exchanged between the car AI Agent and the personal AI Agents from John and Ann, even though these AI Agents at that time used computing resources from different providers in different countries. Information was protected against unauthorised access.
Additionally, information regarding communication service performance has been provided from the 6G network to the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents to enable the collaborative task.
6.9.5 Existing features partly or fully covering the use case functionality
3GPP SA6 has specified an architecture for Edge Applications [52] where third party applications can be hosted at the edge. Application mobility into visited networks is also supported. What is not supported are agents for individual devices/persons/cars/drones where it is the device/person/car/drone that offloads compute for specific tasks to the network (or another device). Also, there is no support for interoperability, with authorisation and security, between different applications.
6.9.6 Potential New Requirements needed to support the use case
[PR 6.9.6-1] The 6G system shall support hosting of large amounts of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent applications managed and controlled by the 6G network and/or multiple AI Agent applications on a UE.
[PR 6.9.6-2] The 6G system shall support secure interoperability between AI Agents and between AI Agents and applications to achieve a collaborative task.
NOTE 1: Interoperability between AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents refers to the ability to discover, authenticate and authorize AI Agents to communicate, exchange data, and work together seamlessly.
NOTE 2: Collaborative task refers to an activity, action requiring the involvement of two or more AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents.
[PR 6.9.6-3] Subject to operator policies, the 6G network shall be able to provide authorized AI applications (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents) with communication service performance information (e.g. throughput, latency) relevant for their operation, including to achieve a collaborative task.
6.10 Use case on home robots
6.10.1 Description
In 6G era home robots will engage in household chores based on preconfigured models, such as sweeping the floor, vacuuming, folding clothes, washing dishes, and organizing rooms. They will also take care of family members by monitoring health data, reminding family members to take medication, and dialling emergency calls. Additionally, they will socialize and entertain with humans while interacting with other smart devices to create a more intelligent ecosystem. As shown in Figure 6.10.1-1, all of this aims to bring us a more convenient, comfortable, and safe family life. In this use case, not all of the AI inference actions are performed solely by home robots.
Figure 6.10.1-1: Home robot performs specific tasks based on pre-trained models
As shown in Figure 6.10.1-2, the working principle of the robot includes two main components: perceive and action. "Perceive" refers to the process of sensor data from, such as, cameras or radar, for the input to a large model and obtaining perception result. "Action" refers to the process of taking perception result and task as input to a large model and obtaining action parameters to control the joints of the robot to complete the corresponding action.
task
task
Sensing data
Sensing data
Perception result
Perception result
Perceive
Perceive
Action parameters
Action parameters
Action
Action
Figure 6.10.1-2: The perception-action loop of a home robot
In the case of specific tasks in specific scenarios, the perception model can be deployed locally on the robot as its size is not large. However, for general tasks in open environments, the perception model needs to achieve the perception ability similar to an adult human brain towards the world. Based on psychophysical and neurophysiological studies [281], [282], humans can perceive the gist (e.g. overall meaning) of complex visual scenes within around 150 ms after stimulus onset. This rapid initial perception enables us to grasp the general context of a scene, even if the stimulus is briefly presented (around 10 ms). In the meantime, it takes longer for a human being to identify individual objects, e.g. finer object identification requires larger than 200 ms [283] and around 300ms [284]. In the target scenarios, robots are expected to have similar perception time as an average human. The number of neurons in an adult human brain is approximately 100 billion, and the current model size for complex logical reasoning and task orchestration in laboratory or limited environments, the model size is also generally on the order of 100 B. Due to cost and size limitations, robots are unable to locally implement perception functions for free and open environments.
Also, considering that it may exist the situation that to distribute the model to several robots for local inference. When offering services enabled by AI inference, the mobile network also meets the challenge of providing enough computing power within reasonable cost and ensure its end-to-end performance. Therefore, the mobile network system could consider supporting some of these distributed inference techniques, and allow the subscribers to choose and utilise them to improve the service quality.
For example, in this use case, a robot and the network could negotiate and perform the layer split distributed inference together, i.e. some layers of the model could be performed on the UE, while the others would be offloaded to the network. By using this technique, the UE could run a large model whose computing requirements may be far beyond the capabilities of a typical mobile phone, while protecting the data privacy by making some local data processing.
The 6G network will be equipped with perception models, enabling it to provide perception services (i.e. 6G network performing AI inference of these models on behalf of robots, to generate the perception) to robots in open environments whenever necessary.
This use case aims to provide timely perception and action guidance via the 6G network, assisting robots cope with complex real-world environments and providing users with better intelligent services.
6.10.2 Pre-conditions
Operator X offers comprehensive 6G services for robots, including communication and AI services. Relevant resources have been deployed in the network to facilitate services like large model inference (e.g. using the AI/ML models are provided by 3rd Party), assisting robots in delivering enhanced performance in complex environments.
Lily purchases a robot to assist with various house chores. The robot exhibits a certain degree of local intelligence. By equipping it with one or more advanced models, the robot can better understand the owner’s basic needs by controlling its body joints. For example, it can help with household chores, take care of family members’ well-being, engage in social interactions and entertainment with humans, as well as interact seamlessly with other smart devices.
Lily has subscribed to specific 6G services (communication and AI services) for her robot. Therefore, the robot is able to access to 6G services, such as perception service.
The robot has two perception modes as shown in Figure 6.10.2-1:
Perception mode a) for specific task locally in robot; and
perception mode b) for general task in open environments via 6G network.
(a) perception for specific task locally in robot
(a) perception for specific task locally in robot
(b) perception for general task in open environments via 6G network
(b) perception for general task in open environments via 6G network
Figure 6.10.2-1: Two perception modes of a home robot: (a) perform local perception for specific task, and (b) perform perception for general task in open environments via 6G network
When a robot is instructed by its owner to start general tasks in an open environment, it will request the perception service for the open environment from the 6G network (e.g. 6G network performing AI inference using the AI/ML models provided by 3rd Party), and, the robot will interact more frequently with the 6G network, which helps ensure safer behaviour through perception services.
6.10.3 Service Flows
Step 1: One day, while on a shopping trip with Lily and her child, the home robot carried a newly purchased wine glass that Lily had carefully selected. At this time, the robot follows Lily, moves when Lily moves and stops when Lily stops, holding the newly purchased wine glasses. Therefore, it is Perception mode a), and only the local model on the robot is needed to work, as shown in Figure 6.10.3-1(a). However, when an urgent phone call interrupted their outing and Lily tells the robot that she is going to take a call and asks the robot to watch the child. The robot finds that taking care of the child is a general task in an open environment, so it switches to perception mode b), which the robot cannot support locally, as shown in Figure 6.10.3-1(b).
(b) perception for general task in open environments
(b) perception for general task in open environments
(a) perception for specific task locally in robot
(a) perception for specific task locally in robot
I'm going to take a call, please watch the kids for me.
I'm going to take a call, please watch the kids for me.
Follow me and help me carry the wine glasses.
Follow me and help me carry the wine glasses.
Figure 6.10.3-1: The robot switches perception mode from perception mode a) to perception mode b) after Lily’s instruction
Step 2: Considering the AI service capability supported by network (e.g. inference for perception service) and AI model(s) supported by network, the home robot requests the perception service for general tasks in open environments from the 6G network and frequently sends locally collected important information to the 6G network. The request message includes the perception requirements/demand (e.g. latency, inference accuracy) from the home robot. Then the 6G network determines whether the perception requirements can be satisfied taking UE demand in the request message into account and then sends the response (e.g. acknowledgement including promised requirements) to the home robot.
Step 3: (Perception) The 6G network utilizes the sensing data transmitted by the robot, optionally incorporating environmental information from 6G network itself, as input for the model inference (e.g. using the 3rd Party provided AI/ML models). When the 6G network perceives a normal situation, and it will not interrupt robot’s current task, as shown in Figure 6.10.3-2 (a); when the 6G network perceives a dangerous situation quickly enough comparable to how an average adult does, such as, the ball suddenly falls from the child's hand and rolls to the side of the road while the child runs to pick up the ball just before a car approaches, it generates corresponding behaviour guidance to send to the robot, as shown in Figure 6.10.3-2 (b).
task
task
Figure 6.10.3-2: The perception-action loop when a normal situation or dangerous situation is detected by the 6G network, respectively
Step 4: The 6G network delivers ACK or the perception result to the home robot within the latency included in step 2.
Step 5: (Action) The robot uses local perception result or perception result from the 6G network, along with the task, as input to the local large model to output action parameters for action. The actions corresponding to a dangerous situation detected by the 6G network may include, for example:
Action 1: Drop the box containing the cup;
Action 2: Move towards the child while emitting an alarm sound;
Action 3: Pick up the child and move to a safe area;
Action 4: Explain to Lily what happened and why the box containing the cup was dropped.
Step 6: After executing one or more actions suggested in step 5, the robot assesses the action results. It then reports the service experience (e.g. bad, fair, excellent) along with locally collected environmental data to the network. In this way, the 6G network monitors the performance of the perception service and, if necessary, may upgrade AI model (e.g. by retraining, switching) to guarantee the inference accuracy.
Step 7: When at home, Lily plays with her child to draw the picture. Lily starts the robot and requests the robot to execute a GenAI application to generate a picture of scenery of mountains and rivers. But the robot cannot load and execute the full large model due to memory limitation, it sends a request to network and ask for the distributed inference service.
Step 8: If the distributed inference service is available, the robot could further negotiate with the network about the inference mode, QoS requirement, etc.
For example, the robot could ask for layer split inference and provide information including the required inference framework (e.g. pytorch) and model profile (e.g. model structure, number of layers, suggested layers to be offloaded to the network, etc.). In addition, the robot may request certain QoS requirements, e.g. end-to-end latency, which combines transmission and computing processing latency.
Based on the computing and network resource status, the network may accept the request as it is, or may propose to update some configurations, or even provide its own suggested parameters for the robot to check. For example, in the case of layer split inference, the network may negotiate with the robot or decide by itself about the number of layers to run on the network.
Step 9: After finalizing the configurations of the distributed inference service, the network will choose appropriate computing resource in the Service Hosting Environment for running the model segments and fulfil the service requirements, e.g. end-to-end latency.
Step 10: The robot could either upload the relevant portion of the split model to the network or provide necessary information for the network to get and load the model. Once both the robot and the network load the model segments, they could conduct the distributed inference service collaboratively.
Step 11: Finally, Lily receives the final picture.
6.10.4 Post-conditions
If the robot relies solely on local model inference, it may encounter system crashes in overly complex situations. Alternatively, if it only considers executing the previous command from the female owner, it may pose a danger to the child. However, by providing timely perception and action guidance through the 6G network, the requirements for the robot's local intelligence level can be reduced. Users can purchase a robot at a lower price and obtain high-intelligence robot services that can more easily adapt to complex real-world environments.
6.10.5 Existing features partly or fully covering the use case functionality
Currently, 3GPP systems only provide communication services, and there is no functionality to provide AI as a service.
6.10.6 Potential New Requirements needed to support the use case
[PR 6.10.6-1] Subject to operator’s policy, the 6G network shall be able to provide AI service (e.g. AI model inference) to a UE.
[PR 6.10.6-2] Subject to operator’s policy, the 6G system shall be able to support negotiation of the service performance (e.g. latency, inference accuracy), between UE and 6G network, when providing AI service (e.g. AI model inference).
[PR 6.10.6-3] Subject to operator’s policy, the 6G network shall be able to support mechanism to guarantee the service performance (e.g. latency, inference accuracy) when providing AI service (e.g. AI model inference).
[PR 6.10.6-4] Subject to operator policy and user consent, the 6G network shall support mechanisms for scheduling and provisioning network resources and computing resources in Service Host Environment for distributed AI inference service.
[PR 6.10.6-5] Subject to operator policy, the 6G network shall support mechanisms to ensure a guaranteed service performance of the distributed AI inference service.
NOTE: The UE is not involved in distributed AI inference service.
[PR 6.10.6-6] The 6G system shall be able to provide the communication and AI service for a user with the KPI requirements in Table 6.10.6-1 below.
Table 6.10.6-1: KPIs for home robot perform perception for general task in open environments via 6G network
Use case
Traffic type
Average packet size (Byte)
Transfer interval (ms)
Data Rate (Mbps)
Joint E2E latency (ms)
(NOTE 6)
Reliability
home robot perceiving overall context of the scene
(NOTE 5)
UL camera data
(NOTE 1)
[<1000]
[10]
[20-60]
(NOTE 2)
[150]
(NOTE 3)
[99.9 %]
for identifying individual objects in the scene
(NOTE 5)
UL camera data
(NOTE 1)
[<1000]
[10]
[20-60]
(NOTE 2)
[200-300]
(NOTE 4)
[99.9 %]
NOTE 1: 6 RGB cameras are equipped for robot “Figure 02” [180].
NOTE 2: 20 Mbps is for six 1080p cameras, and 60 Mbps is for four 1080p cameras plus two 4K cameras. Data Rate is calculated from video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, and around 24Mbps for 4K 30Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real time video uploading of a vehicle as per YD/T 4778-2024 [182].
NOTE 3: Based on psychophysical and neurophysiological studies [281], [282], human beings can perceive the gist (e.g. overall meaning) of complex visual scenes within around 150ms after stimulus onset. This rapid initial perception enables us to grasp the general context of a scene, even if the stimulus is briefly presented (around 10ms).
NOTE 4: For human beings, it takes longer for a human being to identify individual objects, e.g. finer object identification requires larger than 200 ms [283] and around 300ms [284].
NOTE 5: In the target scenarios, robots are expected to have similar perception time as an average human being.
NOTE 6: Joint E2E latency (i.e. round-trip communication latency, and AI inference latency in Service Hosting Environment), and UE is only considered to contribute to the communication service latency.
6.11 Use case on built-in Intelligent Communication Assistant
6.11.1 Description
Empowered by the rapid development of AI technology, the service providers are able to provide personalized and enriched services to their users when making daily routines within their homes, at their workplaces, in stores, at restaurants, as well as traveling for work or leisure. These kinds of personalized services are widely enjoyed by the customers. For example, a lot of countries are facing a major challenge in providing care support for senior citizens due to their rapidly ageing population and declining old-age support. The capability to introduce AI techs to provide more personalized and real time communication services would be a great help. Below we explain some of the advantages foreseen from different perspectives:
The voice service has been provided by the operators for a very long time and is widely used. The capability to understand and respond with natural language is one of the big advancements in AI tech. It’s practical and natural to introduce this kind of capability to enhance the current voice service offered by the operators and push their service offering portfolio to the next level.
The operators have a very solid identification and authentication & authorization system, as defined in TS 22.173 [59], operator’s IMS system can inform and protect terminating users from fraud attempts. Therefore, the operators are able to secure the management and utilization of the customized AI service, such as intelligent communication assistants. The operators are able to link the users and their intelligent communication assistants across the world. This gives the operators a unique position to provide a personalized, secure and trustworthy service for the client.
The operators have always been well complying with the user/data privacy related law and regulations. And users put their trust on the operators. Therefore, operators will have advantage in providing customized service with privacy aware solution such as intelligent communication assistant and it can be easily adapted to fulfil the various regulation frameworks in different parts of the world.
The operators have already had the capability to deliver AI related services to the users. Especially in real time communication service, operators are providing AI capabilities such as voice and video recognition, translation, media rendering, fun calling, and a spectrum of new services. Users can convey information and express emotions more easily and enjoy a superlative service experience [83]. And with the capability of IMS DC, operators are able to provide AI capabilities such as content sharing AR annotations, interactive menus and remote repair. Enterprises can improve communication efficiency and complete business transactions through calls [84]. Those services are gradually deployed by more operators [85], [86]. Currently, AI related services are realized through external AI capabilities, and the AI built-in service such as AI Assistant has not been fully explored.
Based on innovative technologies such as emotional interaction and artificial intelligence with perception, cognition, and even thinking abilities will gradually replace traditional interactive devices. Anthropomorphic artificial intelligence refers to AI that generates proactive intelligent interactive behaviours and can also perform emotional judgment and feedback intelligently. Anthropomorphic artificial intelligence will transform the relationship between humans and AI into one that is emotional, warm, and more equal, resembling human-like interaction. For example, a virtual personal assistant equipped with anthropomorphic artificial intelligence can observe the user's facial expressions, tone of voice, and body language in real time to respond in a more human-like and emotionally aware manner.
Overall, intelligent communication assistant provided by the operators natively is a customized service. It can interact with end users through voice, text, gesture or other modalities to provide enhanced experience. The assistant can be customized for each particular user by accessing user data or network data which are stored or collected in the network. With user’s consent, it can provide various communication services and support individual users based on user’s intention and requirement. The provided services include intent-based search, personalized recommendations, voice-controlled smart home devices, and interaction with various services (including 3rd party AI assistant or capabilities) or devices. The customization can be achieved by providing different levels of the intelligent communication assistant service, based on the authorization from the user for user data. While the customization includes services and/or capabilities (e.g. QoS management capability, traffic handling capability) provided by the operator network or third-party vendors.
NOTE: The user data could include the user’s customization requirement on intelligent communication assistant, user’s usage history of intelligent communication assistant, user’s address, user’s voiceprint etc. The format of user data is understood by intelligent communication assistant and is out of scope of 3GPP.
6.11.2 Pre-conditions
Ava subscribes to Operator EMo’s intelligent communication assistant service. When Ava subscribes to this service, Operator EMo asks her to provide some basic information:
- how she wants to use this service, such as voice recording for the work-related calls, daily life assistant (seat reservation, car rental, travel planning, and etc.), remote monitoring of the home devices and so on.
- the basic personal info to be used for customizing the intelligent communication assistant.
- her permission for the personal data collection, which may also be asked during the use of the service.
Ava chooses to use the intelligent communication assistant as a daily life assistant. She only gives limited permission to collect her personal data.
Mark, Ava’s boyfriend, subscribes to the intelligent communication assistant service provided by Operator AMo’ and gives more permissions for the operator to collect his personal data. The intelligent communication assistant could provide different level services based on the requirements and authorization of the customers. The intelligent communication assistant service will be more and more customized with wider authorization to access user personal data and the growing of the usage duration.
The third-party domain (e.g. the Internet) also provides AI assistant services (e.g. hotel AI assistant), the hotel AI assistant is used to manage the online booking orders and reserve desired rooms for the customers.
6.11.3 Service Flows
Figure 6.11.3-1: Built-in Intelligent communication Assistant
As shown in Figure 6.11.3-1, while walking back home, Ava remembers that she needs to discuss with her boyfriend Mark about the upcoming holiday plan. She makes a voice call to Mark, Mark is walking on the street and joining in the communication. Both Mark and Ava don’t want to show up in the call with the real image, therefore, they show up in the call with their digital avatar.
The conversation is about holiday plans, so Mark and Ava invoke their own intelligent communication assistants respectively for additional help via dialer app, gestures or voices etc. Each intelligent communication assistant will have a related identifier which can be used in authorization & authentication & routing (find each other), and it could understand the users’ intention and execute the instruction.
Once their intelligent communication assistants are activated, the intelligent communication assistants start to capture Mark and Ava’s current intention via voice/text/gesture.
Mark’s intelligent communication assistant captures Mark’s intention of traveling to Shanghai for new year vacation. Mark and Ava’s intelligent communication assistants may collect, store, share, process and use personal data (e.g. personal preferences, identity, etc.) for the intended travel. It checks the weather of Shanghai as well as the interesting locations, various transport facilities traveling to these locations internally or via a third party capability invoking by network API. The assistants can also invoke various network capabilities (e.g. sensing for autonomous vehicles, QoS management, etc.) on demand. Besides, due to Mark’s authorization, his intelligent communication assistant knows his daily schedule and generates the more accurate itinerary for the coming holiday.
With Mark’s and Ava’s consent, their own intelligent communication assistant can interact and collaborate with each other directly, to negotiate itineraries or plans that require both parties’s mutual agreement, then Mark’s intelligent communication assistant shares the itinerary to Ava’s intelligent communication assistant. These two intelligent communication assistants communicate with each other about potential choices before concluding the best holiday plan for Mark and Ava. When the two assistants reach an agreement, they send the agreed plan to Mark and Ava separately. If language translation is required during the call, e.g. Mark and Ava come from different countries, the operator’s network can invoke either its own or third-party’s language translation capabilities on demand.
Both Mark and Ava are happy with the travel plan and ask the intelligent communication assistants to arrange their holiday according to the travel plan, then they end the call.
The intelligent communication assistant finds out that there is no hotel AI assistants in the domain of its service provider (i.e. no hotel AI assistant is registered to the Operator network), and then discovers the address of a hotel AI assistant providing the online booking services and hosted in a 3rd party service provider domain. The intelligent communication assistant sends the hotel booking request to the 3rd party hotel AI assistant to book the room and securely provide the information, such as room type, check-in time, etc.
Both intelligent communication assistants try to arrange the travel: book the rooms, update the calendar apps in Mark and Ava’s terminal, and set up a reminder via SMS on the departure day.
6.11.4 Post-conditions
On the agreed departure day, Mark and Ava receive the reminder via SMS. They check-in to the hotel booked by the intelligent communication assistant via 3rd party platform successfully. At last, Mark and Ava enjoy their travel arranged by intelligent communication assistant.
6.11.5 Existing features partly or fully covering the use case functionality
TS 22.156 [28] defines the service requirements for the authentication of the digital assets, and intelligent communication assistant could be considered as user’s digital assets.
TS 22.101 [58] defines the service requirements for the ID of the application, and intelligent communication assistant could be treated as application.
The user to be identified could be an individual human user, using a UE with a certain subscription, or an application running on or connecting via a UE, or a device (“thing”) behind a gateway UE.
TS 22.156 [28] defines the service requirements for the avatar-based real-time communication. And TS 22.228 [138] define the service requirements for IMS. IMS supports different IMS multimedia applications. IMS supports a wide range of services, notably voice and video calls. There is extensive support for services, tightly integrated with the 3GPP system, with extensive support for roaming and integration with both PSTN and ISDN telephony, emergency services and more. The requirements for a 3D avatar application are largely covered by existing requirements in the 5G standard for IMS.
The requirements related to what is the intelligent communication assistant are not defined.
6.11.6 Potential New Requirements needed to support the use case
[PR 6.11.6-1] Subject to operator policy and user’s consent, 6G network shall be able to enable the IMS services to provide intelligent communication assistant service to users.
[PR 6.11.6-2] The 6G network shall support charging information collection for the intelligent communication assistant service.
[PR 6.11.6-3] Subject to operator’s policy and user’s consent, 6G network shall be able to enable the IMS services to support the interaction between different intelligent communication assistants, e.g. during an IMS calling service, both calling and callee parties are using intelligent communication services.
[PR 6.11.6-4] Subject to operator’s policy and user’s consent, 6G network shall be able to support the communication between operator’s intelligent communication assistant and authorized third-party AI assistant during IMS communication between users utilizing the intelligent communication assistant.
NOTE 1: It is not expected that intelligent communication assistants directly communicate with each other via IMS services.
[PR 6.11.6-5] Subject to operator’s policy and user’s consent, the 6G network shall be able to enable the IMS services to support the intelligent communication assistant to invoke operator’s native capabilities (e.g. AR rendering, XR rendering in the service hosting environment, SMS or voice, trigger QoS adjustment, Sensing) to meet user service requirements dynamically).
[PR 6.11.6-6] Subject to operator’s policy and user’s consent, the 6G network shall be able to enable the IMS services to support invoking authorized third-party capabilities (e.g. the weather inquiry, the language translation, the takeout services) and/or obtain various information from authorized third-party.
NOTE 2: Intelligent Communication Assistant: The virtual intelligent communication assistant locates in operator network and interacts with the users through voice, video, text, gestures or other modalities. The assistant can be customized for each particular user by accessing user data and network data which are stored or collected in the network, with user’s consent. It can provide various communication services and support individual users based on user’s intention and requirement utilizing AI capability. One subscriber can have one or more Intelligent Communication Assistants.
6.12 Use case on 6G System supporting AI model training service
6.12.1 Description
A third-party service provider can provide different kinds of excellent AI capabilities with AI models. However, it needs lots of resources (e.g. computing resources) and training data to train the AI model. For some third-party service providers, they may not afford the huge investment on the HW/SW resources for training the AI model, and they also cannot access the data generated/maintained in 6G system to train the AI model. Of course, based on the regulations of different countries network operator policy, after authorisation of the third-party service provider and with user consent and maintaining privacy as required by regional regulations for the use of their data, the 6G system can support the AI model for training and collect the data to train the AI model for the third-party service provider.
In this case, MNO can take advantage of 6G system resources (e.g. powerful computing resources) to provide the AI model training service, at the same time, the third-party service provider can obtain the AI model trained with efficient data (including data generated in the network) without deploying additional the expensive HW/SW resources.
6.12.2 Pre-conditions
In this case, MNO has service agreement with third-party service provider for providing AI model training for an AI capability. 6G system deploys the AI model training NF to support the AI model training service. 6G system may discover and select the data source entities in same PLMN or different PLMNs, or other network domain (e.g. vertical network).
6.12.3 Service Flows
The service flow is shown in Figure 6.12.3-1:
A third-party service provider requests 6G system to train the AI model, by uploading the AI model, and optionally the training input data.
AI model training service Function in 6G system determines to accept the request if the third-party service provider is authorized.
If authorized, AI model training service Function in 6G network validates the AI model received, selects which AI model training entity in the 6G network to train the received 3rd party AI model (e,g, when multiple AI model training entities exist) and forwards the received AI model to the AI model training entity.
If no training/validation dataset is received from the 3rd party AI model provider, the AI model training entity may identify and use stored training data if authorized to do so for the AI service provider. Authorization may be based on regulatory requirements or operator policy.
The AI model training service function transfers the received AI model for training to the selected AI model training entity and triggers the start of the AI model training using the training dataset.
On completion of the training the AI model training service Function validates the trained AI model and sends the trained and validated AI model to the third-party AI service provider.
Figure 6.12.3-1: 6G system supports AI model training service for 3rd party
6.12.4 Post-conditions
The third-party service provider obtains the trained AI Model, without deploying the additional training server.
6.12.5 Existing features partly or fully covering the use case functionality
TS 28.105 [139] relates to AI/ML model training, notably capturing the scope of existing AI/ML training support but strictly within the 5GS CN and RAN domains are considered, and aspects relating to AI model training for 3rd parties is not in scope.
In addition, TS 22.261 [14] specifies regarding AI/ML model transfer in 5GS in general, clause 6.40.1.
The 5G system can at least support three types of AI/ML operations: (including)
- AI/ML model/data distribution and sharing over 5G system
This case is limited to the transfer of models between device end-points and network end-points in a 5GS, not between network AI entities and 3rd party AI service for 3rd party AI service model training.
And,
Subject to user consent, operator policy and regulatory constraints, the 5G system shall be able to support a mechanism to expose monitoring and status information of an AI-ML session to a 3rd party AI/ML application.
NOTE 2: Such mechanism is needed for AI/ML application to determine an in-time transfer of AI/ML model
These requirements only relate to AI model transfer for ongoing AI service towards a device endpoint to support updating of new/alternative AI model for the service and does not support AI model transfer between network AI server and 3rd party AI service for AI model training. Additional monitoring capability to expose status of AI model training by the 6G System should be exposed to the 3rd party AI provider.
TS 28.105 [139] identifies supported functionality for ML model training and ML model validation in 5GS as,
4a AI/ML management functionality and service framework
- ML model training: training, including initial training and re-training, of an ML model or a group of ML models. It also includes validation of the ML model to evaluate the performance when the ML model performs on the training data and validation data. If the validation result does not meet the expectation (e.g., the variance is not acceptable), the ML model needs to be re-trained.
- ML model testing: testing of a validated ML model to evaluate the performance of the trained ML model when it performs on testing data. If the testing result meets the expectations, the ML model may proceed to the next step If the testing result does not meet the expectations, the ML model needs to be re-trained.
4a.1 Functionality and service framework for ML model training
An ML training Function playing the role of ML training MnS producer, may consume various data for ML model training purpose. …
The internal business logic of ML model training leverages the current and historical relevant data, including those listed below to monitor the networks and/or services where relevant to the ML model, prepare the data, trigger and conduct the training: …
Additionally,
6.2b.2.7 ML model validation performance reporting
During the ML model training process, the generated ML model needs to be validated. The purpose of ML validation is to evaluate the performance of the ML model when performing on the validation data, and to identify the variance of the performance on the training data and the validation data. The training data and validation data are of the same pattern as they normally split from the same data set with a certain ratio in terms of quantity of the data samples. …
6.2b.2.9.2 Performance indicator selection for MLmodel training
The MnS producer for ML model training needs to provide the name(s) of supported performance indicator(s) for the MnS consumer to query and select for ML model performance evaluation. The MnS consumer may also need to provide the performance requirements of the ML model using the selected performance indicators.
The MnS producer for ML model training uses the selected performance indicators for evaluating ML model training, and reports with the corresponding performance score in the ML model training report when the training is completed. Note, test data and validation data is required to check the performance of the trained ML model meets expectations.
The network as a part of its AI model training exposure may inform the 3rd party AI server of the validation performance parameters it supports, and the 3rd party AI server may then provide, in its training request, it’s selected validation performance parameters to use to assess the expected validation testing of the trained model.
In summary, the above requirements do not support the 6GS acting as an AI model training service to 3rd party, for 3rd party AI model training. New requirements below are presented for this use case.
6.12.6 Potential New Requirements needed to support the use case
[PR 6.12.6-1] Based on operator’s policy the 6G network shall be able to securely provide the trained AI/ML model between the Service Hosting Environment and the authorized 3rd party.
NOTE: It is up to the 6G network to determine the AI/ML model training method, e.g. centralized or distributed.
[PR 6.12.6-2] Based on operator’s policy the 6G network shall support requested training for an AI/ML Model provided by an authorized 3rd party in the Service Host Environment based on a dataset provided by the authorized 3rd party requesting AI/ML model training.
[PR 6.12.6-3] Based on operator’s policy the 6G network shall ensure required privacy protection on the training dataset used in the Service Host Environment e.g. whether the dataset is from either the 6G network or a training dataset provided by the 3rd party requesting AI/ML Model training.
[PR 6.12.6-4] Based on operator’s policy the 6G network shall be able to expose training validation performance parameters to the authorized 3rd party to enable selection of validation performance parameters for the 6G network to use to validate the requested 3rd party AI/ML model training.
[PR 6.12.6-5] Based on operator’s policy the 6G network shall be able to expose to the 3rd party the ML model training validation performance report, for the requested 3rd party AI/ML model training.
6.13 Use case on network knowledge as part of Retrieval Augmented Generation for Generative AI
6.13.1 Description
Generative AI is an approach that aims at creating a new content of different kinds mimicking the characteristics of the training data. One of the types of Generative AI is LLM, which is based on the natural language/text and is used to generate plausible language as output based on the user’s query.
In addition to the normal LLMs that use language as input and output data modality, there are also Large MultiModal Models supporting multiple data modalities, e.g. text, audio, visual, etc. and can be used for AI Generation.
RAG is an approach for improving the quality of LLM-generated outputs by enabling the model to access the knowledge sources beyond training data, thus supplementing the model’s internal knowledge obtained during training. It consists of two phases, cf. Figure 6.13.1-1:
1) Retrieval phase: In this phase the search and retrieval of information snippets of most relevance to the user’s prompt is done. This may be performed by different algorithms e.g. similarity scoring with cosine calculation of the encoded user input and documents in the external knowledge source [261]. The retrieved external information is appended to the user’s prompt and given to the model.
2) Generation phase: In this phase the LLM leverages on its generative capabilities by jointly processing the augmented prompt and original prompt during the retrieval phase in order to provide the final output to user prompt.
RAG ensures that the LLM has access to the most relevant and up-to-date facts important for output generation, thereby improving the quality of the output. In addition, the RAG approach lowers the costs, including the energy consumption of updating the LLM model (e.g. with respect to re-training/fine-tuning using large amount of data being performed continuously, periodically or over extensive time windows).
An imminent use case envisaged in 6G era is an application or Generative AI supporting XR service for city sightseeing. The XR user can enjoy a glimpse into an immersive experience as if the XR user were visiting the city and places in favour, e.g. museum, gallery, concert and sport event, etc. The XR user can also learn in advance if mobile data connectivity is available for his or her own mobile devices and if any additional constraint should be taken into account, e.g. due to roaming. MNO’s network knowledge can be leveraged to get the information on the network conditions in order to optimize XR content generation and seamless delivery to user, according to user’s preference and request (i.e. user prompts), e.g. inquire recommended sites, routes to get there, etc.
Figure 6.13.1-1: Retrieval Augmented Generation for LLM
Table 6.13.1-1: Potential sustainability impacts of the use case
(the UN SDGs/GDC matching goals of each aspect within 3GPP context)
Potential benefits of the use case (added value)
Potential areas of attention of the use case (risks to be mitigated)
Environmental sustainability aspects
(UN SDGs 12, 13, 14, 15 and indirectly 6, 7 & 11 [87])
(UN GDC “Develop principles for environmental sustainability of digital technologies” [88])
Energy resources
(UN SDG 7, 11, 12)
Reducing energy consumption by enabling high quality and up-to-date model outputs without need of energy demanding re-training or fine-tuning, but by providing access to the most relevant and up-to-date network knowledge.
Potentially higher energy consumption to realize the retrieval augmented generation using network knowledge sources.
Emissions
(UN SDG 6, 7, 11, 12, 13, 14, 15)
Enabling CO2 emission reduction due to the benefit of reduced energy consumption.
Potential CO2 emission as a result of realization of retrieval augmented generation using network knowledge sources
Socio-economic sustainability aspects
(UN SDGs 2, 3, 4, 5, 8, 9, 10, 11, 16 & 17 and indirectly 12 [87])
UN GDC “Closing Digital Divides and Accelerating SDG Progress”
&
“Expanding Digital Economy Inclusion”
&
“Fostering an Inclusive, Safe Digital Space” [88])
Inclusion & Equality
(UN SDGs 11, 10, 4, 5 and indirectly 3, 16 & 17)
By better taking into account the user current and future context (e.g. network knowledge including predictions) to augment GenAI prompts, the application will better adapt to their conditions (e.g. expected coverage, QoS) thus their digital inclusion.
Trustworthiness
(UN SDGs 11 and indirectly 3 & 17)
Increasing trustworthiness by grounding the models on the most reliable and up-to-date knowledge sources for generating the outputs.
Risks of knowledge source misuse or breach, e.g. by unauthorized consumers.
6.13.2 Pre-conditions
An MNO deploys one or more network knowledge sources at various locations, which are used as part of RAG to augment the user prompts towards Generative AI models as part of applications or agents supporting different services, e.g. consumer XR services, vertical industry services such as production lines or site inspection etc.
Subject to MNO’s policy, network knowledge sources are always in-operation and offering different knowledge for augmenting the Generative AI prompts related to different services when requested by the users. Such knowledge sources can be of different types, containing different network data, e.g. from different network domains, etc, located in different areas and owned by different parties, e.g. MNO or Application Service Providers (ASPs). For example, one knowledge source could contain rather static information on roaming conditions and agreements, availability of different technologies, etc., another knowledge source could contain more dynamic information from management domain, e.g. on network performance in different areas, information on faults or coverage issues, further knowledge source could contain predictions regarding the network performance etc. Furthermore, the knowledge sources may be associated with certain restrictions or recommendations for usage as well as the cost, e.g. in terms of delay in providing the generated output or monetary cost of using the knowledge source.
A user subscribes to application services (e.g. Generative AI based XR applications or agents, or vertical industry services etc.) provided by the MNO and/or an ASP. The ASP has an agreement with the MNO to leverage MNO’s network information as RAG knowledge source(s), which are being exposed by the MNO. For instance, Alice subscribes to an XR service for city sightseeing supported by application or agents powered by Generative AI models from MNO and/or ASP. Alice is in the train on her way from Munich to Paris, where she will spend a weekend sightseeing in the city.
6.13.3 Service Flows
1. The user invokes the subscribed application or agent and requests the Generative AI based service to MNO’s or ASP’s application host. The user prompts the Generative AI model, e.g. LLM as part of the invoked application or agent. E.g. Alice invoked the XR application or agent for city sightseeing. She requests via user prompts to Generative AI insights on history, geographical, population aspects as well as the recommendations on city highlights to visit, including the XR preview of recommendations (e.g. museum and gallery visits, concert or sport events visits).
2. Upon the user prompt, the Generative AI model, supporting the application or agent and present inside or outside MNO’s network, invokes the RAG approach to access the MNO’s network information as knowledge source in order to provide most up-to-date and reliable output (e.g. answer or action) to user prompt.
3. MNO’s network provides the application’s Generative AI model with information about the available knowledge sources that can be used as part of RAG. Such information may include description of the knowledge sources, type of knowledge contained, including different network data, from different network domains, address or area where the knowledge is stored, entity owning the knowledge source, description on usage restrictions, associated cost, etc. For instance, the exposed information includes roaming conditions and agreements in the network that may impact the way Alice will be charged on the way from Munich to Paris, coverage map, known coverage issues in the network, e.g. due to geographical circumstances, high mountain peaks, tunnels, availability of different access technologies, on the way such as 3G, 4G, 5G, etc., 3rd party knowledge such as public transportation schedule in Paris, etc.
4. The application or agent chooses whether to use the available knowledge sources from MNO’s network or not to use them based on the exposed information, e.g. constraints and costs.
5. If the application or agent chooses to use the available knowledge sources from MNO’s network, the application selects the desired knowledge source to be used for augmenting the output generation by the Generative AI model, supporting the application. E.g. the XR application or agent from Alice, selects the knowledge source containing the MNO’s network information related to e.g. roaming conditions and agreements, coverage map, known coverage issues, availability of different access technologies, network analytics etc. In addition, the application can select how and to what extent the knowledge source should be used, e.g. in terms of the target and maximum amount of data that should be retrieved for generating the output.
6. The application or agent retrieves from the knowledge source the information that is the most relevant for Alice and given sightseeing task, e.g. knowledge related to different areas on the way from Munich to Paris.
7. The MNO’s network enables the required measurements related to knowledge source usage by the application, further monitoring if the available knowledge sources were used for augmenting the output generation and deriving the related statistics
8. The MNO’s network provides the reports on conducted measurements and statistics related to usage of available knowledge sources towards the ASP, further charging the ASP on a pay-per-use basis.
6.13.4 Post-conditions
The user can enjoy optimal user experience as the Generative AI output (e.g. answers or actions) as part of the application or agent were taken based on most up-to-date and reliable knowledge sources from the network.
In this example, the outputs from Alice’s XR application or agent may contain the actions on content transmissions which counteracts up-to-date information on unfavourable roaming conditions, known or predicted coverage issues. For some other examples, such as for vertical industry applications, the output may contain the fact of pausing or taking out of production lines some machines that need maintenance, initiating software updates if needed, based on up-to-date troubleshooting tickets resolution records etc.
In addition, the MNO and ASP can contribute to save energy consumption by means of the on-demand knowledge augmentation of Generative AI supporting the user application or agent, without the need for energy consuming processes, such as re-training and fine-tuning of the models.
Nevertheless, in order to leverage RAG approach the application or agent needs to retrieve information on-demand, from the knowledge source exposed by the 6G network, where such retrieval phase may encounter additional delays in getting the outputs.
6.13.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] has defined requirements and KPIs for AI/ML model transfer, but not for RAG use cases.
Network knowledge is already widely available throughout the network in 5G (e.g. analytics provided by NWDAF, network APIs) and exposed to authorized 3rd parties (e.g. via NEF). However, in a 6G network, GenAI prompt augmentation may benefit from knowledge coming from various sources to be provided all at once, in a timely manner.
6.13.6 Potential New Requirements needed to support the use case
[PR 6.13.6-1] Subject to operator’s policy, the 6G network shall be able to expose information from the network to authorized 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents by means suitable for prompt augmentation.
NOTE: Prompt augmentation is an approach of adding further information, or instructions, to a prompt in order to enhance the AI-generated response, e.g. in terms of quality or relevance.
6.14 Use case on intelligent UAV swarms
6.14.1 Description
UAVs, with their agility and rapid deployment, have become indispensable tools in public safety scenarios such as earthquakes, fires, and floods. Equipped with cameras, sensors, and lightweight computing devices, UAVs can acquire real time images and environmental data of the disaster areas and execute the tasks scheduled by the local or remote-control system, such as surveillance, monitoring, and detecting target object in specific locations. As the application of AI, those intelligent UAVs with AI capabilities (e.g. built-in AI models, AI Agent) can be more powerful in perception, decision-making and control, and can perform more complex tasks such as initial target recognition and dynamic path planning, etc.
The data collected by a single UAV is inherently limited, and the situation gets worse when the disaster area is large. Therefore, it has become a trend to use UAV swarms to achieve full coverage on the disaster area and execute complex tasks together with mutual collaboration. However, the coordinated operation of multiple UAVs working in swarms raises challenges to the whole UAV control system. For instance, the capabilities of compute, AI, and energy endurance vary among UAVs of a swarm, so that they may have diverse abilities in data processing locally. Additionally, each UAV will face differentiated wireless conditions at its location, which may impact its achievable transmission QoS. Therefore, when the control platform assigns a task to the UAV swarm, the worst UAV will be the bottleneck of the overall performance, which may be experiencing an overloaded local computing. For the UAV swarms, besides the legacy communication service, sensing service, the 6G system is expected to assist the control platform or UAVs to coordinate the computing, AI services and communication to achieve the expected performance efficiently.
When a certain UAV is unable to complete the task with expected QoS due to the limited AI or computing capabilities, the 6G network can help the UAV to coordinate other UAVs or edge servers with proper AI capability to execute the tasks instead or together by data sharing via inter-UAVs or end-edge-end. On the other hand, the 6G system can leverage the computing resources and capabilities in 6G networks to offload the workload of UAVs, gather the data from different types of UAVs and the environmental data sensed by the base station, deploy complex AI models, and infer comprehensive and intuitive decisions for command and rescue.
This use case illustrates how a 6G system assists the control system and UAV swarms to complete AI based tasks as Figure 6.14.1-1 depicts.
Figure 6.14.1-1: Intelligent UAV Swarms
6.14.2 Pre-conditions
UAV#A, #B, and #C are equipped with cameras and sensors for images and environmental data collection and processing components with limited computing capability while UAV#D is equipped with high-performance processing components, and AI models for thermal image recognition.
Edge server #E is an operator managed server in Service Hosting Environment, provisioned with the AI model for the generation of high-definition 3D maps.
All the UAVs are deployed around the disaster area, and have the subscription for communication, sensing, and computing and AI services.
All the UAVs have been registered in the 6G network with the capabilities.
6.14.3 Service Flows
The control system receives messages indicating that people are trapped in the damaged library, and immediately initiates a mission of search for the trapped people and sends it to the nearby UAV #A.
UAV #A analyzes the mission and identifies the lack of AI models and environment data for thermal image recognition, so it requests 6G network for coordinating the selection of proper UAVs for AI inference and invoking sensing service.
With the help of 6G network, UAV #D is discovered based on the information of computing capability, AI capability, power consumption, and location.
UAV #A sets up the connection with UAV #D to transmit the collected images and environment data. Also, the sensing results and contextual information requested for UAV#A can be exposed to UAV#D from 6G network.
UAV #D infers the detection of trapped people based on local AI models for thermal image recognition and the data provided by UAV #A and 6G network. It then feedbacks the inference result to UAV #A.
UAV #A feeds back the presence, number and rough location information of the trapped people to the control system.
The control system formulates the rescue plan and initiates a mission to acquire the precise 3D location of the trapped people and send it to UAV #A.
UAV #A analyzes the mission and identifies the lack of the data for a certain height, and the lack of AI models and sufficient computing resources for the generation of high-definition 3D map. Regarding further interaction with the control system, it discovers other UAVs (e.g. UAV#B, UAV#C) covering the target 3D location. Then it sends the request to 6G networks for AI service and/or sensing service for the remaining task.
The 6G core network selects Edge Server #E based on the request of AI service and informs UAV #A and Edge Server #E of the coordination result.
UAV#A sets up a connection to Edge Server #E to transmit the collected images and environment data. Meanwhile, the 6G network processes the sensing data and transmits the sensing result and the data from UAV#A, UAV#B and UAV#C for the specific location to Edge Server #E.
Edge Server #E generates high-resolution 3D maps for the trapped people based on the data from UAV#A and the network. Then it sends the 3D map to UAV#A.
UAV #A feedbacks the precise location with 3D map to the control system.
NOTE: The term “6G core network” used in this service flow does not imply any architectural assumption, e.g. whether 6G core network is a new or evolved core network (compared to 5G).
6.14.4 Post-conditions
The rescue teams find and rescue the trapped people based on the precise location and 3D maps.
6.14.5 Existing features partly or fully covering the use case functionality
In TS 22.261 [14] clause 6.40.2.2,
Based on user consent, operator policy and trusted 3rd party request, the 5G system shall support a means to authorize specific UEs to transmit data (e.g. AI-ML model data for a specific application,) via direct device connection in a certain location and time.
6.14.6 Potential New Requirements needed to support the use case
[PR 6.14.6-1] The 6G system shall be able to collect charging information for the computing resource(s) coordinated by the 6G network e.g. per request, per UE.
[PR 6.14.6-2] Subject to operator’s policy, the 6G network shall support mechanisms for a 3rd party AI-based application on UE (e.g. UAV) to invoke an AI service upon request.
[PR 6.14.6-3] Subject to the operator’s policy, the 6G network shall support the selection of computing resources in the Service Hosting Environment upon request from AI-based applications.
6.15 Use case on 6G system assisted target object detection
6.15.1 Description
Figure 6.15.1-1: 6G system assisted data processing for a task
Aside from 6G system providing a data transmission (3GPP communication service), on top of the connectivity, some servers in the 6G CN or operator managed data network (DN) (e.g. edge compute domain) may be connected to provide data processing using its local computation resource. As Figure 6.15.1-1 (above) shows, UE and one or more nodes in 6G CN, operator managed DN (e.g. edge compute domain) and AS are involved. The 6G network can guarantee the overall latency considering the apportionment of communication latency as well as the processing latency.
NOTE: The 6G system may adjust the resource of the processing server in the 6G CN or in the operator managed DN (e.g. edge compute domain) considering the transmission latency to satisfy the needed overall joint latency (communication latency plus processing latency).
This contribution introduces a use case where the data processing is performed by 6G system for a target object detection task.
6.15.2 Pre-conditions
Figure 6.15.2-1: 6G system assisted target object detection
As shown in Figure 6.15.2-1, the UE (vehicle), RAN node, Core network and Edge Server are involved together for performing a target object detection. Each of them performs the specific task as below.
Vehicle:
support Light Detection And Ranging (LiDAR) sensor to sense the target car. It generates data in point-cloud format and transmit the data to RAN node;
RAN node:
support RF sensor to sense the target car. It generates the measurement data in point-cloud format and sends the data to core network.
NOTE: The RAN node has an overview information about the target and the vehicles surrounding the target car can get the detailed data about a specific direction to the target, which helps increase the inference accuracy.
Core network:
The core network is installed with aggregation model, which is used to process the input data by aggregating the data from UEs and RAN. Then transmit the aggregated data to the Edge server supporting inference model for Car positioning.
Edge Server:
Support the inference model. Perform the inference result of the shape and position of the target object based on the aggregated data from core network and the video data from the infrastructure camera.
6.15.3 Service Flows
0) the aggregation model and inference model have been offline trained in cloud server and provisioned into CN node and edge computing server in 6G system.
1) When a car collision happens, the 6G system selects three vehicles which are surrounding the target object (i.e. the collided cars) and capable of doing LiDAR sensing service [89]. As example, the selection may consider one or more of the aspects: capabilities and computation resource of UEs and/or networks, the network congestion status, and the certain task request (e.g. the accuracy of a sensing request). Then, the vehicles generate the LiDAR sensing data meanwhile the RAN node generates the RF sensing data, both the vehicles and RAN node transmit the data in point-cloud format. The serving core network node (e.g. connected behind the user-plane function) generates the output (aggregated data) using aggregated model based on the measurement input from vehicles and RAN node.
2) The edge computing server received the video data sent by the infrastructure camera and received the aggregated data from CN node. The edge server then does the inference using the inference model to derive the accurate shape and position of the target object.
3) The edge server generates the output i.e. inference result, which could be updated into High Definition (HD) map for auto-driving service. The edge server may send the updated HD map to all vehicles so that the vehicle can perform the correct driving decision based on the updated HD map [90].
6.15.4 Post-conditions
Thanks to the in-time detection of the target object, it can quickly inference the accurate shape and position of the collided cars so that the HD map can be updated in time.
6.15.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] clauses 6.40 and 7.10 describe the communication functionality and KPIs for AI/ML model transfer in 5GS (including UE member selections for Federated Learning (FL)). Instead of assuming to be performed as a mere transmission pipeline, the 6G system may get involved for an AI/ML operation by providing computation resources, data, model and enhanced connections.
The existing requirements for AI/ML Model Transfer are assuming the AI application is from 3rd party and the data processing is done in application. However, this new use case is to address MNO owned service such as sensing in which the data is generated within 3GPP system, thus it needs UE and 3GPP network (i.e. Core Network) to get involved of the data processing.
Thus, it requires more functionality and KPIs for 6G network to perform AI/ML operation as a native AI service.
6.15.6 Potential New Requirements needed to support the use case
[PR 6.15.6-1] Subject to operator policy, 6G network shall be able to support selection of computing resources in Service Hosting Environment for 6G Computing Service.
[PR 6.15.6-2] Subject to operator policy, 6G system shall be able to guarantee the overall E2E latency (i.e. latency of communication service, and latency of 6G Computing Service) requested by the application (e.g. target object detection).
NOTE: For [PR 6.15.6-2], UE is only considered to contribute to the communication service latency.
6.16 Use case on energy of the system intelligent management
6.16.1 Description
Considering the energy of the 6G system, it is crucial to have an environmentally and economically sustainable system. By managing and optimizing the energy of the system, operators can reduce energy consumption, improve efficiency, contribute to environmental sustainability, and reduce operational costs
By leveraging the latest trends in artificial intelligence and machine learning, the collection of energy metrics can be enhanced, energy efficiency optimized, and the system's energy usage controlled. Utilizing renewable energy sources can significantly reduce energy consumption and improve energy savings, but other alternatives may be possible in the future.
6.16.2 Pre-conditions
A sustainable operator aims to operate and maintain an energy-efficient system. To achieve this, it is crucial to gather comprehensive information on the system's energy consumption and performance. By collecting detailed data, the operator can identify areas for improvement, optimize energy usage, and implement strategies to enhance overall efficiency. This proactive approach not only reduces operational costs but also contributes to environmental sustainability by minimizing the system's carbon footprint.
The operator is aware of the latest trends in artificial intelligence and machine learning and aims to enhance operation and maintenance procedures by applying intelligence and learning. This approach will create one or more AI/ML models that efficiently will manage the overall energy consumption and efficiency of the entire system.
6.16.3 Service Flows
The system is monitoring and controlling the energy of the system continuously.
6.16.4 Post-conditions
The management of the energy of the system is more efficient, environmentally, and economically sustainable.
6.16.5 Existing features partly or fully covering the use case functionality
None.
6.16.6 Potential New Requirements needed to support the use case
[PR 6.16.6-1] The 6G network shall support the use of AI for the OAM of the 6G network, to assist with its energy efficiency and carbon emissions reduction.
6.17 Use case on intelligent communication assistant
6.17.1 Description
Operators have provided voice and video services for a long time and a lot of users are using these services. The usage of data channel defined in TS 26.114 [145] enables the operators to provide more and more interactive services to the users, like for example, the real time translation, video recognition, see GSMA NG.129 [144].
With the rapid development of AI technology, the operators could provide more and more intelligent services based on the basic voice/video/data channel service, such as the capability to understand and interact with natural languages to enhance the user experience.
On the other hand, leveraging the power of AI technology, the personal AI assistants are becoming essential and powerful tools for users (e.g. Alexa®, Siri®). Currently, most of the personal AI assistants are provided on the terminals (e.g. smart phones). However, the limitation of the power and thermal factors are the bottlenecks of the AI assistant development on terminals.
Operators are highly possible to provide the Intelligent Communication Assistant services. The Intelligent Communication Assistant provided by the operators is an always-on and each subscriber will have his/her own personal AI assistant. The Intelligent Communication Assistant could understand user intention by collecting multi-modal data of the user and execute the user instructions by invoking network services and the services provided by the 3rd party service provider.
The Intelligent Communication Assistant provided by the network is trusted. The operators must obtain user authorization to use the personal data and provide security protection for collected data. In addition, operators have solid authentication and authorization mechanism for the identification to protect the data transmission.
For the 3rd party service providers like taxis and hotels, they could also register its AI assistant service to the networks so that the personal AI assistants could discovery the 3rd party AI assistant. The personal AI assistants could communicate with the 3rd party AI assistant to provide services to the users.
Table 6.17.1-1: Potential sustainability impacts of the use case
Sustainability Handprints
(benefits)
Sustainability Footprints
(costs)
Environmental
Function integration eliminates the need for multiple dedicated application providers with individual functions.
Increased material consumption for new service and network deployment
Energy consumption to support AI/ML
Social
Increased availability of human-centric services and the quality of life.
The introduction of personal AI assistants in communication can help the end users in their formalities and everyday life, facilitating the access to services and remove the barrier of complexity of some tasks or formalities, which could have deterred the end user.
Privacy risks from understanding humans
Potential risks for trustworthiness/safety in case of hacking.
Economic
New business opportunities with inclusive product or service offerings, improving accessibility and meeting the specific needs of various groups, including people with disabilities, children and seniors.
Economic efficiency improvements and reduced costs for human-centric services.
Safety degradations from new risks related to new services
6.17.2 Pre-conditions
Alice subscribes to Operator’s EMo’s Intelligent Communication Assistant service which is provided by the operator’s network, and the network allocates a digital identifier for Alice’s personal AI assistant. The network asks her permission to collect the personal information to provide these services. Alice accepts.
The taxi company registered its AI assistant service to Operator EMo’s network. The AI assistant of the taxi company is used to manage the e-trailing services and communicate with their platform to assign taxies for the customers.
6.17.3 Services Flows
1. Alice is going to take a flight for a business trip, and she calls her AI assistant to book a taxi after landing.
2. Alice's Intelligent Communication Assistant captures the requirements of Alice, asks for the authorization for collecting the private information such as flights and communicating with other AI assistants during the flight.
3. Alice confirms the authorization.
4. The AI personal assistant of Alice checks the flight information, monitors the actual take-off and landing time of the flight.
5. Alice's Intelligent Communication Assistant discovers a 3rd party AI assistant providing the e-trailing services in the network.
6. Alice's Intelligent Communication Assistant sends a request to the 3rd party AI assistant to book a taxi and provide the information such as the origin, destination, estimated departure time.
7. The 3rd party AI assistant allocates a taxi for Alice and informs Alice's Intelligent Communication Assistant that the request has been accepted and sends the taxi information, i.e. driver's license plate number, mobile phone number and real time location to her.
8. After landing, the Intelligent Communication Assistant of Alice confirms that the driver has arrived at the airport according to the real time location and navigates for Alice.
6.17.4 Post-conditions
Alice finds the taxi and takes the taxi to her destination.
6.17.5 Existing features partly or fully covering the use case functionality
The service requirement for the authorization of the digital assets defined in TS 22.156 [28] applies to the Intelligent Communication Assistant as it could be treated as user’s digital assets.
6.17.6 Potential New Requirements needed to support the use case
[PR 6.17.6-1] Subject to regulatory requirements and user consent, the 6G system shall provide suitable means for 6G network (e.g. specifically core network or service hosting environment) to provide AI services (e.g. the Intelligent Communication Assistant services) to the subscribers.
[PR 6.17.6-2] The 6G system shall provide mechanism to collect the user consents for the secure exchanges of personal data with the AI assistant inside the network or (third party) service hosting environment.
[PR 6.17.6-3] The 6G system shall provide mechanisms for registration and discovery of AI-based services provided by authorized 3rd party service providers (e.g. 3rd party providers of Intelligent Communication Assistant service).
6.18 Use case on exposing achievable QoS to aid computational resource selection
6.18.1 Description
6G is envisioned as a key enabler of pervasive AI, with applications ranging from enhanced mobile experiences and personalized services to critical and real time applications in areas like autonomous systems, healthcare, and industrial automation. These applications impose stringent QoS requirements that arise from the diverse nature and criticality of these applications, demanding high data rates, ultra-low latency, extremely high reliability, and high computational capabilities.
This use case scenario is related to supporting AI-driven distributed inference for real time applications [146]. The computing resources available on UE may not be enough to run the AI model, which can give inference with the desired accuracy. Hence, UE can offload the inference task to a bigger AI model requiring more computational resources available on edge servers. However, offloading comes with latency, which the UE needs to be aware of to meet the QoS guarantees.
It is assumed that the compute resources are embedded at different locations in the network, say, on the UE (device) itself, or in the edge. These compute resources may have different capabilities; for example, a small-sized Deep Neural Network (DNN) may be deployed on the UE, and a full-fledged DNN in the edge server(s).
An AI entity on the UE needs to make an inference on a new task. Depending on the complexity of the task, it can decide whether to complete the inference with a small AI model deployed on the UE or use a bigger AI model deployed at the edge servers with more computational resources.
This use case illustrates how the 6G network can support the user in making QoS-aware decisions on selecting computing resources to fulfil their AI inference requirements.
6.18.2 Pre-conditions
A UE has received a task to perform inference. The AI entity has flagged it as a complex task and requires more computational resources to run a larger AI model. These computational resources are available on an edge server. Before offloading the task to the edge server, the potential end-to-end latency incurred in receiving the inference from the edge server must be known at the UE to check if it meets the required QoS for AI Inference.
6.18.3 Service Flows
As shown in Figure 6.18.3-1, the AI entity at the UE has decided that the task at hand is complex and the computational resources on the UE are not enough to obtain good inference. It likes to use computational resources on the MEC server and needs information that reveals the potential end-to-end latency incurred in completing the inference at the MEC server. The network exposes the information in real time QoS (end-to-end delay between UE and MEC), which can be achieved by offloading the inference task to the MEC server. Based on this information, the AI entity at the UE decides if the inference can be completed at the UE or at the MEC server based on its QoS requirement.
Figure 6.18.3-1: QoS-aware computational resource selection by UE
6.18.4 Post-conditions
The user is able to select the MEC server to carry out the computations based on its requirements.
6.18.5 Existing features partly or fully covering the use case functionality
None.
6.18.6 Potential New Requirements needed to support the use case
[PR 6.18.6-1] Subject to operator’s policy, the 6G network shall provide a means to expose the communication QoS information (e.g. latency) between a given Service Hosting Environment and a UE to an authorized 3rd party for providing AI services to the application on the UE.
6.19 Use case on AI-based video analysis
6.19.1 Description
Some of the tasks traditionally performed by humans are expected to become more efficient, accurate, and ultimately automated by using AI. One example of these tasks is AI-based video analysis. More specifically, social infrastructure that has traditionally been inspected by the human eye, such as utility poles and guardrails, can be made more efficient using AI-based video analysis. Alternatively, analysing footage from in-car cameras with AI-based video analysis applications can notify drivers or remote operators in real time about the approach of people or objects. In these scenarios, video data analysis may require significant processing resources, making it difficult to perform all processing solely with the resources of the devices. Therefore, it is considered useful for mobile networks to offload AI-based video analysis tasks to the network. This use case introduces the requirements for supporting these scenarios within the 3GPP system.
6.19.2 Pre-conditions
X is a vehicle equipped with cameras to capture the environment outside the vehicle while driving on roads for infrastructure inspection. A UE capable of accessing operator Z’s network is also equipped in it and always moving when X is driving.
Y is a business entity conducting infrastructure inspections and owns X as a vehicle for this purpose. Y holds a contract with operator Z and is provided with compute offload services utilizing resources within Z’s network based on requests to operator Z.
Z is a mobile network operator offering compute offload services utilizing resources within its own network.
6.19.3 Service Flows
1. Car X, equipped with cameras to capture the environment outside the vehicle, is on the move. As it drives, X records footage of infrastructure such as utility poles, traffic lights, and guardrails around the roads.
2. Y, the operator of X, launches a real time infrastructure inspection application utilizing AI-based video analysis.
3. The infrastructure inspection application provider requests a compute offload service to mobile operator Z.
4. Mobile operator Z, upon receiving the request, sets up the routing between the UE on X and the resources within the network and initiates processing resources to provide the compute offload service.
5. X transmits the camera footage via UE to the resources within operator Z's network, where AI-based video analysis is performed. The results of the video analysis are sent to Y, and if any anomalies are detected, prompt action is taken.
6.19.4 Post-conditions
Y was able to apply AI-based video analysis to real time infrastructure inspections by leveraging network-based processing resource offloading.
6.19.5 Existing features partly or fully covering the use cases functionality
Solution for QoS modification based on communication requirements from application is specified in clause 4.15.6.6 of TS 23.502 [30].
6.19.6 Potential New Requirements needed to support the use case
[PR 6.19.6-1] The 6G network shall support reselection of computing resources in Service Hosting Environment for third party application workload offloading from the UE to Service Hosting Environment (e.g. where AI inference is performed based on video input from UE), considering UE mobility.
6.20 Use case on smart housekeeping
6.20.1 Description
6G system could help to keep the family daily care and security, requiring advanced automation and management capabilities to maintain a comfortable and efficient living space. There will be more AI related applications and intelligent devices (e.g. robots, UAVs, autonomous vehicles) in the 6G era. Users will be able to express their requirements through natural language to convey their needs. In certain scenarios, multiple devices will need to collaborate to complete complex tasks. The 6G system can dynamically coordinate devices based on user's supply and demand requirements.
6.20.2 Pre-conditions
End devices with different capacities and statuses are deployed in Leo's home, primarily serving as comprehensive home assistants that manage household security, automate daily routines, and optimize energy consumption. Beyond these household functions, the system also provides specialized care support for Leo's retired parents and two babies such as monitoring health conditions, delivering medication reminders, and ensuring child safety through continuous supervision.
Leo has subscribed to the intelligent service from Operator A and had his various UEs registered in 6G core network. The 6G system has been authorized to send commands to these UEs based on Leo's requirements, including location movement, resource scheduling, information request, etc.
6.20.3 Service Flows
Leo’s family decides to have BBQ on Tuesday evening, Leo sends his plan and requests (e.g. prepare a BBQ party safely) to Operator A though AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. Based on Leo’s intent Operator A decides which 3GPP services to provide to Leo.
With the help of 6G network, the tasks (understanding by AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) are arranged to Leo’s housekeeping devices (including one robot, smart appliances, one smart car, and intelligent environment sensors).
At the same time, the 6G network keeps providing services to Leo to support the appliances’ tasks (e.g. cleaning, furnishing).
The environmental monitoring camera detects the wobbling grill and immediately reports the potential hazard to the 6G network. The 6G network allocates specific tasks to available UE devices in the house. The garden patrol UAV is assigned aerial monitoring duty, a nearby robot receives stabilization instructions with precise path planning, and a fire-assistant robot is prepared for potential coal scatter containment.
All devices execute their tasks with 6G network coordination. The robot approaches the grill guided by positioning data (provided by 6G network) and UAV visual feedback, stabilizes the structure using the optimal technique recommended by the AI service, while the fire-assistant robot safely collects any displaced hot coals. The coordinated operation successfully resolves the hazard through seamless multi-device collaboration enabled by 6G network services.
6.20.4 Post-conditions
After the BBQ preparation task is completed, the resource including both communication and AI/ML resource which has been occupied by devices involving the BBQ preparation will be released. The housekeeping devices will keep assisting Leo’s parents taking care of his daughter.
6.20.5 Existing features partly or fully covering the use case functionality
Currently, 3GPP systems can support the communication capability to PIN Elements with a gateway. TS 22.261 [14] clause 6.38 covers requirements on PIN. In 6G networks, user intents may be translated into collaborative tasks distributed across multiple UEs. The network can actively initiate, orchestrate, and coordinate these tasks by interpreting user intents, allocating appropriate services to participating devices, and dynamically adjusting resources as tasks progress.
6.20.6 Potential New Requirements needed to support the use case
[PR 6.20.6-1] Subject to operator’s policy, the 6G system shall be able to support mechanism to provide 3GPP service to applications on one or multiple UEs belonging to a user, based on the received intent from the user.
6.21 Use case on 6G network providing on-demand networking with AI Agent
6.21.1 Description
ITU-R involves AI as a new key capability in 6G. It requires specialized use cases by leveraging data collection, local or distributed compute offload, and the distributed training and inference of AI models [27]. As an emerging technology, AI Agent technology can meet such requirements as an intelligence component in 6GS to understand user’s intent, split task into network task, combine and chaining network capability, monitoring task and report to user. Figure 6.21.1-1 depicts procedures of how AI Agent processes a user intent requirement:
Figure 6.21.1-1: AI-Agent based intent translation and task execution
- Intent input: user provides intent to network to express user request, e.g. it could be in natural language “I want to watch a high-definition video”.
- Intent translation: Network AI Agent receives intent requirements, translate it into network requirements.
- Task distribution: Network AI Agent analyses which network functions should be involved to fulfil the task, such as session management related network functions for QoS level guarantee.
- Task execution & interaction: Different network functions will be involved with different tasks. Private network functions components (private network for robots, IoTs, etc.) can also be involved.
- Monitoring & QoS assurance: Real-time monitoring and feedback to make sure whether service is satisfying user intent or not.
6.21.2 Pre-conditions
As shown in Figure 6.21.2-1, Operator X provides AI service to users, which can be based on users’ intention to provide suitable network services including network generation, network capabilities scheduling, QoS assurance. This service is provided to users via an operator portal, it can be an app or web page on the phone. Harry owns an intelligent robot Ron, and a dog Bob. Harry’s smart phone and Ron’s SIM card both have subscriptions with operator X, Ron’s ID, and capabilities are bonded with Harry.
Figure 6.21.2-1: AI Agent based intent task execution workflow
6.21.3 Service Flows
1. Harry is on a business trip. His dog Bob is alone at home and his intelligent robot Ron is responsible for taking care of Bob. Bob needs to go outside for a one-hour walk twice in a day, from 7-8 am and 5-6 pm.
2. Harry hopes 6G network can help to guarantee the safety of Bob, so he uses the operator portal to tell network that, in the next five days, Robot Ron with SIM card ID xxxxx which also embedded with a camera, will walk from Harry’s home to a dog park nearby. On the way there, Ron and Bob need to cross two roads.
3. Harry’s intent is transferred to network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. Based on this intent, network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent figured out that robot Ron needs sensing service from 6G network to help it cross street safely, also need proper QoS to guarantee the video send out to Harry for remote monitoring during that time period.
4. Network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent distributed these tasks to sensing related functionalities and session management related network functions to coordinately fulfil the requirements of this service.
5. After the service configuration, during every day’s dog walking time, Ron will receive a real time sensing result of the road, and the QoS of video transferring can guarantee a smooth and clear view for Harry to monitor his dog Bob.
6. One day there is a music event hosted in the park. Soon the robot Ron or the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent finds out that the network is congested. After receiving feedback, the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent adjusts the policy to generate a dedicated network or a higher QoS policy for the robot Ron, thereby providing a better and more stable service for the robot. At the same time, the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can also perform self-reflection based on the feedback (e.g. memory updating, fine-tuning the model, etc.).
6.21.4 Post-conditions
Network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents provided on-demand networking successfully. Dog walking was safe. Operator X will charge for this kind of customized service from the user per month based on the intent received and the corresponding service provided.
6.21.5 Existing features partly or fully covering the use case functionality
SA1 studied AI related requirement in TS 22.261 [14] clause 6.40. It specified AI/ML model transfer in 5GS, which focuses on AI/ML operation splitting, AI/ML model/data distribution and sharing, distributed/FL. It did not support AI native network in architecture design, task distribution, or intent translation aspects.
6.21.6 Potential New Requirements needed to support the use case
NOTE 1: The mention of AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent doesn’t imply or preclude any architecture assumption or solutions.
[PR 6.21.6-1] Based on operators’ policy and user consents, the 6G system shall support mechanisms (e.g. AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) to translate intent received (e.g. from subscribers) into service and service performance requirements.
NOTE 2: AI capabilities such as AI Agent can, for example, accept intent received from the user, translate it into network requirements, and activate the corresponding 3GPP services (e.g. communication service, sensing service, AI services) with QoS guarantee when they are being consumed.
[PR 6.21.6-2] The 6G network shall provide 3GPP services (including communication, sensing and computing) with QoS assurance based on intent received (e.g. from subscribers).
[PR 6.21.6-3] The 6G system shall support a mechanism to identify and associate the source (e.g. home robot, or specific AI Agent) of the intent with a subscriber and with the actions resulting from the handling/processing of the intent.
[PR 6.21.6-4] The 6G network shall support a mechanism (e.g. AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) to provide mechanisms to monitor and evaluate the quality of the provided 3GPP service and perform adaptations if needed based on the evaluation.
[PR 6.21.6-5] The 6G network shall support charging information collection per UE and/or for the usage of network resources related to multiple 3GPP services (e.g. communication service, sensing service, AI service) to satisfy the intents received from the UE.
6.22 Use case on intelligent calling services
6.22.1 Description
With the advances in AI technology, our service provider can deliver enhanced services to customers. The voice call service is quite universal in people’s daily communication, and we can foresee a few advantages for the combination of AI and voice call. The operator has natural advantage in voice-based services and always been well complying with the user and data privacy related law and regulations. AI with recognition and perception capability can empower the new generation interactive Intelligent calling services, it’s highly practical for the operators all over the world to provide voice based intelligent calling service to the consumers, such as Intelligent pick-up services or Intelligent anti-fraud services. Below we explain the foreseen potential requirements for intelligent calling services:
Intelligent 24-hours online requirements:
Existing operator networks only support call diversion (e.g. CFU/CFB/CFNRy) per TS 24.604 [150], with unified network announcements during subscriber unreachability. It cannot satisfy the evolving user expectations for AI-driven contextual call handling. When users require 24/7 availability for voice call, operator network can provide the AI powered intelligent answering service to prevent missed important calls when use is in power-off or flight mode. The answer can be customized in advanced with customer consent, e.g. response the calling user that the called user is in the business trip flight as well as his/her available time.
Intelligent answering machine requirements:
When users in busy time (e.g. in a long meeting, or in driving mode) , the network signal in users side is poor (e.g. traveling in the mountainous areas)) or when they’re not willing to pick up the call (e.g. the harassing calls, the lower priority but time-consuming calls), the intelligent answering service can save our customer’s hands and time and acting to handle these calls. And it can also provide the calling record for tracking purpose on demand of user requirements.
In brief, the intelligent calling service is an intelligent call-answering service provided by operators to users, based on the authorization from the user for user data. It offers proxy answering and AI-powered response capabilities when the user is unavailable to take calls, e.g. during busy time, when the phone is powered off, or when prolonged online presence is required. It can be provided universally to all common voice users and its character can be customized per user’s preference, e.g. the audio used by the intelligent calling service can be customized with user’s voice. The Intelligent calling services can be authorized with user consent in advance or invoked on demand. Additionally, it can also provide call logs or call summaries.
6.22.2 Pre-conditions
Thomas subscribes to Operator A’s Intelligent Calling services, which is provided by RTC network system. During the subscription procedure, Operator collects the preference from Thomas:
- The trigger condition of calling status, e.g. the Intelligent Calling services can be invoked when Thomas is in flight mode, power-off mode, or not reachable (e.g. when the network signal is poor in Thomas’s side).
- The permission of collecting the calling records or summary the call conclusion for Thomas tracking purpose.
- The intelligent calling characters can be customized with user’s preference, e.g. voice or tone.
Sarah, Thomas’s colleague, also subscribes to Operator A’s RTC network system, as shown in Figure 6.22.3-1..
6.22.3 Service Flows
Figure 6.22.3-1: Intelligent Calling with Built-in RTC (flight mode pick-up)
Thomas will take the 9:00 a.m. flight abroad to attend the international standards conference. Before departure, he inputs his travelling and calendar information. Then he switches his phone to flight mode.
During his flight, his colleague Sara calls him to book his time for meeting discussion. Based on Thomas's authorization before, the network can answer the call on his behalf. Hence the call is picked up automatically and reminds Sara that this is Thomas’s intelligent calling service speaking, upon knows that Sara is calling to reserve Thomas’s meeting time, it responds with the available time slot based on Thomas’s calendar and records the conclusion for this call.
When Thomas takes off the flight mode on his phone, he will receive the call record or summary which is pushed by network side, e.g. via SMS message or voice mail.
During the meeting day, Thomas is occupied by another temporary urgent work matter and it’s already not possible to change the meeting time because there are other participants attending. After communicating with Sara, Thomas activates the intelligent call and when the conference call starts, it acts as Thomas agent to participate in it.
6.22.4 Post-conditions
On the meeting day, Thomas attends the discussion meeting in time.
6.22.5 Existing features partly or fully covering the use case functionality
TS 22.228 [138] defines the service requirements for IMS. IMS supports different IMS multimedia applications. IMS supports a wide range of services, notably voice and video calls. There is extensive support for services, tightly integrated with the 3GPP system, with extensive support for roaming and integration with both PSTN and ISDN telephony, emergency services and more.
TS 22.173 [59] and TS 24.604 [150] define IMS Multimedia Telephony Service and supplementary services, including the Call forwarding related services (e.g. CFU/CFB/CFNRy).
Existing features e.g. Call forwarding related services cannot provide the intelligent call answering, hence the requirements related to what is the Intelligent Calling service are not defined.
6.22.6 Potential New Requirements needed to support the use case
[PR 6.22.6-1] Subject to operator policy and user’s consent, 6G network (e.g. in conjunction to IMS) shall be able to provide intelligent calling service to users in two parties or multi-party call, when the user is unavailable or unwilling to take the calls, e.g. provide intelligent answering with usage of AI capability in case of user’s phone is in flight mode, powered-off or during busy time.
[PR 6.22.6-2] The 6G network (e.g. in conjunction to IMS) shall support charging information collection for the intelligent calling service.
[PR 6.22.6-3] Subject to regulatory requirements, operator policy and user’s consent, the 6G network (e.g. in conjunction to IMS) shall support intelligent calling customization, e.g. the call-answering tone, speech rate customization based on user’s voice, the response can be customized based on different callers.
[PR 6.22.6-4] Subject to operator policy and user’s consent, the 6G network (e.g. in conjunction to IMS) shall support providing the user with information related to the call (e.g. send the conversation record or summary to users after the intelligent calling service call has ended by SMS or voice mail).
[PR 6.22.6-5] Subject to operator policy and regulatory requirements, when an intelligent calling service is used on behalf of a user/subscriber, the 6G network (e.g. in conjunction with IMS) shall be able to identify and associate the specific intelligent calling service being used and the user/subscriber on whose behalf the service is being used.
6.23 Use case on child health management assistant
6.23.1 Description
AI capabilities on UE (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) represent a paradigm shift by embedding AI capabilities directly into user equipment, such as cell phones, smart watches, and cars. The UE with AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent aboard can leverage the UE’s 3GPP subscription data and autonomously invoke 3GPP services (e.g. sensing services, AI/ML services, positioning services) based on understanding and interpretation of user intent. By combining the inherent capabilities and status inside the UE, with the 3GPP service data exposed by the network, the UE can achieve cross-domain data fusion and analysis, thus generating a comprehensive and optimized response to the user.
Building on this autonomous capability, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on the UE can initiate service requests to the network on behalf of the user. It thus becomes essential that upon receiving a service request, the network needs to determine whether it originates from the UE itself or from an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on the UE. From a security perspective, this differentiation helps mitigate risks such as attacks from malicious agents or unintended interpretive errors. From a service standpoint, it allows the network to distinguish between UE-originated and AI-originated traffic, enabling tailored QoS management based on their distinct operation patterns and performance requirements. Furthermore, from a policy control angle, this distinction supports the enforcement of precise operational boundaries and prevents misuse of user privileges through agent-specific policies. Overall, this ability to differentiate significantly enhances security, service customization, and regulatory compliance in 6G networks.
The deployment of UE AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents drives transformative applications across critical domains, e.g. personalized healthcare, smart home systems, and autonomous driving, which can greatly enrich and facilitate people's lives.
6.23.2 Pre-conditions
Mother Emma and Father David bought a smart watch phone for their daughter Lily. Lily's smart watch phone has an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent aboard and health application.
Lily's smart watch phone stores mother Emma's and father David's numbers as emergency contacts. Mother Emma's smart band and cell phone have AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. Father David's cell phone also has AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents.
Lily's smart watch phone has given consent for collecting, distributing and analysing data. Mother Emma's and Father David's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents are authorized to obtain and analyse data collected from Lily's smart watch phone.
The network stores the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent related information, e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent capability.
6.23.3 Service Flows
As shown in Figure 6.23.3-1:
1. Lily went to school wearing her smart watch phone. The smart watch phone routinely tracked her heart rate, body temperature, and physical activity every 30 minutes.
2. During morning classes, the watch recorded consistent readings: heart rate between 72-75 BPM, body temperature ranging 36.3°C-36.5°C, with moderate activity levels. Based on the above parameters, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on Lily's watch determined Lily was in good health.
3. In the afternoon, unusual patterns emerged: Lily's heart rate elevated to 80 BPM with a temperature of 37°C. The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on Lily's watch.
4. The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on Lily's watch detects the unusual pattern, and:
a. sends a notification to the health application on Lily's watch, the application immediately increased monitoring frequency to every 5 minutes;
b. prepares to invoke a voice call to her emergency contacts (i.e. mother Emma and father David), and sends a request to the network for optimal contact selection and corresponding information obtainment; and
c. invokes the location service of Lily's watch, to assist the disease prediction in the application on the watch.
5. The network receives the request from AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent Lily's watch, and:
a. determines that the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent Lily's watch is an authorized AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent;
b. accepts the request based on the subscription data and user consent of Lily's watch;
c. retrieves the stored information of Emma's and David's cell phone (e.g. connection status, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent capability, subscription information, location information);
d. selects Emma as primary contact based on the information stored in the network (Emma's cell phone and smart band: connected status, 2 km away from Lily, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent supported; David's cell phone: deregistered status, 7 km away from Lily, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent supported);
e. selects Emma's cell phone from Emma's device list based on the device's capability (Emma's cell phone: positioning supported, sensing supported, computing supported, and AI supported, Emma's smart band: AI supported); and
f. sends a response message to AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on Lily's watch, including the information of Emma's cell phone (e.g. connection status, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent capability, location information).
6. Based on the response message received from the network, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on Lily's watch:
a. sends the location information obtained from the network to upper layer application. Based on Lily's health profile (age, medical history), location information, and recent prevalent disease in local area, the application calculated an 80% probability of influenza infection.
b. invokes data transmission request to Emma's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent through the network. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on Lily's watch sends Lily's health metrics, inferred disease data to Emma's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent through the network.
c. initiated a voice call to Emma's cell phone.
7. Upon receiving the call, Emma immediately goes to the school to pick up Lily. The 6G network establishes a data path between Emma's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and Lily's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. The data between Lily's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and Emma's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can be transferred through the 6G network. Emma's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent sends a location request to Lily's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent via the established data path. The request from Emma's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent triggers Lily's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to invoke positioning service. Emma's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent also invokes positioning services and sensing services to sense the surroundings of Emma's phone. The network calculates and exposes the Emma's positioning results and the sensing results to Emma's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent, and Lily's positioning results to Lily's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. Lily sends the positioning results via the data path to Emma's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent.
8. Based on the positioning and sensing results received from the network and the Lily's data received from the data path, Emma's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent generates the optimal route for Emma to reach Lily. Every 5 minutes, Lily's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent updates Emma's phone with Lily's latest health metrics and positioning results through the data path via the 6G network. Following the navigation plan generated by Emma's AI[[SUGGESTION_START]]A[[SUGGESTION_END]]gent, Emma successfully retrieves Lily and drives her to the hospital.
Figure 6.23.3-1: Child health management assistant
6.23.4 Post-conditions
After Emma brought Lily to the hospital, Lily received medical treatment and regained normal body temperature within several hours.
6.23.5 Existing features partly or fully covering the use case functionality
In clause 6.40 of TS 22.261 [14], the following requirements of AI/ML are captured:
Based on user consent, operator policy and trusted 3rd party request, the 5G system shall support a means to authorize specific UEs to transmit data (e.g. AI-ML model data for a specific application,) via direct device connection in a certain location and time.
Based on user consent, operator policy, and trusted 3rd party’s request, the 5G system shall be able to provide means for an operator to authorize specific UEs who participate in the same service (e.g. for the same AI-ML FL task) to exchange data with each other via direct device connection, e.g. when direct network connection cannot fulfil the required QoS.
In the above requirements, the data transmission between authorized UEs are constrained to direct connection that are only feasible within a specific location and time. The transmitted data is limited to AI/ML model data.
However, data transmission between AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents expects no limitation on time and area, and the transmitted data obtained by the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent is no longer restricted to AI/ML related data. Most importantly, the data transmission is between UE AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents instead of UEs.
The UE AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can leverage UE's subscription to invoke 3GPP services automatically based on understanding of the intention of the user and also generate a comprehensive response by integrating the data collected from the UE and exposed from the network.
Thus, the existing features can only partly cover the requirements for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents, the network is expected to be enhanced to satisfy the requirements of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents. To support data transmission between AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents without limitation on time and area, the network shall support AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent communication through the network.
6.23.6 Potential New Requirements needed to support the use case
[PR 6.23.6-1] The 6G network shall support a mechanism to manage the information of AI application (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent application) on UE (e.g. related user).
[PR 6.23.6-2] The 6G network shall support a mechanism to authorize AI application (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent application) on UE to invoke 3GPP services.
[PR 6.23.6-3] Based on user consent and operator's policy, the 6G network shall support a secure mechanism to expose information of AI application (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent application) on UE (e.g. related user) to AI application (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent application) on other UE.
[PR 6.23.6-4] Based on user consent and operator's policy, the 6G network shall support a secure mechanism to provide communication service between AI applications (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent applications) on multiple UEs for a collaborative task.
6.24 Use case on distributed 6G network for AI computing
6.24.1 Description
As outlined in this document, numerous high demand use cases in 6G networks, such as augmented reality and autonomous vehicles, are anticipated to heavily rely on AI, requiring significant computational load and substantial power consumption. Currently, many network configurations centralize these computational loads in urban areas, with power supply also concentrated in these areas. In the context, it is concerned that the balance between power consumption and supply are collapsed, which bring a power outage in the urban area.
Ideally, a balance between power consumption and supply is achieved. The power supply is physically distributed across different areas, each with certain constraints (e.g. an upper limit on power supply). Power generation changes according to the demand within the coverage area. It is expected that computation can be physically distributed, and the use of computations can be adapted to match the power supply. Therefore, mitigating these imbalances to maximize overall computational resources and energy efficiency is highly recommended, especially in the context of AI workloads.
As shown in Figure 6.24.1-1, this use case aims to distribute computational workload by appropriately selecting a computing site by routing traffic, particularly considering the available computational resources and surplus power in each area, while also accounting for the specific needs of AI-related tasks. Operators can create optimal computing site selection policies based on typical power usage patterns and the dynamic demands of AI applications to achieve efficient energy consumption. Furthermore, this use case incorporates low latency requirements per traffic flow in the policy and the consequent optimized routing, which is crucial for many AI-powered services.
Figure 6.24.1-1: Distributed 6G Network for AI Computing - The sites A, B, C are the deployed Service Hosting Environment
6.24.2 Pre-conditions
1. Operators distribute computational resources across sites and use the electric power from the areas where these sites are located, to provide computational services.
2. Third-party electric power monitoring systems provide detailed electric power consumption data per computing site via APIs.
3. Operators inference the balance of power consumption and supply for specific time periods for each computing site.
4. Operators can pre-define their own appropriate computing site selection policies based on user attributes (users’ locations, communication time, users’ profiles, etc.) and the latency requirements of the users’ communications.
6.24.3 Service Flows
Based on electric power usage patterns, and power supply information provided by power supply monitoring systems outside 3GPP networks, operators use their own optimal selection policies to select the computing sites and then map computation workloads onto the computing sites. These policies are dynamically adjusted according to the specific location, communication time, users’ profiles, and latency requirements of the users’ communications, as well as the balance between power consumption and the available power supply which is managed outside 3GPP networks. The selected computing site from these policies must optimize network load and electric power consumption in real time, achieving better distributed electric power consumption and improved overall energy efficiency.
6.24.4 Post-conditions
The introduction of the computing site selection policies enhances energy efficiency and achieves balancing of power consumption which is especially important during peak electric power consumption periods. This enables operators to achieve sustainable and energy efficient network operations.
6.24.5 Existing features partly or fully covering the use case functionality
Energy efficiency has been considered in clause 6.15, TS 22.261 [14]. However, Considerations on a balance between power consumption and supply are still insufficient, especially in achieving significant energy efficiency improvements and distributing computational resources that are aimed at this use case. Thus, new requirements are further needed.
6.24.6 Potential New Requirements needed to support the use case
[PR 6.24.6-1] The 6G network shall be able to collect energy related data of the Service Hosting Environment.
[PR 6.24.6-2] The 6G network shall be capable of providing appropriate Service Hosting Environment in order to accommodate compute and communication (e.g. traffic load) resources to meet service requirements (e.g. bitrate and latency).
[PR 6.24.6-3] The 6G network shall be able to consider energy related information from energy management system, which is outside of 3GPP networks, to support mechanisms in order to effectively use the energy, e.g. efficiently use the supplied energy from the energy supplier.
6.25 Use case on AI/ML model training and inference
6.25.1 Description
With the introduction of AI and computing, 6G communication network needs to address the following two scenarios:
1) AI for Network: It is called AI enabled network, which improves the performance, efficiency and user experience of the network itself through AI. AI for network mainly includes the use of AI to optimize traditional algorithms, network functions, as well as network operation and maintenance management.
2) Network for AI: It is called network enabled AI, which provides multiple support capabilities (e.g. connection, computing resource, data) for AI applications through the network, making AI/ML model training/inference more efficient and complying with the local governance of data requirements and data privacy.
With network for AI as an example, the third party may request the 6G network to train AI/ML models considering that third party may lack the computing resources for the complex AI/ML models training. Upon receiving such request, the 6G network may select suitable network entities to perform the AI/ML model training based on the third party’s requirement. Upon the completion of the AI/ML mode training, the 6G network will deliver the well-trained AI/ML model to the third party which runs the AI/ML model to make inference or derive prediction. In addition to third party, the AI/ML model used by subscribers can also be trained and managed by the 6G network. On the other hand, the performance of AI/ML model can be monitored by 6G network. When the performance of AI/ML model drop, the AI/ML model can be retrained by 6G network to reflect the environment change. Moreover, when multiple AI/ML models are available, more suitable AI/ML model can be selected and transferred to third party based on current situation.
In addition to the AI/ML model training, 6G network may also provide the AI/ML model inference service to third parties or subscribers. For example, the AI/ML model inference may be performed at the network edge for real time response, low transport cost, and privacy protection. In this case, the AI/ML model inference data and inference result need to be delivered between third party/subscriber and involved network entities.
Although traditional cloud providers may offer powerful centralized AI model training/inference and management capabilities, the 6G operator can provide more suitable infrastructure for efficient AI model training/inference and management. To be specific, AI/ML models can be updated timely at the network edge of 6G network and subscribers can benefit from immediate feedback for mission-critical scenarios. Similarly, AI/ML model inference at the network edge of 6G network can significantly reduce round-trip communication delays and thus support latency-sensitive AI/ML applications such as autonomous driving, XR and industrial automation. Moreover, the AI/ML model can be trained or be used for inference closer to where the training data or inference data is generated in 6G system. As we know, the UEs of subscribers may collect data about their environment and this data can be transmitted to 6G network for AI/ML model training/inference. This localized processing minimizes the need to send massive amounts of data to distant cloud servers, leading to reduced bandwidth usage and improved energy efficiency.
When the subscriber or third party requests the 6G network for AI/ML model training/inference, the 6G network may select suitable network entities to perform the AI/ML model training/inference. During this procedure, AI training data or AI inference data need to be transferred between subscriber/third party and the selected network entity. Upon the completion of the AI/ML mode training/inference, the well-trained AI/ML model or AI inference result need to be transferred to the subscriber/third party. As we can see, efficient and secure transfer of the data related to AI (e.g. AI training data, AI/ML model, AI inference data) should be supported. Moreover, based on the subscriber/third party’s requirements, the data related to AI can be stored in the 6G network. For example, the well-trained AI/ML model can be stored in the 6G network for potential updates. The AI inference data (e.g. video streaming or sensing data) can also be stored in the 6G network for subsequent AI inference. During these procedures, data privacy should be guaranteed as well.
On the other hand, the QoS requirements on priority, delay, reliability and data rate vary greatly for the data transfer of different subscribers/third party. For example, some other AI/ML model transmission requires high data rate and higher latency while the AI inference result transmission generally requires low data rate and low latency. Therefore, the 6G network should be able to ensure the QoS (throughput, reliability, latency) for the data transfer related to AI.
Today, AI/ML models are growing exponentially in size. Industry-standard 70 B-parameter models, for example, occupy over 150 GB of memory (even more while training), making them risky for a single network entity to process and rendering traditional training methods slow and inefficient. To address this, the 6G network for AI shall adopt distributed approaches, extending beyond the sample parallelism (splitting training data across nodes, as in 5G) to include model parallelism. Model parallelism includes splitting model parameters, layers, or tensors across multiple suitable network entities (e.g. splitting attention layers and feedforward layers across edge nodes) to avoid memory or other resource bottlenecks. Additionally, the 6G network shall optimize inter-network entity communication to reduce latency and bandwidth usage during distributed training.
6.25.2 Pre-conditions
A third-party agricultural technology company develops AI models for precision farming applications, such as crop disease detection, yield prediction, and irrigation optimization. These pre-trained models can be further customized and trained with data from individual farms, including soil conditions, weather patterns, and crop varieties.
The 6G network is operated by operator A, which owns sufficient computing resource and supports AI service (e.g. AI/ML model training, inference and management) for third party. The agricultural technology company subscribes to the AI service from operator A and helps IntelliCrops Farm perform customized AI/ML model training, inference and management.
6.25.3 Service Flows
The IntelliCrops Farm utilizes various sensors and IoT devices to collect data related to soil conditions, weather, crop growth, and other relevant parameters. The collected data can be used as AI training data or inference data and is stored locally on the IntelliCrops Farm.
The IntelliCrops Farm requests a customized AI model from the agricultural technology company. The request specifies the type of model needed (e.g. disease detection, yield prediction) and other requirements specific for this farm.
The agricultural technology company uses mobile operator’s AI services to train the customized AI model. The requirements for the AI model are sent to the 6G network. Upon receiving such request, the 6G network selects suitable network entities to enable the AI model training.
The IntelliCrops Farm’s AI training data is transferred through 6G network for AI model training, ensuring the model is tailored to the farm’s unique conditions.
If AI training tasks need to involve distributed approaches:
The 6G network distributes model components (e.g. parameters, layers or tensors) to selected network entities based on the task’s parallelism needs.
During training, the network optimizes inter-site communication with parallel transmission of training data and model modules between nodes during the backpropagation phase, to ensure efficient data exchange and minimize communication latency.
Upon the completion of the AI mode training, the well-trained AI model can be transferred securely from the 6G network to the IntelliCrops Farm for real time analysis and make decisions regarding crop management, irrigation schedules, and disease control.
Moreover, the IntelliCrops Farm may also deploy the well-trained AI model on 6G network and request AI inference services. The environment data and drone-captured image collected by the IntelliCrops Farm are transferred to the 6G network as input for AI inference.
Upon receiving the request, the 6G network selects suitable network entities to enable the AI inference, which employs the customized AI model specific for the IntelliCrops Farm to make decision on crop management, pesticide spraying or irrigation schedules.
Upon the completion of the AI inference, the AI inference result on crop management, pesticide spraying or irrigation schedules are transferred securely from the 6G network to the IntelliCrops Farm.
6.25.4 Post-conditions
Based on the AI model training and inference service provided by 6G network, each farm can use AI model specifically trained based on its own data, leading to more accurate predictions and optimized performance. Farms do not need to invest expensive computing hardware or AI expertise for model training and inference. Even if an AI/ML model slightly exceeds the capacity of a single network entity, two or more entities can collaborate to complete the task and process even faster with model parallelism and optimized inter-node communication. The farm infrastructure costs can be greatly reduced.
6.25.5 Existing features partly or fully covering the use case functionality
Table 6.25.5-1: Existing features and gap analysis
Specifications and clause
Gap Analysis
Existing Requirements
TS 28.105 [139], clause 4a.0
Only the ML models used in 5GC, NG-RAN and management system are taken into account in TS 28.105 [139]. The ML model used by subscriber is not covered in TS 28.105 [139]. Moreover, how to manage the AI/ML model based on the request of authorized third parties is not covered in TS 28.105 [139] as well.
AI/ML techniques are widely used in 5GS (including 5GC, NG-RAN, and management system), the generic AI/ML operational workflow shown in Figure 4a.0-1, highlights the main steps of an ML model lifecycle.
The ML model lifecycle includes training, testing, emulation, deployment, and inference. These steps are briefly described below:
- ML model training: training, including initial training and re-training, of an ML model or a group of ML models. It also includes validation of the ML model to evaluate the performance when the ML model performs on the training data and validation data. If the validation result does not meet the expectation (e.g. the variance is not acceptable), the ML model needs to be re-trained.
- ML model testing: testing of a validated ML model to evaluate the performance of the trained ML model when it performs on testing data. If the testing result meets the expectations, the ML model may proceed to the next step If the testing result does not meet the expectations, the ML model needs to be re-trained.
- AI/ML inference emulation: running an ML model for inference in an emulation environment. The purpose is to evaluate the inference performance of the ML model in the emulation environment prior to applying it to the target network or system. If the emulation result does not meet the expectation (e.g. inference performance does not meet the target, or the ML model negatively impacts the performance of other existing functionalities) the ML model needs to be re-trained.
NOTE: The AI/ML inference emulation is considered optional and can be skipped in the ML model lifecycle.
- ML model deployment: ML model deployment includes the ML model loading process (a.k.a. a sequence of atomic actions) to make a trained ML model available for use at the target AI/ML inference function.
ML model deployment may not be needed in some cases, for example when the training function and inference function are co-located.
- AI/ML inference: performing inference using a trained ML model by the AI/ML inference function. The AI/ML inference may also trigger model re-training or update based on e.g. performance monitoring and evaluation.
TS 23.482 [190], clause 4.2
Application enablement layer is designed to provide enhancements and optimization for ML model distribution/training/inference for vertical applications. However, this is purely based on the interaction between VAL client and AIMLE server. 6G network does not control this procedure. Computing resource selection is also not considered in SA6’s work.
The AIMLE server shall be capable of provisioning and exposing ML client information.
The AIMLE server shall be capable of supporting the registration, discovery, and selection of AIMLE clients which participate as ML members in AIML service lifecycle.
The AIMLE layer shall be capable of supporting ML service lifecycle operations (e.g. ML model training).
The AIMLE server shall be capable of supporting discovery and provisioning of AIML models.
TS 22.261 [14], clause 6.5.2
User traffic in 5G system is delivered between UE and applications. Efficient user plane is supported to address diverse user’s traffic requirement on reliability, latency, and bandwidth. The user traffic is transparent to the network entities involved in the user plane data transfer. For the transfer of data related to AI, the data is transferred between between multiple end-points within the operator managed data network or third party.
Based on operator policy, application needs, or both, the 5G system shall support an efficient user plane path between UEs attached to the same network, modifying the path as needed when the UE moves during an active communication.
The 5G network shall enable a Service Hosting Environment provided by operator.
Based on operator policy, the 5G network shall be able to support routing of data traffic between a UE attached to the network and an application in a Service Hosting Environment for specific services, modifying the path as needed when the UE moves during an active communication.
Based on operator policy, application needs, or both, the 5G system shall support an efficient user plane path, modifying the path as needed when the UE moves or application changes location, between a UE in an active communication and:
- an application in a Service Hosting Environment; or
- an application server located outside the operator’s network; or
- an application server located in a customer premises network or personal IoT network.
The 5G network shall maintain user experience (e.g. QoS, QoE) when a UE in an active communication moves from a location served by a Service Hosting Environment to:
- another location served by a different Service Hosting Environment; or
- another location served by an application server located outside the operator’s network; or
- another location served by an application server located in a customer premises network or personal IoT network, and vice versa.
The 5G network shall maintain user experience (e.g. QoS, QoE) when an application for a UE moves as follows:
- within a Service Hosting Environment; or
- from a Service Hosting Environment to another Service Hosting Environment; or
- from a Service Hosting Environment to an application server located place outside the operator’s network; or
- from a Service Hosting Environment to an application server located in a customer premises network or personal IoT network, and vice versa.
TS 22.261 [14], clause 6.40.1
The AI/ML splitting operation is used for the application domain (e.g. speech recognition, image recognition, video processing) enhancement of mobile devices (e.g. smartphones, automotive, robots) in TS 22.261 [14]. To be specific, the mobile device executes the operation/model up to a specific part/layer and then sends the intermediate data to the network endpoint. The network endpoint executes the remaining parts/layers and feeds the inference results back to the device. The intermediate data and the inference result for the AI/ML splitting operation in 5G is essentially transferred via traditional user plane between UE and network server/application function.
Artificial Intelligence (AI)/Machine Learning (ML) is being used in a range of application domains across industry sectors. In mobile communications systems, mobile devices (e.g. smartphones, automotive, robots) are increasingly replacing conventional algorithms (e.g. speech recognition, image recognition, video processing) with AI/ML models to enable applications. The 5G system can at least support three types of AI/ML operations:
- AI/ML operation splitting between AI/ML endpoints
The AI/ML operation/model is split into multiple parts according to the current task and environment. The intention is to offload the computation-intensive, energy-intensive parts to network endpoints, whereas leave the privacy-sensitive and delay-sensitive parts at the end device. The device executes the operation/model up to a specific part/layer and then sends the intermediate data to the network endpoint. The network endpoint executes the remaining parts/layers and feeds the inference results back to the device.
- AI/ML model/data distribution and sharing over 5G system
Multi-functional mobile terminals might need to switch the AI/ML model in response to task and environment variations. The condition of adaptive model selection is that the models to be selected are available for the mobile device. However, given the fact that the AI/ML models are becoming increasingly diverse, and with the limited storage resource in a UE, it can be determined to not pre-load all candidate AI/ML models on-board. Online model distribution (i.e. new model downloading) is needed, in which an AI/ML model can be distributed from a NW endpoint to the devices when they need it to adapt to the changed AI/ML tasks and environments. For this purpose, the model performance at the UE needs to be monitored constantly.
- Distributed/Federated Learning over 5G system
The cloud server trains a global model by aggregating local models partially-trained by each end devices. Within each training iteration, a UE performs the training based on the model downloaded from the AI server using the local training data. Then the UE reports the interim training results to the cloud server via 5G UL channels. The server aggregates the interim training results from the UEs and updates the global model. The updated global model is then distributed back to the UEs and the UEs can perform the training for the next iteration.
TS 22.261 [14], clause 7.10
Different QoS requirements are captured in TS 22.261 [14] on AI model transfer and split AI/ML inference in 5GS. As we mentioned before, the AI model transfer and data for split AI/ML inference is actually delivered between UE and applications via user plane traffic in 5G system. When it comes to 6G network, the QoS requirement for the data transfer of AI model and data for split AI/ML inference still needs to be addressed.
The 5G system shall support split AI/ML inference between UE and Network Server/Application function with performance requirements as given in Table 7.10.1-1.
Table 7.10.1-1 presents the KPI table of split AI/ML inference between UE and network Server/Application function.
The 5G system shall support AI/ML model downloading with performance requirements as given in Table 7.10.1-2.
Table 7.10.1-2 presents the KPI table for AI/ML model downloading.
6.25.6 Potential New Requirements needed to support the use case
[PR 6.25.6-1] Subject to operator’s policy, the 6G network shall be able to provide AI/ML model training and inference within service hosting environment, and AI model management (e.g. model monitoring, retraining, validation) for authorized third parties.
[PR 6.25.6-2] Subject to operator’s policy, the 6G network shall be able to take into account third party’s requirement(s) (e.g. latency) and the computing resource availability for the AI/ML model training and inference within service hosting environment.
[PR.6.25.6-3] Subject to operator’s policy, the 6G network shall support the collection of charging information for the AI/ML model training, AI model inference and management which are within the service hosting environment for authorized third parties.
[PR 6.25.6-4] Subject to operator’s policy, the 6G network shall support to distribute AI/ML model to multiple Service Hosting Environments to support distributed AI/ML model training.
[PR 6.25.6-5] Subject to operator’s policy, the 6G network shall be able to support to coordinate with Service Hosting Environment to minimize the latency of distributed AI/ML model training.
6.26 Use case on optimizing user experience for GenAI applications
6.26.1 Description
Applications based on GenAI capabilities are becoming popular recently, and as the research and development goes deeper, we see they are offering services from simpler text-based chatting to more complex multi-modal services like audio and video generation. Examples of these new applications and services include real time translation, image or video recognition / visual assistant and real-life assistance (e.g. home doctor, cooking and game agent). Some of them are already commercialized and have the potential to be a main type of traffic in the future.
Therefore, it’s important to understand the traffic characteristics of these GenAI applications, so that the 6G network could optimize the service experience of their users, while at the same time improving the efficiency of the network when transmitting this traffic.
Figure 6.26.1-1: Traffic characteristic of typical GenAI applications
Figure 6.26.1-1 (above) shows three kinds of typical GenAI applications, visual assistant, text-to-image generation, and chatbot. Their characteristics are summarized as below:
Common characteristics:
Burst traffic: high bandwidth requirement during a short time period can be noticed in both situations, while application #1 mainly for uplink and application #2 for downlink;
Traffic encryption: not shown in the figure, but current GenAI applications usually support end-to-end encryption of the traffic, though it ensures the security and privacy of the user, it also makes the network harder to identify the traffic type and provide proper service quality assurance to these applications;
Various connection design: applications are using different connection designs, for example, some applications have long-lived connection for each task/session, and some applications use different connections and even different source IPs for each request and might support parallel transmission (e.g. in image generation case).
Application #1 Visual Assistant:
Low transmission latency requirement: not shown explicitly in the figure, the real time service requires low end-to-end latency. For example, according to the following analysis, good interaction experience requires a round-trip latency within 400 ms [176]. And because the current GenAI algorithm usually takes time to generate content (for example, GPT-4o® is able to respond to audio inputs within at least 232 ms), the transmission latency boundary is quite limited (e.g. less than 70 ms) [177];
Short-lived connection: also not shown explicitly, the image upload sets up a new connection each time.
Application #2 Text-to-image Generation:
Unpredictable bandwidth requirement: the GenAI application supports multi-modal communication, and could transmit text, audio or video randomly based on user request, and therefore has no periodic traffic pattern;
High reliability requirement: unlike audio or video processing which are tolerant to some packet loss, the “prompts” used to express user intent are required to be transmitted reliably for correct processing.
Application #3 Chatbot:
Small packet length: Chatbot applications typically have very small packet length, including both data packets and ACK packets, which would lead to decrease of processing speed considering the current user plane design.
Table 6.26.1-1: KPIs comparison between GenAI application and short video application
Application
Average packet size (Byte)
Uplink
(note)
Downlink
(note)
Average Data Rate (Mbps)
Packet Percentage
Traffic Percentage
Burst Data Rate (Mbps)
Burst Packet Percentage
Burst Traffic Percentage
Average Data Rate (Mbps)
Burst Data Rate (Mbps)
short video
1144
0.007
17%
1%
0.23
17%
1%
0.91
24
short video live streaming
921
0.034
34.7%
1.6%
0.23
38%
1.6%
2
13.2
visual assistant
838
1
49%
48%
67.6
51%
52%
1.2
69.4
text-to-image generation
987
0.03
28%
5.6%
0.9
22%
2.7%
0.6
34.1
chatbot
87
0.008
48%
22.7%
0.08
48%
31.8%
0.11
0.14
Note: meanings of KPIs are as follows
average (uplink/downlink) data rate: average data rate of uplink/downlink traffic during the whole transmission
(uplink) packet percentage: number of uplink packets divided by number of all packets during the whole transmission
(uplink) traffic percentage: amount of uplink data traffic divided by amount of all data traffic during the whole transmission
burst (uplink/downlink) data rate: average data rate of uplink/downlink traffic within a burst
burst (uplink) packet percentage: average number of uplink packets within a burst divided by average number of packets within a burst
burst (uplink) traffic percentage: average amount of uplink data traffic within a burst divided by average amount of data traffic within a burst
Table 6.26.1-1 also shows a statistics example of KPIs of traditional video streaming applications and typical GenAI applications. It’s obvious that GenAI applications have quite different characteristics from traditional applications, for example, noticeably high burst traffic and uplink packet/traffic percentage (more interactive).
To better serve the GenAI applications and services, it’s proposed in this use case that several enhancements could be introduced into the 6G system, including:
- UE and network coordination: UE could help network to identify the application and traffic type more accurately and quickly, and the network could help UE make better flow control
- Better QoS assurance: to ensure the GenAI application’s performance and service reliability, the network need to be aware of the burst and provide assurance for the whole burst; to deal with short-lived connections, QoS configuration needs to be done in a shorter time period; provide end-to-end service assurance by choosing an optimized path to the AS; and make QoS adjustments according to payload type, real time network status and user feedback
6.26.2 Pre-conditions
User Andy has subscribed to Operator B’s services and uses B’s mobile network to access and utilize an AI image generation service provided by a GenAI service provider DrawYourPicture.
Operator B and GenAI service provider DrawYourPicture have a specific SLA in place that allows the Operator B to treat the traffic to/from DrawYourPicture in an optimised manner.
6.26.3 Services Flows
1. Andy is writing a blog on his phone and would like to use DrawYourPicture’s mobile application service to generate an image as part of his blog.
2. DrawYourPicture’s service is E2E encrypted. To ensure the service quality, Andy’s phone coordinates the predicted service requirements to the network before sending the actual request, including for example application type (GenAI/Image generation), size and duration of the burst traffic, required latency, etc.
3. After receiving these information, the network checks the SLA with DrawYourPicture and tries to prepare an optimized session for Andy. The network may choose an optimized path to DrawYourPicture’s aAS and prepare and configure a proper QoS rule utilizing, for example, historical data, AI predictions.
4. Andy sends some prompts to DrawYourPicture with the content of the blog.
5. To ensure the service quality, the network needs to assure the whole packets related to the prompts are delivered through an optimized path for better performance and avoiding packet loss.
6. DrawYourPicture’s AS receives the prompts and uses its GenAI algorithm to generate an image.
7. The network prepares to send the generated image back to Andy, it coordinates with his phone about the workload status of the user plane and provides a recommended bandwidth to it.
8. Andy checks the generated image and thinks it doesn’t reflect all his requirements. So he decides to send a sample image searched online to DrawYourPicture, to help it generate a better image.
9. Before sending the image, Andy’s phone coordinates the new requirements and current QoE to the network, and the network updates the QoS rules and/or routing paths for this image upload accordingly. After that, the phone uploads the image to DrawYourPicture.
10. Andy receives a newly generated image from DrawYourPicture and is satisfied with the result.
11. The network summarizes the whole service procedure and keeps the record for self-learning and self-optimization for providing connections to similar image generation applications in the future.
6.26.4 Post-conditions
Andy gets the ideal image for his blog post, and experiences an optimized service from operator B.
Operator B fulfils Andy’s service requirements associated with DrawYourPicture service and improves the network efficiency through coordination with Andy’s phone.
6.26.5 Existing features partly or fully covering the use case functionality
The paper [178] describes a feasible approach on how to identify encrypted traffic, which requires: 1) the initial packet’s SNI is not encrypted, and 2) need to have packets transmitted in X seconds’ time to help identification. This mechanism is not suitable for identifying GenAI applications which have burst type of traffic.
The current mobile network system supports QoS assurance mechanism. Although the current mechanism is implemented by exchanging signaling within control plane, and it usually takes a lot of signaling exchanges and introduces additional latency, which is not suitable for short-lived connections.
The current mobile network system supports measure QoE of the user, but the analysis is not too accurate because traffic statistics are used as the only approach.
6.26.6 Potential New Requirements needed to support the use case
[PR 6.26.6-1] 6G system shall provide efficient QoS assurance mechanisms for different types of application traffic, with the KPI summarized below:
Table 6.26.6-1: Required KPIs for GenAI applications
Traffic Type
Burst size
Max Allowed latency for a burst
(note)
Service bit rate: user-experienced data rate
Delay
(note)
Average packet size
Image based GenAI app
400 KB
50 ms
64 Mbps
20 ms
> 800B
Video based GenAI app
20 MB
400 ms
400 Mbps
20 ms
> 800B
chatbot
0.5 KB
20 ms
200 Kbps
30 ms
< 800B
Note: meanings of some KPIs:
max allowed latency for a burst: max latency for sending out the whole packets within a burst
delay: transmission latency of a packet
[PR 6.26.6-2] Subject to operator’s policy, agreement with authorized 3rd party and user consent, 6G network shall be able to be aware of the burst characteristics (e.g. Burst Data Rate) in traffic and provide mechanisms to optimize resource efficiency and assure user experience when handling such traffic.
NOTE: Improved coordination between applications on the UE and the 6G network is expected, considering GenAI traffic is usually encrypted end-to-end. For example, the application on the UE could inform the 6G network of the type of GenAI traffic (e.g. image-based, video-based, chatbot) and/or characteristics of the traffic (e.g. burst), for the 6G network to consider appropriate mechanisms to provide communication service.
[PR 6.26.6-3] Subject to operator’s policy, agreement with authorized 3rd party and user consent, 6G network shall be able to adjust the QoS configurations dynamically when the payload type changes (e.g. from text to image), or the pay load type is predicted/notified to be changed during a session.
[PR 6.26.6-4] The 6G network shall be able to collect charging information based on unique traffic characteristics pertinent to e.g. specific GenAI applications, or enhanced/guaranteed user experience.
6.27 Use case on network federation for collaborative AI model training
6.27.1 Description
Training complex AI/ML models requires vast datasets and substantial computing power. To address this challenge, MNOs can dynamically federate their networks to enable collaborative model training across both networks. The network federation allows for the on-demand establishment of a shared AI infrastructure, leveraging diverse datasets and distributed computational resources. Federated networks can collaboratively employ AI/ML techniques to train complex AI/ML models in a way that ensures data privacy and maintains sensitive data within individual networks.
Consider two fictional MNOs, "Connectify" and "GlobalNet," each with its own AI/ML infrastructure, including datasets and computing resources. These MNOs can dynamically federate their networks to create a shared AI infrastructure, leveraging the combined resources for collaborative model training.
6.27.2 Pre-conditions
Connectify and GlobalNet are two 6G network operators with coverage in a smart city.
Each 6G network possesses AI/ML infrastructure and collects traffic data from various sources, such as IoT devices, vehicles, and through sensing capabilities.
Connectify and GlobalNet establish a partnership (an agreement) to federate their AI/ML infrastructure whenever needed.
6.27.3 Service Flows
The city transportation authority submits a request to Connectify, specifying the need for an AI model to optimize traffic flow in the city.
Connectify determines that it cannot satisfy the request alone because, for example, it does not possess sensor data across the entire city, neither the necessary AI resources. Thereby, Connectify establishes a federation with its partner GlobalNet.
Once the federation is established, a secure communication channel is created between Connectify and GlobalNet, that can be used to coordinate and share their AI resources.
An aggregation system, managed by Connectify, is instantiated to coordinate the collaborative training of the AI model requested by the city transportation.
Connectify and GlobalNet train local AI models using their collected traffic data. Model updates from each network are securely transmitted to the central aggregation system, managed by Connectify. The central aggregation system aggregates the model updates and generates an aggregated AI model reflecting diverse traffic conditions across the city.
After several rounds of training and exchange of model data across the two federated networks, the final AI model is deployed in Connectify's network, and the city transportation authority is informed that the requested AI model is available.
The final model may also be made available to GlobalNet.
6.27.4 Post-conditions
The city transportation authority utilizes the final AI model, which was collaboratively trained using the federation established between Connectify and GlobalNet for sharing AI infrastructure. The AI model can be used to:
- Optimize traffic signals in real time;
- Suggest alternative routes and detours to drivers;
- Predict traffic congestions and proactively manage traffic flow;
- Optimize public transportation schedules;
- etc.
The federation established between Connectify and GlobalNet can be maintained for future AI/ML collaborations or released using standardized APIs.
6.27.5 Existing features partly or fully covering the use case functionality
FL concepts are discussed in TS 22.261 [14] for 5G systems.
However, service requirements for dynamic network federation for collaborative AI/ML model training across federated 6G networks are not defined.
6.27.6 Potential New Requirements needed to support the use case
[PR 6.27.6-1]: Subject to operator policy and regulatory requirements, the 6G network shall be able to enable a federation with one or more other 6G networks (without involving the 6G radio network) in order to enable the collaborative execution of AI/ML tasks, e.g. model training and testing.
6.28 Use case on network-assisted video-based AI inference task offloading for mobile embodied AI
6.28.1 Description
In the realm of Mobile AI, the integration of AI inference in services (e.g. video, multimedia, gaming, XR) on mobile devices presents a compelling market trend that highlights the need for real time intelligence and enhanced (e.g. context-aware) user experiences. This scenario involves leveraging AI algorithms to process and analyse video data on mobile devices, enabling features such as object detection, scene recognition, and adaptive video quality adjustments. These capabilities not only enhance the user experience by providing more interactive and intelligent applications but also open up new possibilities for various industries, including entertainment, healthcare and so on.
While Mobile AI have become increasingly powerful, they still face limitations when it comes to handling complex AI inference tasks, especially those requiring real- ime processing of high-resolution video data. In light of these challenges, offloading AI inference tasks to the Service Hosting Environment, operator or 3rd party-managed cloud has emerged as a promising trend to support AI inference for Mobile AI. This use case focuses on the 6G communication service, via which a Mobile AI embodied device’s generated video data transferred over the 6G network is delivered to a server in Service Hosting Environment or cloud, to be as input for AI inferencing.
As the inputs of Mobile AI may involve multi-modality (e.g. video, audio, point cloud, etc.) data, here we take object recognition through video as an example. Let's consider a scenario where a Mobile AI embodied device (e.g. robot) is equipped with a number of cameras. The network-assisted AI inference task offloading operation is usually more demanding for the uplink in terms of data rate and communication latency. For this type of use cases, based on the exact application and combination of different cameras, the difference in required communication performance can be summarized in Table 6.28.1-1.
Table 6.28.1-1: Real time video uploading for network-assisted AI inference offloading services for mobile embodied AI devices
Scenario
I
II
III
6 & 8 Cameras
(note 1)
1080p * 6
(1080p@15Hz)
1080p * 4 + 4K * 2
(1080p@15Hz,4K@30Hz)
1080p * 2 + 4K * 4
(1080p@15Hz,4K@30Hz)
1080p * 4 + 4K * 2
(1080p@60Hz,4K@60Hz)
Data rate (Mbps)
(note 2)
20
60
100
E2E RTT
(note 3)
100~300ms
NOTE 1: 6 RGB cameras are equipped for robot “Figure 02” [180], 8 RGB cameras are equipped for robot of Tesla Optimus [183].
NOTE 2: Data Rate = Video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, around 10 Mbps for 1080p 60Hz, around 24Mbps for 4K 30Hz and around 30Mbps for 4K 60Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real time video uploading of a vehicle as per YD/T 4778-2024 [182].
NOTE 3: See analysis for Reaction Time Statistics [181].
E2E Round Trip Time (RTT) including UL and DL
Specifically, we take the third scenario as an example. The mobile AI embodied device has two cameras (same type) that operate at a resolution of 1080p and a frame rate of 15 Hz (6 Mbps data rate, 66,67ms data interval). In addition to these, it has four cameras (of another type) that function at a higher resolution of 4K and a frame rate of 30 Hz (96 Mbps data rate, 33,33 ms data interval). Given these specifications, the peak data rate of UL that needs to be processed can be estimated to be around 100 Mbps (3 * 2 Mbps + 24 * 4 Mbps).
Figure 6.28.1-1: UL video data frame (as metadata) related to offloaded AI inference operation in S.H.E. or cloud
Owing to various challenges in the dynamic wireless environment, such as (adjacent cell) interference, mobility, poor coverage at cell-edge, the uplink video transmission may encounter a situation where sometimes the video data frames experience higher packet loss. Depending on the situation, different frames may experience different levels of packet loss. As a result, the network-assisted AI inference task offloading depending on successful uplink transmission of video frames to accurately process and analyse video content, may experience degraded performance (see Figure 6.28.1-1).
Figure 6.28.1-2: User experience achieved by error-tolerant codec (Grace) and traditional codec (FEC, error concealment), across varying packet loss rates [162]. The packet loss rate is defined as the packet loss per frame in [162], which is explained in the paper that “it is important to note that our notion of packet loss differs from network-level loss.”
Figure 6.28.1-3: Different levels of tolerance of packet error rate for different frames, for a given AI inference application
However, thanks to AI technology, the AI codec technology enables an error-tolerant capability in a frame. As shown in Figure 6.28.1-2, the AI codec with error-tolerance, i.e. Grace method, provides a better user experience than the traditional codec, especially for the case of high packet loss rate per frame. Moreover, the Structural Similarity Index, a perceptual metric that quantifies image quality by comparing luminance, contrast and structure between reference and test images) [262] is about 18 dB (0.985) when the user experience (MOS≥80) is excellent [263], the packet loss rate per frame is 20% correspondingly. It means the average packet error rate of each frame should be no more than 20% packets to obtain such user experience. The maximum value of X-axis is 80%, which means the max allowed packet error rate per frame is 80%, exceeding this value will result in source decoding failure. The corresponding user experience of service/user will collapse when the source decoding is failed. Besides, due to different goals of such type of applications (i.e. offloaded AI inference task at the cloud side, using bursty/periodic video data received from a mobile embodied AI device), different applications exhibit different levels of packet loss tolerance on a per frame basis (which is derived based on the aforementioned Grace method), e.g. shown in Figure 6.28.1-3. For instance, real time danger detection can tolerant a higher packet loss within a frame compared to the text/ number recognition applications. This is because danger detection primarily needs to detect whether an object is moving towards the target people, without the necessity of identifying the specific object. Consequently, low-resolution videos can be used, allowing for a greater tolerance of packet loss, which can achieve acceptable user experience. In contrast, text or number recognition demands precise identification, necessitating higher-resolution and more reliable video transmissions. Even minor network fluctuations can lead to incorrect text or number recognition, making it less tolerant to packet loss within a frame.
As stated above, different AI inference applications/tasks allow different levels of packet loss tolerance within a frame. Besides, for the same AI inference application, the error tolerance of different frames also varies. Therefore, it is crucial for 6G system to address these challenges to ensure reliable and efficient AI inference in video transmission and guarantee user experience when the packet loss occurs within a frame.
6.28.2 Pre-conditions
Alex has a mobile embodied AI device equipped with high-quality cameras to record videos and a stable network connection through 6G network. His mobile embodied AI devices meet the minimum requirements for video uploading and recognition.
The video-sharing platform Alex uses supports AI inference services and has a user-friendly interface for uploading and processing videos. The platform's servers in Service Hosting Environment, or cloud, and network infrastructure are functioning properly without any known outages or maintenance issues.
Alex has an active account on the platform with necessary permissions to upload videos and access AI enhancement features.
6.28.3 Service Flows
Alex records a high-quality video of the buildings he visited during his travel using his mobile embodied AI devices. He wants the AI APP to perform danger detection when he walked in the street and perform number recognition when he is shopping at supermarket.
Alex uploads the real time video on the APP platform and inputs the task instructions. The AI inference task is sent to the cloud-based servers through 6G network.
Due to the error of network transmission or delay, some errors/failures occur in parts of the transmitted information, which in irrecoverable for 6G system. When the instruction is about danger detection, a low-resolution video is allowed and more packet loss within a frame can be tolerant. While when he is at supermarket and want the mobile AI to recognize the price of products, a higher-resolution video should be provided and less packet loss within a frame can be tolerant.
The platform server receives video and task information, conducts AI inference based on the information. Moreover, the information such as the tolerant level of UL data is sent to the network from third party. This enables the 6G network to perform transmission more properly, thereby enhancing the overall user experience when using the AI inference applications.
Alex enjoys a safety travel and receives recognition results of the video correctly.
6.28.4 Post-conditions
Even if the partially encoded sensor information is transmitted incorrectly, Alex's video is successfully uploaded and processed with AI inference. Alex is satisfied with the platform's performance, including the ease of upload and the accuracy of AI inference. The platform's network and AI systems function as expected, meeting Alex's needs and expectations.
6.28.5 Existing features partly or fully covering the use case functionality
In the current 3GPP system, some error correction codecs have been are proposed, for example low-density parity check code, Polar code, which are specifically designed to achieve high reliability and low bit error rate (BER) in the context of TS 38.212 [179] clause 5.3. When errors occur during transmission, hybrid automatic repeat request, forward error correction (FEC) and automatic repeat request can be utilized to further improve reliability.
TS 22.261 [14] clause 7.6 specifies the KPIs for high data rate and low latency services, including Cloud/Edge/Split Rendering, Gaming or Interactive Data Exchanging, Immersive multi-modal VR.
TS 22.261 [14] clause 7.10 specifies the KPIs for split AI/ML inference between UE and Network Server/Application function, as shown in Table 7.10-1 from [14].
Table 6.28.5-1: KPI Table of split AI/ML inference between UE and Network Server/Application function (Table 7.10-1 from [14])
Uplink KPI
Downlink KPI
Remarks
Max allowed UL end-to-end latency
Experienced data rate
Payload size
Communication service availability
(note)
Reliability
Max allowed DL end-to-end latency
Experienced data rate
Payload size
Reliability
2 ms
1.08 Gbit/s
0.27 MByte
99.999 %
99.9 %
99.999 %
Split AI/ML image recognition
100 ms
1.5 Mbit/s
100 ms
150 Mbit/s
1.5 MByte/frame
Enhanced media recognition
4.7 Mbit/s
12 ms
320 Mbit/s
40 kByte
Split control for robotics
NOTE: Communication service availability relates to the service interfaces, and reliability relates to a given system entity. One or more retransmissions of network layer packets can take place in order to satisfy the reliability requirement.
In the TS 23.501 [140] clause 5.37.4, some information such as delay, jitter, congestion information and so on, have been specified to be sent from the AF to network in order to provide a better user experience.
In TS 23.501 [140] clause 5.7.3.5 and Clause 5.7.3.6, the packet error rate is defined as an upper bound for the rate of PDUs (e.g. IP packets) that have been processed by the sender of a link layer protocol but that are not successfully delivered by the corresponding receiver to the upper layer. The calculation of packet error rate is associated with a default value for the Averaging window. The averaging window may also be signalled together with a standardized 5QI to the (R)AN and UPF and if it is received, it shall be used instead of the default value.
In TS 23.501 [140] clause 5.7.7.3, the PDU set error rate have been defined to represent the upper bound for the rate of PDU Sets that were processed by the sender of a link layer protocol (e.g. Radio Link Control in RAN of a 3GPP access) but have not been successfully delivered by the corresponding receiver to the upper layer (e.g. Packet Data Convergence Protocol in RAN of a 3GPP access).
However, current technologies like FEC introduces significant bitrate, which may bring additional transmission burden. Moreover, the 3GPP system requirements given above and the current information provided by services are based on packet-level transmission. Here we take the default value (2000ms) for the averaging window as an example. As the averaging window exceeds the interval between frames, there are multiple frames within the window. So, the packet loss rate represents a statistical measure of PDU errors within this window, which is decoupled from the granularity of the frame. More importantly, the packet error rate cannot fully reflect the user experience, as the same packet loss rate may lead to varying user experiences. We assume that one frame has 10 PDUs, and the second frame has 90 PDUs. For the same packet loss rate of 10%, the experience of the first case that all 10 PDUs in the first frame are lost is completely different with the case that 10 PDUs in the 90 PDUs in the second frame are lost. Besides, the PDU set error rate characterise the error rate of PDU set/frame as a whole, instead of representing the error rate within a frame. Therefore, existing features and requirements are insufficient to ensure a satisfactory user experience when packet loss occurs within a data frame.
6.28.6 Potential New Requirements needed to support the use case
[PR 6.28.6-1] Subject to operator policy and agreement with the third party, 6G network shall support means for an authorized third party to provide information about expected communication performance requirement, for the 6G network to provide data transfer accordingly for the application to enhance user experience.
NOTE: For example, for AI inference application based on video, such information can refer to max allowed packet error rate and average packet error rate on a per video frame basis. The max allowed packet error rate characterizes the upper bound of packer error rate of each frame to achieve successful source decoding. The average packet error rate is averaged over a number of video frames related to providing acceptable user experience.
6.29 Use case on smart home user-centric AI service
6.29.1 Description
With the development of digital technologies, there are more and more smart devices. The smart devices could be from different vendors, which could not be connected with each other via the application provided by a specific vendor. With the help of direct device connection, those smart devices could be connected with each other. In home, there are different smart devices for different family members. In order to provide services and analysis for different family members, information collected from different smart devices for different family members could be analyzed separately with the AI model for personalized experience.
Utilizing artificial intelligence, better smart home and personal experience could be achieved with model training and/or model inference using the information collected from those smart devices. Based on the collected information from the users, AI could be utilized for personalized intelligent applications for different users.
People pay more attention to data privacy and security during data transmission. In the personalized user-centric intelligent application, personal data are required for model training and model inference. Data privacy and security could be ensured with data procession under operator’s control and data transmission using direct device connection, which is ensured by authorization from the MNO.
6.29.2 Pre-conditions
Alice owns different smart watches and there is smart scale to monitor her body composition information, sleep monitoring devices to track the sleep, and smart watches for fitness monitoring, etc. These are smart devices that could monitor and record the health and workout for different family members, and which could support their mental health and vitality.
Alice owns a smart assistant (UE) in home that could connect to the 6G system. The smart assistant could help with the smart home applications such as workout recommendation, diet plan recommendation, etc. Smart assistant is equipped with a voice recognition function and natural language processing, so that it can understand user’s queries.
Alice’s phones, i.e. UEs, have their own identities. Smart watches are associated with their own UE’s identities.
6.29.3 Service Flows
1. The personal health data of Alice, e.g. weight, sleeping monitoring, will send to the smart assistant for storage periodically using direct device connection or non-3GPP access.
2. During weekend, Alice decides to workout and she does not have any plan in her mind. Alice decides to ask for the smart assistant at home for workout recommendations.
3. Based on the voice request from Alice, the smart assistant recognizes that it is Alice requesting the workout recommendation. The smart assistant does not have enough compute resource to do the recommendation for Alice.
4. The smart assistant requests the 6G network to do the AI inference.
5. If the 6G network decides that more information about Alice is needed, the 6G network notifies the smart assistant. The smart assistant discovers Alice’s smart watch and asks Alice’s smart watch to provide the health data of Alice. The smart watch receives the request and sends the requested data to the 6G network either directly or via the indirect device connection with the smart assistant as the relay.
6. The 6G network executes the AI service and provides the result to the smart assistant.
6.29.4 Post-conditions
Based on the result from the service hosting network, Alice gets the workout recommendation. When the 6G network charges, the 6G network charges Alice once, but not on all the UEs involved.
6.29.5 Existing features partly or fully covering the use case functionality
TS 22.278 [74] clause 7A covers requirements on proximity service of direct device connection. It is supported for UEs to establish connection in between, and without any network entity in the middle.
TS 22.261 [14] clause 6.40.2.2 covers requirements on AI/ML service traffic transmission via direct device connection. Requirements are given for UE to transmit data for the same AI/ML service. The requirements are targeting the AI/ML service and the involved UEs are not dedicated for the same user.
TS 22.261 [14] clause 6.38 covers requirements on PIN that includes PIN Elements managed locally by the PIN Element with Management Capability. It is supported to provide communication capability to PIN Elements with a gateway.
6.29.6 Potential New Requirements needed to support the use case
[PR 6.29.6-1] Based on the request from authorized 3rd party and user consent, the 6G network shall be able to support secure coordination with one or more UEs in order to fulfil an AI service request for a user.
NOTE: It is assumed that the UEs belong to the requesting user.
[PR 6.29.6-2] The 6G network shall be able to collect charging information for AI service per user.
6.30 Use case on smart healthcare
6.30.1 Description
Smart healthcare improves health outcomes by integrating advanced technologies such as the Internet of Things (IoT), AI, and mobile communication systems into everyday medical and caregiving environments. These innovations aim to address pressing global challenges, including aging populations and the rising demand for personalized care, by creating interconnected and intelligent healthcare ecosystems.
In smart healthcare scenarios, dedicated appliances are often designed for specific purposes, with some offering high-performance hardware storage and computing capabilities. Connected via the 6G network their resources can be flexibly utilized via unified coordination.
6.30.2 Pre-conditions
Hospital X is subscribed to Operator O for supporting the smart healthcare service.
Patient A is admitted to the smart healthcare ward in Hospital X, where Operator O provides network support for all smart healthcare processes with several intelligent devices, ensuring the smooth execution of tasks defined by Hospital X's smart healthcare system. The ward is equipped with the following devices:
Wearable Health Device Set W: This set consists of a smart bracelet and a chest strap worn by Patient A. Device W continuously records and uploads vital sign data, including electrocardiograms (ECG) and heart rate, allowing medical staff to monitor changes in the patient's health in real time.
Smart Bed B: Equipped with various sensors, the smart bed monitors vital signs in real time, including respiratory rate, body temperature, blood pressure, and blood oxygen saturation. This data is wirelessly transmitted to nurse stations or the mobile devices of doctors.
Care Robot C: This robot is designed for intelligent nursing, supporting delivering meals and medications, as well as performing cleaning and disinfecting duties within the room. Robots have some advanced capabilities regarding caregiving. In specific scenarios, it can directly operate on devices within the ward, further enhancing its functionality and adaptability in providing personalized care. Care Robot C operates across multiple rooms, dynamically navigating to specific rooms based on the task execution status of each ward to provide timely services.
Environment Control Device D: D can automatically adjust lighting, temperature, humidity, and other environmental parameters based on the patient's medical needs and personal preferences.
Interactive Device E: E support multimodal interactions, including visual, touch, and voice inputs, enhancing patient engagement and enabling personalized care. Equipped with high-definition cameras, AR and XR consultation capabilities, it can monitor patient activities, detect abnormal behaviors such as falls, and trigger automatic alerts in emergency situations.
Set of Smart camera F is settled in hospital X, e.g. health recovery area, garden area.
6.30.3 Service Flows
Tasks Setup: At 7:00 AM, the doctor uploads Patient A's Daily Recovery Schedule via the smart healthcare application supported by 3GPP services provided by Operator O. The plan includes meal schedules, medication reminders, light exercises, and environmental adjustments. Hospital devices and equipment, including Care Robot C, Smart Bed B, Environment Control Device D, and Interactive Device E, are coordinated to ensure the plan is smoothly executed.
At 9:00 AM, the breakfast and medication task are triggered based on the Daily Recovery Schedule, leveraging the 6G network to support the tasks across on-demand devices. Smart Bed B reminds Patient A to take his pills before breakfast, while Care Robot C delivers the meal. Simultaneously, Environment Control Device D adjusts the room's settings to ensure a comfortable environment.
At 12:00 PM, after lunch task, it comes the outdoor activity and room cleaning task. Patient A engages in an outdoor activity in the hospital’s designated recovery area. Robot C initiates an automated cleaning task in the room, ensuring the space is clean and ready for Patient A’s return.
At 3:00 PM, it’s time for the remote consultation with doctor. In preparation for the XR-based remote consultation, 6G network sends Care Robot C to arrive at the ward in advance to provide computational support (e.g. slit-rendering). Following the consultation requirements, Care Robot C collects Patient A's vital sign data from Smart Bed B and organise all the personal data collected sends them to doctor to assist with the consultation. Once everything is ready, the doctor and Patient A successfully conduct the XR remote consultation using Interactive Devices E.
At 7:00 PM, the evening relaxation and monitoring task comes. Environment Control Device D creates a calming atmosphere, and Smart Bed B adjusts to an optimal relaxation position. After Patient A rests, the 6G network reallocates resources to maintain basic ward needs while prioritizing data AI analysis for Patient A’s recovery, providing insights for following medical evaluations which is used by the doctor to set the Daily Recovery Schedule next day.
Throughout the entire day, a continuous closed-loop AI learning process occurs: Devices (Smart Bed B, Care Robot C, Wearable Health Device Set W, etc.) capture Patient A's behaviors, preferences, and responses to treatments. This behavioral and physiological data, based on the different capabilities of collection devices, may first undergo AI inference at the device level before transmitting results to the 6G network for further inference and model updates, or directly transmit raw data to the 6G network for comprehensive multi-source data fusion, cross-patient pattern recognition, and complex healthcare model training. The updated model is then pushed back to UE devices, allowing them to dynamically adjust their behaviors and interventions based on the refined understanding of Patient A's needs and preferences. This continuous AI learning system ensures increasingly personalized care delivery and is used by the doctor to set the Daily Recovery Schedule for the next day.
6.30.4 Post-conditions
Patient A successfully completes the recovery day with all tasks and activities executed as planned, supported by the Smart Healthcare Ward and the 6G network. Based on the collected data and AI analysis of the day's recovery progress, the doctor updates the next day's recovery plan to optimize Patient A's ongoing rehabilitation.
6.30.5 Existing features partly or fully covering the use case functionality
Current 3GPP systems support communication services for AI-related tasks (e.g. AI/ML FL) as specified in TS 22.261 [14]. SA6 also has the study about Vertical UEs group (e.g. group management services) provided in TS 23.434 [247], and AI/ML enablement layer is designed to support ML model distribution/training/inference for vertical applications in TS 23.482 [190]. However, the network cannot provide AI services to enable and enhance the collaboration between Vertical UEs for AI-related tasks.
6.30.6 Potential New Requirements needed to support the use case
[PR 6.30.6-1] Subject to operator’s policy and user consent, the 6G network shall be able to support mechanism for AI application on UE to invoke AI services provided by 6G network.
[PR 6.30.6-2] Subject to operator’s policy, the 6G network shall be able to provide AI service to enable collaborative task for AI applications running on multiple UEs.
6.31 Use case on UE-Network collaboration with AI capabilities
6.31.1 Description
In the 5G system, research on AI mainly focuses on the field of discriminative AI, such as speech recognition, image recognition, and video processing. The core task of discriminative AI is to classify, recognize, or predict input data. However, the rise of generative AI, especially LLMs, has brought new challenges and opportunities to the AI field. Compared to discriminative AI, generative AI requires higher creativity and flexibility, because it not only needs to understand the structure and patterns of data, and also be able to generate new data that conforms to these structures and patterns.
By deploying some AI models (possibly small LLMs) on the UE, UE can acquire AI capabilities such as real time translation, call summarization, image generation and intelligent question answering. Users can easily activate AI capabilities of UE through diverse interactive methods such as voice commands and gesture recognition. Once activated, UE accurately captures user intent and quickly executes AI tasks, which brings unprecedented personalized intelligent service experience to users.
However, UE often faces severe challenges when independently undertaking AI tasks entrusted by users. These challenges may stem from the inherent limitations of UE in terms of perception accuracy, computing power, storage space, and power consumption. Additionally, AI tasks undertaking in the UE may also be affected by the complex and dynamic network environment. Especially when dealing with computationally intensive or data intensive tasks such as generative content creation and holographic communication, UE may result in lower task execution efficiency and quality and even fail to complete AI tasks.
Therefore, we suggest that UE with AI capabilities can rely on the collaboration between UE and network to achieve complex AI tasks. More precisely, the network will play a critical role in sharing responsibility for the inference of complex AI tasks for users. UE with AI capabilities can subscribe to the high priority AI services of the network. Then the UE can obtain support from the network to jointly complete AI tasks. Specifically, UE can request network to coordinate LLMs, storage resources, and computing resources to provide necessary assistance for the inference and execution of AI tasks. These LLMs, storage resources, and computing resources are provided by the operator's server clusters or computing cent[[SUGGESTION_START]]r[[SUGGESTION_END]]es, or by external 3rd party servers.
In order to implement UE-Network collaboration, we hope that network can achieve unified management of UE with AI capabilities. In other words, the network can comprehensively perceive and manage key information of UE with AI capabilities such as AI models deployed on UE and resource utilization status. Meanwhile, the network can also provide relevant information of AI capabilities it supports to UE. So, UE can optimize AI task execution strategies based on its own and the network's AI capabilities. That is to say, UE will intelligently offload some AI tasks to the network for execution, in order to fully utilize the high-performance computing resources and efficient data processing capabilities of the network.
In addition, the network may be expected to monitor the network environment in real time to achieve optimized resource allocation and improved system efficiency. The network is also suggested to be capable of accessing trusted data sources (e.g. from operator or 3rd party), which provides powerful data support and decision-making basis for the completion of AI tasks. If the data sources are deployed within the operator's network, it can achieve fast data retrieval and reduce transmission latency. If the data sources are deployed on a 3rd party server, remote access should be achieved through a network interface.
We can imagine that when UE encounters AI tasks that cannot be independently processed, it will offload some complex AI tasks to the network for execution. UE first performs preliminary preprocessing on the data. This step involves data cleaning, format conversion, and preliminary feature extraction. Subsequently, UE will generate prompts based on the local personalized database. UE uploads the preprocessed data along with prompts to the network to request assistance from the network. After receiving the above data, the network splits complex AI tasks into multiple subtasks and dynamically and flexibly allocates these subtasks and resources to different entities. During this process, the network can intelligently schedule the operator's server clusters, computing cent[[SUGGESTION_START]]r[[SUGGESTION_END]]es, or external 3rd party servers to form a collaborative work network, efficiently completing tasks together and ensuring the speed and quality of task execution.
6.31.2 Pre-conditions
1) UE with AI capabilities has deployed some AI models and a local personalized database, which can accurately identify user needs and complete simple AI tasks.
2) UE has reported the relevant information of AI capabilities to the network, including AI models deployed on UE, resource utilization status etc.
3) The relevant information of AI capabilities supported by the network has been synchronized with the UE.
4) LLMs, storage resources, computing resources, and data sources are provided by the server clusters and computing centr[[SUGGESTION_START]]e[[SUGGESTION_END]]s of operators, or third-party servers.
5) Cindy, Bob, and Daisy have subscribed to the high-priority AI services of the network.
6.31.3 Service Flows
Taking LLMs, storage resources, computing resources, and data sources provided by the operator's server clusters as an example, as shown in Figure 6.31.3-1.
Social media influencer Cindy is live streaming the Summer Olympics on her phone. She is searching for the best shooting location in a crowded scene.
Cindy plans to use generative AI technology in live videos to generate Olympic mascots as her virtual assistant for real time interaction.
Cindy's phone AI capabilities are not sufficient to support this complex generative AI task. Therefore, Cindy's phone extracts features from the images captured by the camera and the audio recorded by the microphone and transmits the feature data to the network. At the same time, Cindy's phone will also synchronously send prompts generated based on the local personalized database to the network, requesting the network to generate Olympic mascots for real time interaction with Cindy.
After receiving the feature data and prompts sent by Cindy's phone, the network splits the generative AI task into multiple sub-tasks and quickly coordinates various entities in the network for inference. At this point, the network can effectively coordinate the LLMs, storage resources, and computing resources of the operator's server clusters. Finally, the network added an Olympic mascot that interacts in real time with Cindy in the live video. This network enables Olympic mascots to supplement athletes' personal information based on the data sources.
Meanwhile, Bob and Daisy are watching Cindy's live video on their respective phones.
Bob is watching Cindy's live video on the high-speed train. The network perceives that Bob's phone is in a poor network environment. Meanwhile, Bob's phone has the ability to restore feature data to its original form based on the local database. Therefore, due to the smaller transmission volume of feature data compared to audio and video streams, the network efficiently transmits the processed feature data to Bob's phone. After receiving these feature data, Bob's phone restores the feature data into real time smooth audio and video streams.
In contrast, Daisy is watching Cindy's live video at home. The network perceives that Daisy's phone is in a smooth network environment. However, Daisy's phone does not have the ability to restore feature data to their original data based on the local database. Therefore, the network generates high-quality audio and video streams and stably transmits them to Daisy's phone.
Figure 6.31.3-1: Generative audio and video streams enabled by UE-Network Collaboration
6.31.4 Post-conditions
The interactive live video of Cindy and the virtual Olympic mascot at the Olympic site is presented clearly and smoothly on Bob and Daisy's mobile screens.
6.31.5 Existing features partly or fully covering the use case functionality
In TS 22.261 [14] clause 6.40.2.1
Subject to user consent, operator policy and regulatory constraints, the 5G system shall be able to support a mechanism to expose monitoring and status information of an AI-ML session to a 3rd party AI/ML application.
NOTE 2: Such mechanism is needed for AI/ML application to determine an in-time transfer of AI/ML model.
In TS 22.261 [14] clause 6.40.2.
Subject to user consent, regulation, trusted 3rd party’s request and operator policy, the 5G network shall be able to expose information to assist the 3rd party to determine candidate UEs for data transmission via direct device connection (e.g. for AI/ML model transfer for a specific application).
NOTE: The information does not include user’s specific positioning and can include QoS information
Subject to user consent, operator policy, regulation and trusted 3rd party’s request, the 5G network shall be able to expose information of certain UEs using the same service to the 3rd party (e.g. to assist a joint AI/ML task of UEs in a specific area using direct device communication)
NOTE: The information does not include user’s exact positioning information.
In TS 23.482 [190] clause 4.1.
[AR-4.1-c] The AIML enablement layer shall support interaction with 3GPP network system to consume network and AI/ML support services.
As mentioned above, we can see that 3rd party AI/ML applications have limitations in obtaining monitoring and status information of UE's AI/ML sessions, which still rely on 5G system to provide. In the future, 6G networks will be able to fully utilize this inherent advantage to directly interact with UE and provide AI services for it. This will not only significantly reduce the latency of inference but also provide customized and prioritized AI services for UE based on real time monitoring and status information, thereby greatly improving service efficiency and quality.
And in TS 22.261 [14], it is mentioned that 5G systems can support AI/ML operation splitting between AI/ML endpoints, which is essentially AI/ML models splitting. And it only emphasizes the centralized processing of 5G network endpoints to perform the remaining parts/layers, without involving distributed processing of different entities and resources through 5G network endpoints. But we hope that the 6G network can have the ability to split a complex AI task into multiple independent and interrelated sub-tasks. Then, the 6G network can also coordinate and mobilize various entities and resources in the network to work together efficiently to complete these segmented sub-tasks. This ability will greatly enhance the processing efficiency and flexibility of AI tasks.
6.31.6 Potential New Requirements needed to support the use case
[PR 6.31.6-1] The 6G network or application enablement layer shall be able to manage and coordinate various AI tasks considering AI workload offloading into Service Hosting Environment.
6.32 Use case on disaster rescue planning enabled by network AI Agents
6.32.1 Description
This use case aims to describe the disaster rescue planning for multiple rescue robots enabled by the AI Agents deployed within 6G network (abbreviated as "network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents"). When a disaster strikes, unpredictable challenges such as collapsed buildings, deformed roads, and communication outages make the rescue extremely complex. AI Agent technology is rapidly rising in the field of artificial intelligence, which leverage technologies such as environmental perception, knowledge memory, and large-scale models (not necessarily the LLM) to decompose complex AI tasks into sub-tasks and formulate a series of actions to solve them. By leveraging network AI Agents for rescue planning, the rescue efficiency can be significantly improved, maximizing the protection of victims' lives and personal property.
From a technical implementation perspective, AI Agents can be categorized into various types such as reinforcement learning based agent, agent with transfer learning, LLM-based agent [185]. In 6G networks, multiple AI Agents may be deployed based on their actual capabilities, with each AI Agent potentially serving distinct functions (e.g. some of the AI Agents may be responsible for interpreting intent, while others may responsible for task decomposition). Leveraging network AI Agents, 6G network can provide intent based services to users, 3rd party application, and network entities.
The intents can be provided by users, 3rd party applications, network entities and other AI Agents, serving as input to network AI Agents for subsequent processing. In this use case, the intent is "execute the rescue mission with multiple rescue robots in region Z". Upon receiving the intent, the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents initiate the rescue planning and decompose the rescue into multiple operations and other standardized 3GPP service. This may specifically include: road obstacle sensing (sensing service), multi-robot rescue route planning (AI inference service), training obstacle avoidance models (AI training service), real time optimal route computation for rescue robots (computing service) and communication resource allocation for disaster zones (communication service).
In this case, the deployment of network AI Agents requires careful consideration of the following issues:
Reliability assurance: The decisions made by the AI Agents in 6G network may directly change the network status, parameters, configurations, etc. Due to the hallucination effect of LLM and the trial-and-error of reinforcement learning, decisions made by the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents in the network may sometimes be unreliable (e.g. when the intents exceed the AI Agent’s capabilities or the AI Agents receives malicious intents). In disaster scenarios, every decision is critical and potentially life-determining. When 6G network utilise network AI Agents to provide services, the network should have dedicated environments or tools (e.g. NDT) to verify the decisions made by the AI Agents, ensuring the decisions won’t cause network overload or fault. Only decisions that have been verified for reliability can be executed to change the network environment.
Efficient data transfer: This includes efficient data transfer between different network AI Agents, as well as between network AI Agents and other network entities. On the one hand, unlike the ML models given in TR 22.874 [248] and TR 22.876 [249], the network AI Agents require more complex and larger-scale models (e.g. LLM, large-scale reinforcement learning models, etc.) to translate the intent, decompose the rescue task, etc. The amount of data required for training is proportional to the number of parameters of the model [186]. Typically, an RL model capable of handling complex tasks requires approximately 10M to 100M parameters. LLMs have larger parameter counts. Lightweight LLMs require 1B to 10B parameters. The network AI Agents need to collect large amounts of domain-specific data from other network entities to train the model or fine-tune the pre-trained model. On the other hand, when receiving the service request, the network AI Agent needs to provide services quickly and accurately for specific regions. Due to geographical and linguistic characteristics of the certain region, the network AI Agents must perform operations such as model training or fine-tuning, downloading and sharing external knowledge bases, and invoking tools to enable it to serve the specific region swiftly and precisely. Additionally, to provide rescue planning services, the network AI Agents with different capabilities need to collaborate and exchange large amounts of information in real time, such as models, sensing data, network information, etc.
6.32.2 Pre-conditions
1. Operator A has deployed network AI Agents in its 6G network and is able to provide intent based services enabled by network AI Agents as well as other related 3GPP services (sensing, AI inference, model training, computing, connection, etc.) to users.
2. The rescue team in region Z, which owns multiple rescue robots, has subscribed to the services enabled by network AI Agents provided by Operator A.
6.32.3 Service Flows
1. Unfortunately, a massive earthquake has suddenly struck region Z, causing partial building collapses, and trapping numerous residents in the ruins. Due to aftershocks and other factors, remaining structures continue to face collapse risks. Moreover, rapidly evolving road conditions—affected by falling debris and other hazards—have become highly unpredictable.
2. The rescue team in region Z sends the intent (i.e. "execute the rescue mission with multiple rescue robots in region Z") to the 6G network.
3. The intent translation network AI Agent (the network AI Agent responsible for translating the intent) receives the intent and then translates the intent into structured information understandable by the task decomposition network AI Agent (the network AI Agent responsible for task decomposition) and send the information to it.
4. The task decomposition network AI Agent performs in-depth analysis by collecting extensive real time data from the current network and decomposes the rescue operation into the following sub-tasks:
AI inference service: Reason out an overall rescue plan for multiple rescue robots, including dividing rescue areas and planning optimal rescue routes.
Sensing service: Provide real time sensing of actual road condition for rescue robots.
Model training service: Fine-tune real time obstacle avoidance models for certain rescue robots, helping them avoid suddenly appearing obstacles.
Computing service: Provide computing service to the rescue robots with limited local computing resource to calculate the optimal route for each path segment based on real time road information.
Communication service: Dynamically allocates communication resources based on real -time and predictive network conditions to ensure communication QoS. Rapidly deploy region-specific network AI Agents for region Z to minimize service latency as much as possible by leveraging techniques such as transfer learning.
5. The task decomposition network AI Agent distributes the service requests to other relevant network entity (e.g. network functionalities or other AI Agents) responsible for the services.
6. The network entities responsible for related services receive the service requests and start to work. Each network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and network function performs its designated role while dynamically exchanging relevant information in real time, working in coordination to collectively accomplish the rescue mission.
7. The 6G network sends the information (e.g. sensing results, AI inference results) to the rescue robots, which then conducts rescue operations based on the received information.
8. The rescue robot provides feedback on actual rescue situations (such as successful rescues, failed rescues, and the reasons for failure) to the network AI Agents.
9. The network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent quickly performs self-reflection based on the feedback (e.g. collecting new data, fine-tuning the model, etc.).
6.32.4 Post-conditions
Thanks to the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents' comprehensive task analysis and precise orchestration of rescue robots, the rescue team successfully extracted the majority of victims from the rubble within the critical golden rescue time, achieving what has been hailed as a miracle in rescue history.
6.32.5 Existing features partly or fully covering the use case functionality
In 5G and 5G Advanced (Rel-18 and Rel-19), SA1 has studied use cases and requirements of AI/ML model transfer phase 1 and phase 2, which mainly focused on model transfer and big data transfer at application layer (TR 22.874 [248] and TR 22.876 [249]). In 6G, with the deep integration of AI and network, the efficient big data transfer between network AI Agents should be further studied.
6.32.6 Potential New Requirements needed to support the use case
[PR 6.32.6-1] The 6G network shall support mechanism, e.g. AI capabilities such as AI Agent, to provide suitable 3GPP service or combination of multiple 3GPP services to subscribers requested by received intent from the user.
NOTE: The mention of AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent doesn’t imply or preclude any architecture assumption or solutions.
[PR 6.32.6-2] The 6G network shall be able to provide mechanisms (e.g. by interacting with NDT) to ensure the reliability and the validity of the AI inference results (e.g. by verifying decisions made by the network).
[PR 6.32.6-3] Subject to operator’s policy and user consent, the 6G system shall provide mechanisms to efficiently collect, transfer, and process multiple types of data when providing multiple 3GPP services in response to an intent from a subscriber.
6.33 Use case on AI text-to-video generation supported by computing
6.33.1 Description
With rapid advancements of deep learning and natural language processing techniques in recent years, AI text-to-video generators have emerged as an advanced powerful tool that allows generation of videos from textual descriptions as shown in Figure 6.33.1-1. The generators make it faster for producing diverse content in proficient and economical way. However, high speed and high graphics quality AI text-to-video generation require support from a large amount of complex AI reasoning and graphic rendering [187]. Considering the limited capabilities, computing resources, and battery power of the terminal devices, these complex AI computing might not be able to run locally on the terminals with an ideal experience.
Fortunately, in addition to the terminal computing resource, there are numerous available computing resources spanning from in-network to edge and cloud [188], [189]. The 3GPP network can automatically detect the computing resources and network status, and intelligently optimize the computing tasks of AI text-to-video generation, such as determining a small amount of personalized settings or preferences based on historical data on the local device, and offloading the AI reasoning and graphic rendering, which are beyond the device’s local computing capabilities, to appropriate computing nodes and selecting and configuring optimal traffic forwarding paths between the user device and computing nodes to guarantee the high quality text-to-video generation and transmission.
Figure 6.33.1-1: AI text-to-video generation
6.33.2 Pre-conditions
John is a media worker. In his work, he often needs to make videos with various themes and styles to match different texts. For example, John might make a video tutorial for an appliance manufacturer based on a product's instruction manual or make a humorous short video to accompany a joke for an entertainment company, and so on. Since John needs to manually edit, splice and render the videos, he is not in high efficiency.
In this use case, computing resources resided in two kinds of places are included: one place is the core network/cloud where a larger amount of computing resources with more powerful computing capabilities could be hosted, and the other is service hosting environment at the edge which has fewer computing resources but closer to the user. These computing resources are connected to the 6G network through different UPFs.
6.33.3 Service Flows
Figure 6.33.3-1: Service flow of AI text-to-video generation supported by computing
1. As shown in Figure 6.33.3-1, John was given an assignment to create a how-to video based on a piece of text describing how to use a smartwatch. In order to reduce manual workload, John decided to use the AI text-to-video generation application V to help the production of this video.
2. John opens the application V on his old laptop, and inputs the tutorial text to be converted to video, as well as the video settings, such as duration = 60 s, resolution=1080p, frame rate = 24 fps, etc.
3. After receiving the text and setting parameters, the application V client on John's laptop found that what needed to be generated was a tutorial video, so it found the tutorial videos on smart glasses and smart screen which have been generated before by John using this application. Based on these videos, it simply summarised the general style of such videos.
4. The application V client found that making this video required too much computing to be done locally, therefore it requests the network for help. The application V client sends requirements to network via the standardized API. The network decomposes the requirements of the video generation task into requirements for reasoning and requirements for rendering and check with the topology of computing resources within the network.
5. Based on the condition of computing resources and network status, the network assigns different computing resources for the computing task of video generation: computing node N1 located in the core network/cloud for reasoning as it has more powerful computing capabilities, which can improve the reasoning speed and quality, and computing node N2 located in the edge service hosting environment for rendering, to reduce the bandwidth for transmitting the generated videos. And the network can also select appropriate UPFs (e.g. closest to the corresponding computing nodes to provide E2E QoS assurance) and configure optimal forwarding paths, including underlay transport network, for the laptop to connect with these computing resources based on detected real time compute and network status and traffic characteristics.
6. During the video generation procedure, the network side monitors the computing status of computing node. If the network side finds that the computing node cannot further satisfy the computing requirements, the network side is responsible for reselect a new computing node to further processing or continue the video generation.
7. The AI text-to-video generation computing task is offloaded to computing nodes N1 and N2, and is aggregated on the local device.
6.33.4 Post-conditions
With network assistance, John utilizes the AI text-to-video generator to obtain high quality videos quickly, thus greatly improving his work efficiency.
6.33.5 Existing features partly or fully covering the use case functionality
TS 23.482 [190] has supported procedures for AIMLE Server registration to FL repository as FL member. In-network or edge side computing node management has not been supported.
6.33.6 Potential New Requirements needed to support the use case
[PR 6.33.6-1] The 6G network shall support the capability to receive the requested computing requirements of 6G Computing Service.
[PR 6.33.6-2] Subject to operator's policy, application needs or both, the 6G network shall support the reselection of computing resource(s) inside Service Hosting Environment and support the continuity due to, e.g.: UE mobility.
6.34 Use case on 6G computing support for AI model inference
6.34.1 Description
The advantages for the 6G network to provide computing service are:
- The 6G network can provide computing support for AI inference to the end-user.
- The 6G network can select a suitable computing node per subscriber per service based on the request from subscriber or service provider.
- The 6G network can leverage the overall E2E latency (i.e. communication delay+ computing delay) budget of an application service by adjusting communication and computing resource on demand.
- The 6G network owns big volume of data (e.g. sensing or positioning data as environmental information) which can be used for high quality AI inference.
Offloading the computing task for on-device AI inference
With the advent of AI model, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent service will become a popular service in the near future. In order to provide the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent service individually, the RAG technique has been widely used, in which a personal knowledge base is created for each user. When a user asks a question to the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent, some prompts will be generated based on the personal knowledge base and then send the question and prompts together to the AI model for inference.
Figure 6.34.1-1: 6G computing support for AI model inference
In many circumstances, the required computing support for an AI inference (e.g. AI model/Gen AI based inference) task is so significant that the UE may not have sufficient computing power and knowledge database to run the AI model inference. In order to perform AI model inference, as shown in Figure 6.34.1-1, the UE may send the request (together with prompts) to 6G network and get response as a kind of computing task offloading. The 6G network may offer computing support for a UE to offload the computing task to the CN node, trusted edge server or remote AS in 6G network.
The computing load (computing operations) consumed for a specific AI model inference is proportional to the size of the AI model and the input/output tokens. Then the computing time can be derived based on the AI model size, input/output tokens, and the provided computing resources (how many computing operations to be finished in a period of time).
NOTE: The AI model may vary per different UE vendors or 3rd party application.
6.34.2 Pre-conditions
A company-X installed an AI model into the 6G network and rented some computing resource in the 6G network.
Alice has installed company-X’s AI model for helping her daily work.
6.34.3 Service Flows
Alice likes to use company-X’s AI model to assist her daily work (e.g. looking for some information, drafting some material). The usual work task is easy for AI model, and a AI model in cloud server can easily handle it and provide feedback in short time (several second)
One day, Alice wanted to use the AI model to analyze a large number of professional documents by asking the AI model to categorize, summarize the documents as customized by her. The analysis work was quite computing resource consuming, the application thus provided a notice to Alice that it would takes about 30 minutes, and recommends Alice to use “value added service” which leverages more computing resources to reduce the waiting time to only 5 minutes.
Alice accepted the recommendation by paying extra fees thus the application began to leverage more computing resources in local network (6G network) to accelerate the analysis.
Alice got the analysis result in 2 minutes with an accurate analysis result, including the list of key areas mentioned in documents, the proposal from each different author, and the validity and availability of the proposals.
6.34.4 Post-conditions
Thanks to the 6G provided computing service, Alice can get the inference feedback with accurate and timely analysis result.
6.34.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] clauses 6.40 and 7.10 describe the communication functionality and KPIs for AI/ML model transfer in 5GS. The current requirements are assuming the 5G network is performed as a data pipeline only. However, 6G network may get deeply involved for an AI/ML operation by providing computing power, data, model and enhanced connections.
Thus, it requires more functionality and KPIs for 6G network to perform AI/ML operation as a native AI service.
6.34.6 Potential New Requirements needed to support the use case
[PR 6.34.6-1] Subject to operator policy and user consent, the 6G network shall be able to authorize a user to offload task from a 3rd party application (e.g. an AI inference workload) to the Service Hosting Environment.
[PR 6.34.6-2] Subject to operator policy, the 6G network shall support selection of computing resources in the Service Hosting Environment for offloading tasks.
[PR 6.34.6-3] Subject to operator policy and user consent, the 6G network shall be able to prioritize the processing of offloading task request(s) from a high-priority user per service.
NOTE: the prioritization of processing of computing is with more stringent requirement from high-priority user.
6.35 Use Case on 6G native AI in multi-domain convergence
6.35.1 Description
In 5G, network can provide analytics by NWDAF. NWDAF can only give the prediction or statics related to UE mobility, Network states, etc[[SUGGESTION_START]].[[SUGGESTION_END]], while how to utilize these analytics gracefully is not given. In 6G, network will be more intelligent by providing more complex scenario analytics and suggestions and AI-centric combing the latest research from R19 and R20. Ubiquitous AI can be everywhere and deeply integrated with Sensing, NTN, IMS, etc.
In this proposal, it’s a beginning with combining to Sensing, while more use cases related to other topics will be deplored later on. This use case demonstrates intelligent public warning by the start from road condition monitoring, searching and predicting affected users and maybe even user applications including UEs and V2X, and notify end users or related AFs to prevent from walking or driving into affected areas by 6G system.
6.35.2 Pre-conditions
Figure 6.35.2-1: Network support for 6G ubiquitous and native AI in Multi-domain convergence
As shown in Figure 6.35.2-1, the smart city road is covered by 6G network. Users are walking, riding and driving on this road. Some users are using navigation software, while users are not. When an urgent event suddenly happens (earthquakes, traffic accidents, flooded street, etc.), some part of the road is blocked. Some users will be affected, to prevent accidents and avoid entering into blocked area, users should be informed to slow down or pass by in time and in advance.
6.35.3 Service Flows
1. On an urban road which is covered by 6G system. Some stones suddenly crash the road from right to left direction, that crash area is a target area.
2. User A is moving into the target area by walking, User B is moving into the target area by driving, User C is departing from the target area and User D and E are on the opposite lanes.
3. 6G system can detect the abnormal status by Sensing or notifications from non-3GPP ways. 6G system can collect, aggregate and analyze the 3GPP and non-3GPP sensing data related to the abnormal status, while keeping user privacy information protected, then “picture” the affected target area.
4. 6G network can get the affected users in the target area and predict the most probably affected users who will enter into the target area in the future.
5. For the affected and probably affected users, 6G network can send notifications to users automatically through SMS, VoLTE [357] calls or even applications (vehicle system or navigation software). For serious conditions, the network can contact appropriate emergency services as well.
6. 6G Network keeps monitoring the affected area, affected and probably affected users. If the area is back to normal, 6G network stops the notification of warning or send the notification for ending warning.
6.35.4 Post-conditions
Users get out of the target area quickly or avoid entering into the target area.
6.35.5 Existing feature partly or fully covering use case functionality
SA2 has identified UE mobility statistics or predictions in TS 23.288 [114].
6.7.2 UE mobility analytics
NWDAF supporting UE mobility statistics or predictions shall be able to collect UE mobility related information from NF, OAM and to perform data analytics to provide UE mobility statistics or predictions.
SA1 has identified monitoring for environmental conditions in TS 22.137 [6].
5 5G wireless sensing service functional requirements
5.1 Description
The 5G system is expected to meet the service requirements for 5G wireless sensing service, which provides capabilities for sensing one or more objects in the environment, monitoring environmental conditions, and human motion and gestures to enable more diversified applications.
6.35.6 Potential New Requirements needed to support the use case
[PR 6.35.6-1] Subject to regulatory requirements, operator’s policy and user-consent, 6G network shall provide the result of AI task (e.g. AI analysis or prediction) to certain application on UE(s) or authorized third parties in certain area.
6.36 Use case on AI/ML model managed service for intelligent vehicles
6.36.1 Description
The era of AI affects and transforms every industry. In particular, transportation industry, which is closely related to people’s travel and lives, has been significantly affected. Autonomous Driving (AD) technology enables vehicles to perform driving tasks independently without human intervention, which helps vehicles become more intelligent and move toward a new vehicle paradigm. Meanwhile, Intelligent Driving (ID) technology aims to improve the safety and driving experience of the vehicles, as well as the efficiency of the entire traffic by leveraging AI, communication networks, and intelligent transportation infrastructure, to solve the issues caused by the expansion of vehicle volume and the complexity of urban roads. Normally, ID systems will be equipped with various AI/ML models tailored to different traffic demands, such as real time traffic flow prediction, dynamic route planning, driving decision, collision warning and avoidance, and etc., and be able to choose appropriate AI/ML models for different traffic and road conditions. The application of AD and ID is changing the driving mode, the driver’s role and behaviours. And their collaboration gives rise to new business models and services of the transportation industry, such as robotaxis, robo-shuttle, autonomous last-mile delivery vehicles, etc.
However, how to keep up with the pace of the entire industry raises new challenges to the vehicle manufacturers. They may choose to develop their own ID system, or integrate the third party’s modules, but the gap of technical capabilities and engineering practices in applying AI is leading to differentiated performance of vehicles and behaviours. For example, the AI/ML models for driving decisions vary among different ID systems, which may lead to different driving decision under the same situation. The incompatibility of the inference results may even cause driving decision conflicts and then pose safety hazards. Besides, for the vehicles used for public transportation e.g. robotaxis, the management platform also needs to mitigate the gaps of compatibility and performance among ID systems from different vehicle vendors, and the capability of AI model training and generation in order to provide consistent user experience. Those mentioned problems create new business opportunities for the MNO to participate in intelligent driving field of ToB-ToC market beyond the basic connection services, e.g. host, generate and distribute AI/ML models for all the connected vehicles via the 6G network.
It’s possible for the 6G network to offer a so-called AI/ML model managed service to subscribed users as the figure shows, which may include the management and maintenance of traffic AI/ML models that are trained or fine-tuned based on in-network data on the edge computing servers or acquired from the trusted 3rd party, selection of suitable AI/ML models for subscribed users based on their demands and transmission of AI/ML models to the vehicles or other applications.
With the ubiquitous connectivity and powerful infrastructure, 6G network will be advantageous to:
Facilitate the unification of the models: the 6G network can integrate AI/ML models from different sources and centrally manage all the AI/ML models and distribute them to any connected vehicles. As a trusted partner, the MNO can work with the transportation authorities to build up the models compliant to the regulation and with better applicability.
Reduce conflicts and improve efficiency: the usage of unified AI/ML models among all traffic participants can avoid the conflicts and issues caused by the difference of the models in ID systems.
Lower the door of intelligent driving and create new business models: centralized management of AI/ML models enables cloud intelligent driving services for vehicles, so that the vehicles with limited computing capability, restricted or low-performance AI/ML models can benefit from ID when connect to the network.
Provide exclusive models and specific services: Leveraging the wide coverage, 6G network can collect multiple types of traffic data (e.g. pedestrians, vehicles, roads, environments) and generate exclusive AI/ML models on edge computing servers. Compared with the generic models supplied by the vehicular network vendors based on tests and simulations, the exclusive models are more regionally applicable and adaptable to the needs of ID.
Providing AI/ML models with high-timeliness: Local model training by vehicles brings computational overhead and latency, failing to meet the timeliness of traffic models. For example, local trained models by vehicles in high-speed mobility may become invalid due to the location changes. In contrast, 6G networks continuously collect data and real time update AI/ML models on different edge computing servers and can allow the user to obtain the latest models immediately without waiting for training.
This use case describes how the 6G network provides AI/ML model managed services to Robotaxis of the taxi operator in order to offer consistent user experience to the passengers as Figure 6.36.1-1 shows.
Figure 6.36.1-1: AI/ML model managed services for intelligent vehicles
6.36.2 Pre-conditions
The network operator XNet has deployed AI/ML model managed services in its 6G network.
There are various basic transportation AI/ML models provisioned in the network.
The network can collect traffic data such as accident events, maps from the trusted 3rd party (e.g. transportation authorities, map vendors).
The network supports 3GPP sensing services.
The network is capable of training the models and fine-tuning the models with sufficient compute resources.
The robotaxi operator XKTaxi has an SLA with the network operator XNet for the communication services, AI/ML model managed services and other services for all the robotaxis (e.g. RoboTaxi A) operated by XKTaxi.
All the robotaxis are intelligent vehicles, and capable of deploying AI/ML model locally.
6.36.3 Service Flows
1. RoboTaxi A receives one order from the taxi scheduling platform for the guest from the airport to the downtown hotel. Then RoboTaxi A plans the route based on the local AI/ML model of path planning obtained from the network before.
2. RoboTaxi A identifies the planned route including viaducts and high-dense urban roads, so it sends the request to the 6G network to get the AI/ML model of the driving decision specific for the viaduct region and high-dense regions. In parallel, it subscribes to the service of updating AI/ML model of path planning for the target region.
3. The 6G network generates and/or selects proper AI/ML models based on the request information (e.g. model type, target location, vehicle’s AI capability), the SLA and the policy, and then allocates computing resource (e.g. edge computing server) for training and transmitting the selected latest AI/ML model to RoboTaxi A under requested QoS when RoboTaxi A approaches the target region.
4. RoboTaxi A deploys the received AI/ML model locally and makes real time driving decision timely and correctly.
5. During the journey, the 6G network will train the models of path planning based on collected traffic information from other services (e.g. sensing) and the trusted 3rd parties, as well as traffic prediction models acquired from meteorological department. Once there is a new model available, RoboTaxi A will get the notification from the 6G network and obtain the updated models in proper timing and location.
6. RoboTaxi A re-plans the route based on the updated AI/ML models and makes the driving decision with the up-to-date inference result for the impacted regions.
6.36.4 Post-conditions
RoboTaxi A takes the passengers to the downtown hotel safely and quickly.
6.36.5 Existing features partly or fully covering the use case functionality
Table 6.36.5-1: Existing features and gap analysis
Specifications and clause
Gap Analysis
Existing Requirements/Features
TS 22.261 [14], clause 6.40.2.1 and clause 7.10.1
5G network only provides the connection for AI/ML model transfer, not involved in the AI/ML model training, generation and distribution.
QoS management and KPIs for AI/ML model transfer between UE(s) and Network Server/Application function have been specified.
TS 28.105 [139]
The functionalities and tools are just used for the users in the network (e.g. MDA, see TS 28.104 [251], NWDAF, see TS 23.288 [114]), but can’t support the use cases of the subscribers in the need of AI/ML model management.
And the mechanisms to store, select and generate AI/ML models in the network were not specified in 5G.
The requirements and features of generic AI/ML management related capabilities and services to support the AI/ML techniques used in the management of 5G network have been specified, including ML model training, ML model testing, AI/ML inference emulation, ML model deployment and AI/ML inference.
6.36.6 Potential New Requirements needed to support the use case
[PR 6.36.6-1] Subject to operator’s policy, the 6G network shall be able to store and train authorized 3rd party’s AI/ML models inside the Service Hosting Environment.
[PR 6.36.6-2] Subject to operator’s policy, the 6G network shall be able to select or generate AI/ML model(s) from the stored AI/ML models inside Service Hosting Environment upon 3rd party application’s request (e.g. model type, target area, requested AI capabilities for deployment, etc.) for the application’s use.
NOTE: The algorithms used to generate a new AI/ML model are out of 3GPP scope, which may include model training, model aggregation, model pruning, etc.
[PR 6.36.6-3] The 6G network shall be able to collect charging information for the usage of AI/ML models that are stored or generated within the Service Hosting Environment.
6.37 Use case on energy efficiency for AI service
6.37.1 Description
AI services deliver transformative value across industries by enabling data driven decision automation, predictive analytics, and cognitive process optimization – particularly in smart manufacturing, precision healthcare, and autonomous transportation systems.
Climate change and the rising consumption of energy motivate increased energy efficiency. Energy efficiency is a strategic priority for operators around the world. However, AI services inherently consume significant resources. AI training demands high-performance computing clusters, and inference requires continuous processing power.
Studying the energy consumption for AI services is critical. It enables operators to quantify the energy usage of each AI task, facilitating management of computing nodes with varying efficiency. This empowers dynamic optimization strategies to reduce AI services related energy waste, improve resource utilization, and accelerate progress toward a sustainable, low-carbon society.
6.37.2 Pre-conditions
There are several AI computing nodes deployed in the network, which can provide computing resources for AI inference and AI training.
The network can calculate the energy consumption information for an AI task.
The 3rd party entity has sustainability requirements for AI tasks.
6.37.3 Service Flows
1. A 3rd party entity needs an AI model for autonomous driving, but it lacks model training capability. To meet the sustainability requirement, the 3rd party also concerns about the energy consumption for the AI model training task. The 3rd party then initiates an AI model training request to the network, requiring model training and the energy consumption information.
2. Based on the request received from 3rd party, the network retrieves the profile of the computing nodes (computing capabilities, energy consumption information) available for the AI training task.
3. The network identifies three available computing resources:
- Node A: 90% renewable energy, 10% conventional energy, medium energy consumption;
- Node B: 50% renewable energy, 50% conventional energy, medium energy consumption; and
- Node C: 20% renewable energy, 80% conventional energy, high energy consumption.
To minimize carbon emissions, the network selects Node A, which has highest clean energy ratio and moderate consumption.
4. The network delivers the AI training task to Node A and requests contextual parameters (e.g. computing resources, data volume, etc.) for energy consumption monitoring.
5. Node A completes the AI training task and returns both the trained AI model and contextual parameters to the network. The network calculates total energy consumption for the AI model training task based on the received computing resources consumption and data volume.
6. The network sends the trained AI model and its energy consumption information to the 3rd party entity.
6.37.4 Post-conditions
Based on the trained AI model, the 3rd party entity can achieve AD. With the information received from the network, the 3rd party entity can meet the sustainability requirement.
6.37.5 Existing features partly or fully covering the use case functionality
In clause 6.15a of TS 22.261 [14], the following requirements of Energy Efficiency as a Service Criteria are captured:
Subject to operator’s policy and agreement with 3rd party, the 5G system shall be able to monitor energy consumption for serving this 3rd party.
Subject to operator’s policy and agreement with 3rd party, the 5G system shall be able to expose information on energy consumption for serving this 3rd party.
In above requirements, the information considered only relates to the use of the communication service [14]. The information related to AI services (e.g. AI inference, AI training) is not taken into consideration.
6.37.6 Potential New Requirements needed to support the use case
[PR 6.37.6-1] Based on operator's policy and agreement with 3rd party, the 6G network shall support monitoring energy consumption for an AI service (e.g. inference) requested by 3rd party.
[PR 6.37.6-2] Based on operator's policy and agreement with 3rd party, the 6G network shall support exposing energy consumption information of an AI service to 3rd party.
[PR 6.37.6-3] Based on operator's policy and agreement with 3rd party, the 6G network shall support a mechanism to assist in selecting computing resources inside Service Hosting Environment for AI service considering requirement of the 3rd party.
6.38 Use case on AI for disability support
6.38.1 Description
As AI evolves, it will further improve human communication, enabling proactive communication with people, devices, and surroundings. This includes hearing aids with complete noise cancellation and the ability to tune in on specific sounds, visual aids enabling recognition beyond normal vision, and communication aids using eye movements or brain interfaces. This can have a revolutionary enabling effect for people with various disabilities and people in challenging situations where typical human capabilities are insufficient, allowing them to recuperate some of the impaired capabilities through the aid of AI-powered tools.
AI enhanced communication can be about person-to-person communication, for example a phone/video call, or person-to-AI function communication, where the AI function/agent assists the person in orientation, understanding, and information processing.
AI enhanced communication can also be applicable to IMS emergency communications when a user with disabilities communicates with a PSAP.
Sustainability impact analysis
Use of AI for disability support is valuable and has benefits within health & human wellbeing area. However, challenges regarding trustworthiness and energy needs to be considered
Energy resources
From the environmental perspective, energy-efficient AI models and increased reliance on renewable energy sources could help minimize the risks. Sources of electricity for AI devices and operations is an important factor as it could impact emissions to air, water and soils.
Trustworthiness
Ethical challenges include determining responsibility - for instance, assessing the impact and entity accountable in the event of technology malfunctions or if developers or providers decide to discontinue support. Determining the appropriate level of control over current and future technology functionality, given the technology’s intimate nature. Ensuring the technology’s value alignment, particularly concerning privacy and integrity as the technology is likely to collect, analyze, and store highly sensitive user data. Other issues in terms of value alignment are mitigating bias, ensuring that the technology works for everyone, and identifying the beneficiaries of the technology (e.g. those with the greatest need versus those with the greatest ability to pay). In evaluating trustworthiness, several aspects must be considered. These include the functionality, reliability, and benevolence (friendliness) of the technology itself, as well as external factors such as the trustworthiness of developers and providers, business models, data management practices and adherence to policy and regulations
6.38.2 Pre-conditions
The following pre-conditions are considered:
A user has impaired vision, hearing, mobility, or orientation capacity, due to either disabilities or environmental factors that affect bodily functions.
The user is equipped with a device capable of sending video and audio streams, such as smart glasses with camera and microphone. The device is also capable of receiving audio streams and optionally video streams.
6.38.3 Service Flows
The following service flow is considered, involving an application that can be part of the network (e.g. as an IMS application) or external:
Immersive video (RGB and depth) and audio stream is captured on user device.
In an application, the video and audio streams are analysed for user input (cues, intents)
User gesturing, audio cues, eye tracking, and field-of-view tracking (what the user is looking at).
In an application, the video stream is further analyzed, and situational awareness is established.
Based on audio, data input, or visual cues from the user, the application adds contextual information to the visual and/or audio stream or modifies the incoming audio stream. This may include:
Scene understanding: answering questions “What am I looking at?”, “Explain what is happening”. Adding digital tags in field-of-view.
Orientation: answering questions “Where am I?”, “How do I go to X?”. Adding digital arrows in field of view.
Visual aid: text in field-of-view read out in audio.
Audio aid: amplification, enhancing contrast, simplification or translation of audio stream.
Communication aid: assistance in communication with others, e.g. through translating user intents (gestures, eye movements, field-of-view tracking, etc.) to audio stream.
Reminders: assistance in remembering appointments etc., e.g. through audio or digital messages in field-of-view.
A virtual overlay of contextual and modified information, in the form of video and/or audio streams, is transmitted to user devices.
The above service flow can also be applicable to IMS emergency communications service with a PSAP.
6.38.4 Post-conditions
Users get orientation, scene understanding, and adaptive communication support from the application through their devices and can thereby become more independent and capable.
6.38.5 Existing features partly or fully covering the use case functionality
Some forms of assistance can be implemented as an over-the-top service, on top of the existing mobile broadband connections. However, in the next subsection this contribution analyses the requirements for arrangements where the network involvement provides an enhanced service.
6.38.6 Potential New Requirements needed to support the use case
The following requirement for AI for Disability support are derived from the use case above.
[PR 6.38.6-1] Based on operator policy and user consent, the 6G system should enable the IMS services enhanced by AI capability, to enhance the audio and video stream for disability support.
NOTE: The disability support can be e.g. describing and explaining the surroundings, audio aid (modification of the audio stream i.e. enhancements, clarifications, translations, speech to text, text to speech, etc[[SUGGESTION_START]].[[SUGGESTION_END]]), visual aid (modification of the visual stream).
[PR 6.38.6-2] Based on operator policy, user consent and regulatory requirements, the 6G system may be able to enhance the IMS emergency communication service with AI capability, to enhance the audio and video stream for disability support.
[PR 6.38.6-3] The following KPIs in Table 6.38.6-1 should be supported, in Dense Urban, Urban, and Rural indoor/outdoor scenario:
Table 6.38.6-1: Performance requirements
Profile
Characteristic parameter
Bit rate down link
Bit rate up link
Application round trip latency
Communication service availability
Overall user density
Activity factor
UE speed
AI for Disability support
[1-2] Mbit/s
[50-100] Mbit/s (note 1)
[300-1000] ms
(note 2)
Dense Urban [99.9]%
Urban [99.9]%
Rural [99]%
Dense Urban
[2 500] / km2
Urban [1 000] / km2
Rural [10] / km2
(note 3)
[50]%
5 km/h
Note 1: envisioned 4K resolution
Note 2: Input stream (video, audio, data) to output stream (video, audio, data) 300-1000 ms dependent on task including the disability application
Note 3: It is assumed that 10% of all users are using AI for Disability support
Editor’s Note The value of Application round trip latency are FFS
Editor’s Note The value of Bit rate up link are FFS
6.39 Use case on considerations on responsible AI
6.39.1 Description
It is envisaged to deploy AI-enabled solutions, even in a more native way for 6G, to raise efficiency and autonomous operation of networks. In addition, the exposure of AI-generated information towards 3rd party applications is foreseen. For all these AI applications it is recommended that their application is done in a responsible way, that means that the network entity, function, application, human or 3rd party, who decides on the application can do this in an informed way and has enough information to take responsibility for the deployment and use of application and their output. In other words, it can be trusted that the AI application will perform as expected, is robust enough in all foreseen circumstances. There are means and techniques available to provide this additional information about an AI application or used ML model summarized under the term Trustworthy AI.
A 3rd party application traffic light control needs to have predictions about the expected car density, maybe per cardinal direction, for a certain time period in advance in order to set proper light switching cycles to ensure smooth traffic flow. To avoid having own built-in means for detecting cars, recording the number of cars over time, and doing training of a forecast model using collected data, it is considered to use a subscriber density prediction service exposed by a mobile communication network, since this is a simpler and less costly alternative.
To decide in a responsible way, the traffic light control asks the network via the exposure interface:
Robustness: Is the AI application robust enough? What is the resilience score in case of missings in the input data, to take into account that radio links from UE to network can be interrupted. The network can respond with a missing resilience score for the used AI application and the traffic light control can decide if this score acceptable.
Explainability: Asking for some reasoning for a given prediction using local explanation techniques. E.g. The network is sharing the main reason as explanation why the subscriber density forecast for the given area is predicted as low.
For example, the criteria defined and described in ISO “AI - Assessment of the robustness of neural networks” [280] such as stability, sensitivity, relevance and reachability can be applied to define and assess robustness scores following a black-box approach so that it can apply to any kind of AI/ML implementation.
With this additional information the traffic control application can do an informed decision whether to use information exposed by the network or not. Robustness and sustainability information could also be used for a generic decision, if the prediction service exposed by the mobile network should be used at all.
In case there is a non-negligible risk that an application using AI can produce harm to humans, the use of means provided by Trustworthy AI techniques becomes even more important. For example, in the case of traffic lights control, frequent or fast traffic light changes may eventually lead to car accidents and material damages and human life loses. Therefore, it is of paramount importance for third party applications to have explanations of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent reasoning as well as explanations on the network analytics used, or even in some cases to require explainability by using it as criteria in their request. Criteria may indicate that the request for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent reasoning or explanations on the network analytics need to be provided together with the local explanations. If not supported, the request is not valid.
When regulatory requirements apply, it may even become mandatory for the third-party application owner to book-keep all the decisions that the third-party application took (along with the detailed reasoning for such decisions) to fulfil traceability obligations, e.g. due to either AI/ML regulations or vertical industry regulations (e.g. road safety regulations).
The Trustworthy AI principles are also applicable in Core and RAN with or without UE involvement. Network and UE functions, and associated services can provide additional information regarding robustness, explainability besides their performance indication. Functions, services or agents can then do an informed decision which function to deploy while considering all relevant aspects for a responsible deployment of AI.
Table 6.39.1-1: Potential sustainability impacts of the use case
(the UN SDGs/GDC matching goals of each aspect within 3GPP context)
Potential benefits of the use case (added value)
Potential areas of attention of the use case (risks to be mitigated)
Environmental sustainability aspects
(UN SDGs 12, 13, 14, 15 and indirectly 6, 7 & 11.
UN GDC “Develop principles for environmental sustainability of digital technologies”)
Energy resources
(UN SDG 7, 11, 12)
Enables sustainable choices of AI services, e.g. supports informed trade-off decision between functionality/performance and energy consumption
Emissions
(UN SDG 6, 7, 11, 12, 13, 14, 15)
Enables informed selection of AI services that do use renewable energy or that respect a maximum amount of carbon emissions
Socio-economic sustainability aspects
(UN SDGs 2, 3, 4, 5, 8, 9, 10, 11, 16 & 17 and indirectly 12.
UN GDC “Closing Digital Divides and Accelerating SDG Progress”
&
“Expanding Digital Economy Inclusion”
&
“Fostering an Inclusive, Safe Digital Space”)
Inclusion & Equality
(UN SDGs 11, 10, 4, 5 and indirectly 3, 16 & 17)
Limiting biases when making decisions
Trustworthiness
(UN SDGs 11 and indirectly 3 & 17)
Increasing security, integrity, robustness and trustworthiness of AI and applications through informed selection of AI service.
Work & income
(UN SDG 8 and indirectly 12)
Work-related processes (e.g. hiring) may have specific needs eg on explainability of AI workloads
Infrastructure
(UN SDG 9)
Some verticals may have specific needs eg on explainability of AI workloads for their own processes or regulations eg in the transport industry
TCO reduction
(UN SDGs 8, 9 and 12)
Controlling & optimizing deployment and operational costs e.g. through informed selection of most appropriate AI service.
6.39.2 Pre-conditions
A mobile network is offering a subscriber density prediction service for selected positions and cardinal directions via its exposure interface.
The “Traffic Light control” 3rd party application has access to a mobile network and is authenticated and has access to the exposure interface of the network.
6.39.3 Service Flows
The 3rd party application “Traffic Light control” looks for and finds a subscriber density prediction service offered by mobile network via an exposure interface.
The 3rd party application requests further service requirements for this service regarding the robustness, and explainability aspects of the AI/ML model, besides usual performance requirements like RMSE (root mean square error), accuracy, precision or any other performance score typically used for assessing the basic inference performance of an AI/ML model. E.g. in this information request the application asks for figures concerning robustness and prediction performance, sets a requirement for the energy consumption needed for inference, sets the requirement that local explanations need to be supported.
6.39.4 Post-conditions
The 3rd party application “Traffic light control” uses the subscriber density prediction service in a responsible way and takes benefits of the received predictions by having a more optimized cycle setting enabling a smooth traffic flow with minimized waiting times for cars.
The 3rd party application is solely accountable for its service supported by AI, because it took the informed decision to integrate the subscriber density prediction service.
The Traffic Light control itself could be implemented in a more lightweight and cheaper manner, because it does not need to have own traffic detection sensors and prediction capabilities.
6.39.5 Existing features partly or fully covering the use case functionality
In 5G network, exposing network analytics involving AI/ML to third-party applications is achieved through network APIs, enabled by the NEF and Common API Framework (CAPIF) [360] and the NWDAF.
6.39.6 Potential New Requirements needed to support the use case
[PR 6.39.6-1] Subject to the operator’s policy and regulatory requirements, the 6G network shall be able to receive AI-related service requirements from a service consumer (e.g. 3rd party) as part of a request for an AI service, for example related to AI inference accuracy and latency.
6.40 Use case on AI-driven multi-vehicle cooperative perception
6.40.1 Description
In the context of AD technology, reliable and timely perception is fundamental to ensuring both safety and efficiency [297]. Significant efforts have been made to enhance object detectors using state-of-the-art neural networks and multimodal sensors. However, standalone perception systems have an inherent limitation, as they rely solely on onboard sensors, which provide only line-of-sight information [298]. V2X communication technology addresses this limitation by enabling vehicles to exchange information, thereby facilitating a wide range of V2X applications, including cooperative deployment. Through data sharing with other cooperative vehicles, obscured objects can be detected, and the quality of perception can be substantially improved [299].
6.40.2 Pre-conditions
As shown in Figure 6.40.3-1, the vehicles on the road are autonomous and are equipped with wireless communication and sensing devices, along with AI-based encoders and decoders designed for cooperative perception. They also have appropriate identification and authorization mechanisms in place to connect to the V2X network.
6.40.3 Service Flows
1. During the operation of the EGO vehicle, its sensors continuously collect perception data from the surrounding environment. Due to the complexity of road conditions, the sensors can only provide line-of-sight information, leaving significant areas occluded.
2. The EGO vehicle analyzes its own perception data to identify regions where information is missing and sends a cooperative perception request to the V2X network to acquire perception data for these occluded areas.
3. Upon receiving the EGO vehicle's cooperative perception request, the base station authenticates the EGO vehicle. Based on real time network conditions and perception task requirements, the base station selects appropriate cooperative vehicles (e.g. CoV1–3) to establish communication links with the EGO vehicle. Additionally, it assists in optimizing transmission paths to ensure efficient and reliable data exchange.
4. The selected cooperative vehicles (CoV1–3) respond to the base station’s task assignment by processing their own perception data. They perform AI-based encoding to transform high-resolution perception information into semantic features, which include critical details such as object size, position, and velocity. These semantic features are then transmitted to the EGO vehicle via the V2X network.
5. The EGO vehicle receives the semantic features from CoV1–3 and inputs them, along with its own perception data, into AI-based decoder. The decoder fuses and reconstructs the data to generate a complete perception map that covers the previously occluded areas.
6. Throughout the journey, the base station continuously monitors the V2X communication network and dynamically adjusts communication links between vehicles. Based on real time perception needs, vehicle mobility, and communication quality, the base station coordinates task handovers or adds new cooperative vehicles to ensure that the EGO vehicle consistently acquires comprehensive and timely environmental information.
7. The base station ensures the security of data transmissions throughout the process by verifying the perception data sent by cooperative vehicles, preventing malicious attacks and data breaches.
Figure 6.40.3-1: Multi-vehicle cooperative perception
6.40.4 Post-conditions
During driving, EGO vehicles always have complete perception information to ensure driving safety.
6.40.5 Existing features partly or fully covering the use case functionality
TR 37.885 [96] provides the ITS operation regulations for V2X networks in the frequency band above 6 GHz, as well as evaluation methods including system-level simulation assumptions and channel models.
TR 38.885 [96] provides resource allocation, uplink and downlink, and QoS management for V2X networks.
6.40.6 Potential New Requirements needed to support the use case
[PR 6.40.6-1] Subject to user consent and operator’s policy, the 6G network shall provide efficient mechanisms to support the collaboration of UEs.
6.41 Use case on authentication and authorization for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents
6.41.1 Description
AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents demonstrate significant utility through their ability to interpret user intent and autonomously execute actions across devices. This functionality enables seamless fulfilment of user requirements while enhancing operational efficiency.
However, security remains a critical priority for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent deployment. As these systems operate on intent-driven logic, vulnerabilities arise in two key scenarios:
- Malicious intent: when users have harmful objectives (e.g. unauthorized users ask AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents to pretend to be authorized users), AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents may inadvertently execute actions detrimental to network integrity or user safety.
- Interpretation errors: misunderstanding user intent could trigger unauthorized or hazardous operations.
Authentication and authorization for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents are important. Authentication can guarantee the identity of the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent is authentic, which can ensure only the legal devices access the network. Authorization can guarantee the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can access only the subscribed services, which limits the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent access to only necessary resources, reducing the potential attack surface. For UE AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents, the network needs to identify whether the request sender is a UE or a UE AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent, as the security policy for the UE and UE AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent could be different.
Besides, authorization and authentication for the user who owns the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent devices are also critical. The information of the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and the corresponding user can be stored in the network. Authentication for the user of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent devices can verify who controls the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents. This can further exclude the risks of impersonation if credentials are stolen or devices are compromised.
With authentication and authorization for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents and related users, the network can mitigate potential threats to the networks and users.
6.41.2 Pre-conditions
An AR experience exhibition is held in TechFrontiers Pavilion, where participants wearing smart glasses with embedded AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can access personalized AR content.
The AR exhibition is open only to invited AR enthusiasts. The authorized AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent information (e.g. authentication information) is preconfigured in the network by the exhibition organizer.
The exhibition organizer and network operator have an agreement to provide every participant with multi-modal data transmission with ultra-low latency and high-bandwidth connectivity through the operator's network.
6.41.3 Service Flows
As Figure 6.41.3-1 shows:
1. Alice, Bob, and Cindy arrived at the TechFrontiers Pavilion wearing AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent integrated smart glasses. While Alice and Bob presented official invitations and registered their devices, Cindy—lacking an invitation—attempted unauthorized access by impersonating invited attendee Dale using his registered glasses.
2. All three attempted to launch ImmersiveGuide (an AR application) on their glasses. Their AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents automatically transmitted access requests to the exhibition network.
3. The network received three service requests: one each from Alice's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and Bob's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent via their own glasses and one from Dale's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent (initiated by Cindy via Dale’s glasses) .
4. The network performs the two-step authentication and authorization for the requests:
- AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent authentication. All three passed the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent authentication check as all the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents on the smart glasses (Alice's, Bob's and Dale's smart glasses) have registered to the event.
- User authentication. Alice and Bob passed the user authentication check[[SUGGESTION_START]],[[SUGGESTION_END]] but "Dale" failed the user authentication check as "Dale" is pretended by Cindy.
- Authorization check: both Alice's and Bob's smart glasses are authorized.
4. As a result, the requests from Alice's and Bob's AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents are approved, while Cindy's request from Dale's glasses is not approved due to failed user authentication.
5. For authenticated and authorized Alice and Bob, the network establishes dedicated high-reliability, low-latency, high-bandwidth data paths for Alice and Bob respectively.
6. Alice’s and Bob’s AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents received the multi-modal data from the network, locally rendering real time AR scenes tailored to their preferences. Due to the rejection, Cindy is unable to access the service.
Figure 6.41.3-1 AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents launch ImmersiveGuide Application
6.41.4 Post-conditions
Alice and Bob fully enjoyed the AR experience exhibition at the TechFrontiers Pavilion. The network can mitigate potential threats via the authentication and authorization check.
6.41.5 Existing features partly or fully covering the use case functionality
In clause 6.41 of TS 22.261 [14], the following requirements of providing access to local services are captured:
The 5G system shall be able to authenticate and authorize the UE of a user authenticated to a hosting network to access the hosting network and its localized services on request of a service provider.
From above requirements, the authentication and authorization of a UE is supported when accessing the hosting network and localized services.
The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can be deployed on the devices (e.g. UE or 3rd party device). The network needs to identify whether a service request is sent from the device or the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on the device. From a security perspective, the network can avoid attacks from the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent due to malicious intent and interpretation errors. From a service perspective, the network can differentiate UE originated traffic and AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent originated traffic, allocating QoS based on their distinct traffic characteristics and service requirements. From a policy perspective, the network can implement different policies to prevent abuse of user's authority and enforce operational boundaries.
For AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents, the authorization and authentication are also important. Authentication can guarantee the identity of the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent is authentic, which can ensure only the legal devices access the network. Authorization can guarantee the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can access only the subscribed services, which limits the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent's access to only necessary resources, reducing the potential attack surface.
Besides, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent is always related to the user. The information of the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and the corresponding user can be stored in the network. Authentication for the user can verify who controls the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents. This can further exclude the risks of impersonation if credentials are stolen or devices are compromised.
Thus, new requirements for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent authorization and authentication, and also related user authentication are essential.
6.41.6 Potential New Requirements needed to support the use case
NOTE: The 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent refers to the 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent application on UE.
[PR 6.41.6-1] The 6G network shall support a mechanism to authenticate and authorize 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent.
[PR 6.41.6-2] Based on the operator's policy, the 6G network shall support a secure mechanism for authenticated and authorized 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to invoke 3GPP services.
6.42 Use case on AI-assisted multi-modal communication service
6.42.1 Description
In recent year, multi-modal communication service has drawn great attention. Multiple use cases have already been defined in various specifications. For instance:
As defined in TS 22.261[14]: tactile and multi-modal communication services etc.
As defined in TS 22.156 [28]: collaborative and concurrent engineering, AR enabled immersive experience etc.
While the previous use cases mainly focus on the KPI of modality, transcoding between different modalities and media synchronization, there are certain limitations. For instance, collaborative and concurrent engineering defined in TS 22.156 [28] requires sophisticated sensor setup to reconstruct the 3D model in the virtual space. However, most factories do not have such condition to setup a dedicated lab for virtual meeting.
On the other hand, the emergence of AI technologies, especially with multi-modal large model [265], can fill the gap. The AI enables the media transformation between, e.g. image to video, video to 3D model [264], text to video. This addition enhances the user experience and pose new requirements on the network.
6.42.2 Pre-conditions
SyncSphere, a tech start-up, has set up a virtual meeting environment (with the corresponding 6G communication subscription provided by UniUpMobile) for collaborative and concurrent engineering in their customized laptop design. The engineer from SyncSphere can join this virtual space remotely with various UE, e.g. smart phone, VR glasses etc. The user can freely move their point of view and interact with the 3D model presented in the virtual space depends on the UE capability.
May, Hua and John are three engineers from SyncSphere. They need to setup a virtual meeting to finalize their customized laptop design for their customer, and multi-modal communication session/sessions need to be established to support this virtual meeting. May and Hua are in their own office and join the meeting with their VR glasses. John is in the laptop factory, which lack the bandwidth to support a pair of VR glasses. So, John joins the meeting with his smart phone. John’s smart phone does not support generate 3D model of the laptop.
John, as a 6G subscriber from MNO EchoMobile, subscribes to the AI-assisted multi-modal communication service provided by EchoMobile. The AI-assisted multi-modal communication service support multiple multi-modal media transformation, e.g. the video data format to 3D model data format. The laptop factory does not have the environment and equipment to setup dedicated sensors to reconstruct physical laptop to a virtual 3D laptop model which can be viewed in the virtual space. John plans to use his smart phone to record the video of the physical laptop, generate the 3D model with the help of AI-assisted multi-modal communication service, and send the generated 3D model to the virtual space for his colleague to check. EchoMobile has an agreement with John to protect the data security and data privacy of the media during the usage of the service.
6.42.3 Service Flows
1. May initiates the virtual meeting in her office with her VR glasses. Hua and John join the meeting and multi-modal communication session/sessions are established. This can be done by means of the IMS (including IMS CN with Data Channel capability) or via OTT applications. The virtual meeting environment is hosted on SyncSphere’s server with the corresponding 6G communication subscription provided by UniUpMobile.
2. When the session starts, multiple streams are established over the 6G network between the corresponding devices that carry multi-modal data. May, Hua and John can hear each other. May and Hua can freely move their point of view in the meeting space. John can see the meeting space from a fixed point of view using his smart phone.
3. John initiates the AI-assisted multi-modal communication service and start to record the physical laptop using the camera on his phone. With the help of AI capabilities in the 6G system, the AI-assisted multi-modal communication service takes the video stream as input and generates a laptop 3D model. The generated 3D model is sent to the virtual meeting space, which May and Hua can see and interact with it. The 6G system provides data security and protects data privacy during the media transmission and usage of the AI capability.
4. After finalizing the design, all participants leave the meeting.
6.42.4 Post-conditions
May, Hua and John complete the computer design efficiently with the help of AI-assisted multi-modal communication service provided by 6G system.
6.42.5 Existing features partly or fully covering the use case functionality
Requirements for the multi-modal communication service are defined in TS 22.261 [14]:
The 5G system shall enable an authorized 3rd party to provide policy(ies) for flows associated with an application. The policy may contain e.g. the set of UEs and data flows, the expected QoS handling and associated triggering events, other coordination information.
The 5G system shall support a means to apply 3rd party provided policy(ies) for flows associated with an application. The policy may contain e.g. the set of UEs and data flows, the expected QoS handling and associated triggering events, other coordination information.
NOTE: The policy can be used by a 3rd party application for coordination of the transmission of multiple UEs’ flows (e.g., haptic, audio and video) of a multi-modal communication session.
The KPI requirements for the multi-modal communication service are defined in TS 22.261 [14]:
- clause 7.11 KPIs for tactile and multi-modal communication service.
Requirements for 3GPP system to support the XR communication are defined in TS 22.156 [28]:
[R-5.2.2-001] The 5G system shall support 5G CN to provide real-time feedback in support of conversational XR communication among multiple users simultaneously.
NOTE 1: The feedback can include information such as network condition and achieved QoS. Such information can be used by the IMS, for example, to trigger the codec negotiation.
Requirements for 3GPP system to support multi-modal data transcoding are defined in TS 22.156 [28]. However, the current scope only includes transcoding between multi-modal data that does not require AI:
[R-5.2.2-003] Subject to operator policy and user consent, the 5G system (including IMS) shall support change of media types between video and avatar media for parties of a multimedia conversational communication.
[R-5.2.2-004] Subject to operator policy, the 5G system (including IMS) shall support transcoding between media such as text, video and avatar media in multimedia conversational communications.
NOTE 4: Text, video or other media could allow a party to control the appearance of its avatar, e.g., to express behaviour, movement, affect, emotions, etc.
NOTE 5: The transcoding of media enables avatar communication, e.g., in scenarios in which UE participating in an IMS call or other service does not support e.g., FACS, encoding avatar media, generating avatar media, etc.
6.42.6 Potential New Requirements needed to support the use case
[PR 6.42.6-1] Subject to operator policy and user’s consent, the 6G system (including IMS) shall support media transformations in multi-modal communication services, including image to video, 2D video to 3D video/avatar media, text to video and vice-versa.
NOTE 1: The media transformations could be provided via AI capabilities of 3rd party or 6G network (including IMS).
NOTE 2: For multi-modal communication service, refer to [14].
6.43 Use case on AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent for network performance assurance
6.43.1 Description
According to [266], an agent in AI refers to an artificial entity capable of perceiving its environment using sensors, making decisions, and then taking actions in response using actuators. The evolution of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents has undergone several stages, ranging from symbolic agents, reactive agents, reinforcement learning-based agents, transfer learning and meta learning based agents, to LLM based agents [[[SUGGESTION_START]]185[[SUGGESTION_END]]].
Due to the LLM's powerful capability in knowledge acquisition, multi-modal comprehension and generation, planning and reasoning, more and more research focuses on the usage of LLM to build AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents. Generally speaking, an LLM based AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent may be capable of performing certain tasks via perceiving environment, understanding intents, reasoning, planning, making decisions, and taking actions with tools. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent may decompose the complex task into smaller, manageable sub-tasks that can be executed either sequentially or in parallel by one or more AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents. In other word[[SUGGESTION_START]]s[[SUGGESTION_END]], a sub-task is a smaller, self-contained, and goal-oriented unit of work derived from a larger, complex task. By defining sub-tasks with clear objectives, multiple AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can collaborate to tackle more complex real-world problems. For example, each AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can be equipped with specialized skills and domain knowledge and engage in specific tasks. Through the collaboration of multiple AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents, the task efficiency and decision accuracy can be improved. It should be noted that plan reflection can be supported to ensure the robustness of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent empowered system. To be specific, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents leverage internal feedback mechanisms to enhance their strategies and planning approaches [[[SUGGESTION_START]]185[[SUGGESTION_END]]]. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can also interact with humans to rectify some misunderstandings and then make plans which align with human values and preferences. Furthermore, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can learn from past accomplished tasks and refine their future plans.
LLM based AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can be used in 6G network to solve complex tasks such as big event assurance. As we know, the big events, such as Olympic games, concerts, marathons, etc., usually pose stringent requirements for network performance for a given time and area. Large number of concurrent users, big volume of data transmission and frequent real -time operations are assumed. With Asian games in 2022 as an example, there are 48 matches and 1.5 million users over 16 days. Moreover, the big event such as technology expo, trade fair, or corporate gathering, may be held within an industrial park. In these cases, there are a lot of participants of the event, including exhibitors, visitors, and staff. The network performance requirement is higher than usual. The QoE of certain VIP users within the big events should be assured. In other word[[SUGGESTION_START]]s[[SUGGESTION_END]], full network coverage, good user experience, and zero complaints are expected for the big events.
In order to provide the desired network performance, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can be used to integrate the communication knowledge, structured data and network atomic capabilities to achieve the big event assurances. For example, the authorized third party may raise the network performance assurance task and requirements by intent, for example, the service types, bandwidth requirement, the number of users that needs to be supported for a given time and area, the service requirement of VIP users, etc. According to [147], intent specifies the expectations including requirements, goals and constraints for a specific service. The intent may provide information on particular objective and possibly some related details. Through the multi-modal human-computer interaction, user agent can understand the intent, decompose the intent into several sub-tasks and calling service agents. The service agent makes action plan for efficient network configuration and resource allocation, network coverage, personalized guarantee of user experience, communication service performance monitoring and quality assurance in runtime, risk prediction and avoidance, etc. The action plans made by service agent need to go through the plan reflection phase. After the thoughtful reflection, the decisions can be adjusted and the execution sequence can be optimized based on the feedback provide by the current environment. Finally, action agents may execute the specific action by using various tools, including built-in tools, API interfaces, models, etc., to complete each sub-tasks step by step. Here the user agent may be LLM based while the service agent and action agent may use light-weight AI models.
Based on the assistance of multi-agent collaboration in 6G network, it is expected that the network performance requirements for a big event can be fulfilled and the labor workload can be reduced.
6.43.2 Pre-conditions
National game will be held with millions of athletes and visitors for fifteen days. Operator A is responsible for the communication service provision with 6G network around the national game area. Operator A supports the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent for the efficient 6G network performance assurance. Various AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents are created when the 6G network is deployed.
6.43.3 Service Flows
Before the national game, the organizer of the national game provides the communication requirements to be supported for the national game by intent, such as the service types, bandwidth requirement, the number of users, the service requirement for VIP users, etc. for a given time and area.
Upon receiving such requirement, the intent for network performance assurance is analyzed by AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and divided into several sub-tasks. The sub-tasks may cover efficient network configuration, resource allocation, user experience guarantee, real time performance KPI monitoring, risk prediction and avoidance, etc.
The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents for sub-tasks will make detailed action plan for the sub-tasks. The action plan will be evaluated and tested until it works smoothly before the national game.
During the game, multiple AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents collaborate with each other to perform the tasks for network performance assurance in the national game. For the VIP users, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent may coordinate relevant network entities and allocate sufficient communication resources to ensure their service quality. Moreover, the monitoring dashboard is created so that the status of user experience and network performance can be continuously monitored.
If pre-warning is received during the game, the corresponding AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can make optional solution based on pre-set intents. If this solution is evaluated as feasible, the network re-configuration will be carried out by AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents automatically.
6.43.4 Post-conditions
With intelligent AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents collaborating together, the network performance can be assured and workload on complex routine network configuration and risk prediction/avoidance can be reduced.
6.43.5 Existing features partly or fully covering the use case functionality
Table 6.43.5-1: Gap Analysis for this use case
Specifications and clause
Existing Requirements
Gap Analysis
TS 22.261 [14], clause 6.51
External monitoring systems are often used by MNOs to track network activity for network surveillance and troubleshooting to perform diagnosis and fault analysis of their system. Such monitoring system is fully under the control of the MNOs, and the monitoring can be performed at signalling level. Due to the introduction of encryption of the signalling exchanged between network functions, there is no standardized, secure interface to share signalling traffic between the 5G network and the monitoring system.
A number of capabilities are required for the 5G network to continue supporting this feature, with regards to performance to minimise the impact on the real time traffic and to consider the security needed to protect the copies sent towards the external monitoring system.
External monitoring system is used by MNO to track network activity in 5G. For the 6G, the performance monitoring can be part of the task of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent, which may decide the performance KPI that need to be collected automatically. Moreover, the performance KPI collection is more frequent than 5G.
TS 28.312 [147], clause 5.1
The intent driven MnS producer for radio network shall have capability enabling MnS consumer to express intent containing an expectation on coverage performance to be assured for the specified area.
The intent driven MnS producer for radio network shall have capability enabling MnS consumer to obtain intent report information (including fulfilment information) for the intent containing an expectation on coverage performance to be assured.
The intent driven MnS producer for radio network shall have capabilities enabling the MnS consumer to express intent containing an expectation on radio network capacity performance to be assured for the specified area.
The intent driven MnS producer for radio network shall have capabilities enabling the MnS consumer to obtain intent report information (including fulfilment information and achieved value) for intent containing an expectation on radio network capacity performance to be assured.
The intent driven MnS producer for edge service shall have capability enabling authorized MnS consumer to express intent containing an expectation for delivering a service at the edge of the network.
The intent driven MnS producer for edge service shall have capability enabling authorized MnS consumer to obtain intent report information (including fulfilment information) for intent containing an expectation for delivering a service at the edge of the network.
In 5G network, intent-driven management can be supported, which translates the intent to identify which actions are needed, then executes the required actions to fulfil the intent. Rule-based mechanism and AI/ML based mechanism can be used to fulfil the intents.
However, current intent-driven management focus on “what” not “how”. It cannot support the plan reflection procedure in AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. Moreover, for AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent based network performance assurance, real time performance monitoring, dynamic planning and decision making should be enabled to provide on-demand network performance assurance automatically, which is more suitable for the burst traffic for a given time and area. Last but not least, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent based network performance assurance can take into account the service assurance of certain users within the big event.
6.43.6 Potential New Requirements needed to support the use case
NOTE: The mention of AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent doesn’t imply or preclude any architecture assumption or solutions.
[PR 6.43.6-1] Subject to operator’s policy, the 6G network shall be able to provide means for the authorized third party to request 3GPP service by intent.
[PR 6.43.6-2] Subject to operator’s policy, the 6G network shall be able to support mechanisms (e.g. AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) to provide the on-demand 3GPP service at a given time and location area based on the authorized third party’s request by intent.
6.44 Use case on customized service provisioning based on AI Agents
6.44.1 Description
As the telecommunications industry increasingly prioritizes personalized services delivery to improve user engagement and retention, the integration of AI technologies with communication networks has attracted growing attention. As an emerging AI technology, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents could autonomously perform tasks on behalf of users, systems, and/or applications, providing a promising way for improving operational efficiency, driving service innovation, and is expected to inject new impetus into MNO market growth.
In this use case, a user interacts with an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in the 6G network to request experience assurance for an upcoming railway journey. As depicted in Figure 6.44.1-1, upon clarifying the user request, the AI[[SUGGESTION_START]]A[[SUGGESTION_END]]gent leverages the network atomic capabilities (e.g. QoE prediction) to generate assurance policies for the user.
Figure 6.44.1-1: Customized service provisioning based on AI Agents
6.44.2 Pre-conditions
Operator A has deployed an AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in 6G network, which is able to provide intent-based network services for users.
Users can interact with the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to access customized network services.
The network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can interact with networks and access third-party resources via a mechanism such as tool invocations.
6.44.3 Service Flows
Bob plans to depart Beijing at approximately 9 am tomorrow for a business trip to Chengdu. During his train journey, he will need to attend an online meeting to share work updates at around 2 pm.
Bob hopes that the 6G network can ensure the network quality during his meeting on the train. Therefore, he informs the network that he will attend an online meeting on the Beijing-Chengdu train at approximately 2 pm tomorrow and needs high-quality network support during the meeting.
Bob’s intent is transferred to the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. Based on his intent and the network-internal information, the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent could evaluate the received intent is clear enough to give the service recommendation.
If not, the network Agent could collect additional necessary information (such as detailed train schedules information) from the trusted third-party data sources.
The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent first identifies multiple available train routes within the specified time and travel range, and then analyzes which routes Bob will possibly take and how long the meeting would last. Subsequently, it identifies the wireless base stations that will cover these possible train routes within the estimated time range, and predicts the QoE for Bob’s online meeting scheduled for tomorrow.
Finally, the agent generates several assurance policies along with their corresponding fees and pushes them back to Bob as recommended assurance packages including different train routes and different meeting durations.
Bob selects one of the recommended assurance packages as feedback to the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent.
The network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent pre-configures the network and will execute the corresponding assurance policies during Bob’s trip.
6.44.4 Post-conditions
During the train journey, Bob’s has a very good online meeting experience.
6.44.5 Existing features partly or fully covering the use case functionality
TS 23.288 [114] defines the procedures to support network data analytics, while NWDAF can provide partial support for QoE prediction and network status analysis.
TS 23.288 [114] defines the procedures for data collection from AF via NEF, while NWDAF can partially support external data acquisition.
6.44.6 Potential New Requirements needed to support the use case
NOTE: The mention of AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent doesn’t imply or preclude any architecture assumption or solutions.
[PR 6.44.6-1] The 6G network shall support mechanisms (e.g. A[[SUGGESTION_START]]I[[SUGGESTION_END]] capabilities such as A[[SUGGESTION_START]]I[[SUGGESTION_END]] [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) to authorize the received intent(s) from user.
[PR 6.44.6-2] Based on operator policy and user consent, the 6G network shall support mechanisms (e.g. AI capabilities such as A[[SUGGESTION_START]]I[[SUGGESTION_END]] [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) to provide 3GPP services on demand based on the received intent(s) from user by taking into account of network-related information and information from trusted third-party.
[PR 6.44.6-3] Based on operator policy and user consent, the 6G network shall support mechanism to collect charging information for customized service based on received intent(s) from user.
6.45 Use case on flexible UE-Network coordination through AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent(s)
6.45.1 Description
One of the goals of developing 6G system is to support an increasing number of diverse terminal devices leveraging not only connectivity but also advanced capabilities beyond basic communication (e.g. sensing, computing, and location). Examples include cars, UAVs/uncrewed aircraft, robots/Automated Guided Vehicles (AGVs), lightweight wearable devices, low-power IoT devices, etc. These devices have different capabilities and functionalities, and different expectations on network services, which may lead to a fragmented design on service interaction, e.g. specialized capability negotiation, dedicated service discovery or invocation operation (e.g. Nnef_UAVFlightAssistance operation in TS 23.256 [300] dedicated for UAV) and specific UE configuration.
Besides, the current service provision paradigm of network relies heavily on the consumer side (including applications and UE) to understand what services can be invoked in advance. In certain scenarios, spatial and temporal information must also be considered by the UE side, e.g. sensing-related services may be associated with specific areas, communication and power saving-related plan is associated with coverage condition. This all causes challenges for enabling flexible and user-friendly service provisioning and UE-network interaction.
A possible way to alleviate this issue is to enable more generic UE-network coordination with emerging agentic AI technologies, which are expected to power the network with greater autonomy in predicting, perception and understanding of UE side context. On the one hand, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can allow more flexible coordination and interaction (e.g. through human languages) and also support a universal mechanism for communication with diverse terminals at the service layer. On the other hand, the capability to memorize, reason and learn from additional knowledge could make AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents capable of actively identifying appropriate services, policies or configurations for the terminals in a certain location and time period and proactively pushing service recommendations to the UE side.
6.45.2 Pre-conditions
User A is traveling to city X for a vacation. He has subscribed to operator O’s services, and both his car and AR glasses are connected to operator O’s network.
Operator O has AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents running in its network to expose and provide services to terminals, including cars and AR glasses. And the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent knows what services are available in city X, and which services are accessible for specific types of terminals.
6.45.3 Service Flows
The service flow is shown in Figure 6.45.3-1 as follows:
a) A service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in network perceives the user A moving into city X by vehicle. City X has deployed sensing service for smart transportation.
a) A service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in network perceives the user A moving into city X by vehicle. City X has deployed sensing service for smart transportation.
b) As the car accesses the network and moves along the road, the service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in network perceives this and proactively pushes a sensing service recommendation to the car for sensing-assisted (automatic) driving.
b) As the car accesses the network and moves along the road, the service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in network perceives this and proactively pushes a sensing service recommendation to the car for sensing-assisted (automatic) driving.
c) User A accepts the sensing service to enable advanced autonomous driving, which can utilize sensing information beyond line of sight (e.g., sudden road congestion, traffic participants present in blind spot, etc.) from the network.
c) User A accepts the sensing service to enable advanced autonomous driving, which can utilize sensing information beyond line of sight (e.g., sudden road congestion, traffic participants present in blind spot, etc.) from the network.
d) After checking in at the hotel, User A decides to take a city walk and wears a pair of AR glasses to access a virtual tour guide service. Upon connecting to the network, the AR glasses may receive a notification from service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent about available computing services.
d) After checking in at the hotel, User A decides to take a city walk and wears a pair of AR glasses to access a virtual tour guide service. Upon connecting to the network, the AR glasses may receive a notification from service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent about available computing services.
e) The AR glasses negotiate with a computing service AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in the network to offload computing tasks (e.g., image rendering, object recognition and LLM-based Q&A) on demand. This offloading may dynamically adapt based on the device’s battery level, GPU/NPU performance, and communication quality.
e) The AR glasses negotiate with a computing service AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in the network to offload computing tasks (e.g., image rendering, object recognition and LLM-based Q&A) on demand. This offloading may dynamically adapt based on the device’s battery level, GPU/NPU performance, and communication quality.
f) As User A travels to crowded areas for sightseeing, a policy AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in the network may provide a communication quality map and path recommendations to ensure a sustained communication experience. Additionally, the AI Agent may offer a VIP service package recommendation to increase User A’s Quality of Service (QoS) priority during the trip.
f) As User A travels to crowded areas for sightseeing, a policy AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in the network may provide a communication quality map and path recommendations to ensure a sustained communication experience. Additionally, the AI Agent may offer a VIP service package recommendation to increase User A’s Quality of Service (QoS) priority during the trip.
Figure 6.45.3-1: UE-Network Coordination for Connectivity and Beyond Connectivity Services
User A drives his car into city X.
The service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in the network finds A’s car is in city X. Considering that A is a visitor and that city X is usually very crowded, and given that sensing service is available in the region, the service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent sends a notification to A’s car about the availability of this sensing service, and tells it could help travelers like A to drive the car more safely.
User A thinks it’s good to have additional safety assurance and accepts using the sensing service from operator O. User A may also enable autonomous driving mode of the car.
The service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent receives the confirmation and coordinates with the car about the details of the sensing service. For example, the car may report its current location and selected path to the sensing service AI Agent, and the sensing service AI Agent provide sensing information beyond line of sight (e.g. sudden road congestion, traffic participants present in blind spot near the car, etc.) to the car for better safety and driving experience.
User A drives safely to the hotel, he checks in and decides to have a city walk wearing his AR glasses.
After connecting to the network, the service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent finds A’s AR glasses’ performance could be enhanced by the computing service provided in city X’s region, so it sends a notification to A’s AR glasses about it.
With the user A’s permission, the AR glasses could then coordinate with the network about how to use the computing service.
For example, the AR glasses could provide a list of the applications that are potential to be accelerated/offloaded to the computing service AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. And the computing service AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent could respond with a filtered list to the AR glasses, as a recommended configuration for the AR glasses to judge whether to request computing services when needed. The computing service AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent could make the decision considering the location of A, capabilities of the AR glasses, network status, etc.
When the user A walks to an area where network status is not good, the computing service AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent may send a warning about potential service downgrade to the AR glasses and update the configuration of the recommended applications to be accelerate/offloaded. As a result, the AR glasses may decide to run more applications locally and reduce the request of computing services. Besides, when User A travels to crowded areas for sightseeing, the service access AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent may coordinate with a policy AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to provide a communication quality map and a path recommendation to user A, to help user A maintain a sustained good communication experience. Additionally, the policy AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent may offer a VIP service recommendation to guarantee User A’s QoS priority. If User A accepts the recommendation, the policy AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent will update QoS parameters to prioritize the connection of use A.
6.45.4 Post-conditions
Operator O could promote suitable services to various types of new devices, and the services operate efficiently. And user A gets a better experience through these services.
6.45.5 Existing features partly or fully covering the use case functionality
3GPP system supports mechanisms to update UE configuration by network, e.g. through OMA device management protocol, capability negotiations and service level (e.g. LCS) interaction between UE and network, as defined in TS 24.501 [268] and TS 24.368 [269]. However, it relies on proper implementation on UE side and is not flexible enough to enable potential new services.
6.45.6 Potential New Requirements needed to support the use case
[PR 6.45.6-1] Subject to operator policy and user consent, the 6G network shall be able to optimize the user experience by actively informing UE about availability of related network services based on information such as device type, device location, mobility behaviour, connection status, subscription information, etc.
[PR 6.45.6-2] Subject to operator policy and user consent, the 6G system shall support dynamic configuration of UEs and the associated 3GPP services (e.g. communication service, sensing service, computing service, AI service) to adapt to varying conditions (e.g. network conditions, application needs, resource availability) to provide optimal service operation.
NOTE: The optimal service operation aims to deliver network services efficiently and effectively, maximizing user satisfaction while minimizing resource utilization.
6.46 Use case on AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent management
6.46.1 Description
With the aging of the population, the care support for the elderly living alone has become a big social problem, which is not a regional problem but a global problem. In the article The AI is about to completely change how you use computers [270] published by Bill Gates and the Agent Network Presentation published in W3C [271], it is indicated that the agents will replace the existing software services. It would be expected that there will be several terminals with AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents to support corresponding services solving the care support problem of the elderly living. For example, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in smart camera could support monitoring for 24 hours of the elderly, and it can give an alarm when accident appears, such as fall detection; the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent of smart wearable bracelet could provide medical and health functions for the elderly, such as heart rate detection. However, as Figure 6.46.1-1 shown, most frameworks only consider the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents communication within their ecosystem, potentially blocking the communication and coordination among different ecosystems, even these AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents serve one user. To support the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent communication in different ecosystems, several AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent protocols have been published for the agent – agent communication intra and inter the network and also for the agents to invoke the tools.
Figure 6.46.1-1: AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent communication in different ecosystems
Usually, when there is an emergency situation, the elderly or the community workers will call the emergency service centre, for example, Emergency Medical Service in the US and National Health Service Emergency Care in the UK, which is a 3rd party service provider in the 3GPP network. In some emergency centres, there are some professional doctors or medical personnel that could give guidance to the caller. While, without the detailed information, they could not give accurate and correct guidance to the caller in the telephone.
Due to the reasons above, a systematic solution to connect the 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents necessarily needed and it could be a lucrative opportunity for the operators. The operators will provide the functionality of 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent management (e.g. agent registration, agent discovery) to maintain an accurate profile for the user using devices with 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents and provide precise services to them on demand.
It might be the enhanced IMS network which provides the 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent management functionality, but 6G network is used in this use case. Similar with the legacy IMS network, the agent-agent communication could be enhanced based on the operator's MSISDN system. With the MSISDN system, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents could be globally reachable even in roaming scenario and the scenarios which need interworking with other operators.
The interaction of the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on the devices and 3GPP network could be classified into two categories.
Category 1: AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent registration
3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent registration is initiated by the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents on devices. It is mainly used to share the supported capability of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents to the 6G network, so that 6G network could discover the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents to provide the systematic AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent services to the users.
For example, the smart camera indicates its video recording capability, fall detection capability when it registers withthe 6G network, the smart bracelet indicates its heartbeat monitoring service when registering into the 6G network.
Category 2: AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent invoking
The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent invoking is initiated by the 3GPP network proactively. With the MSISDN system of the operator and globally network interworking, the devices’ AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents are reachable 24-hours a day. For example, a 6G network (different from the 6G network that AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent subscribes to) could ask the 3rd AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on the bracelet to monitor the heartbeat of the user based on the user consent at any time.
With the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent registration mechanism, when providing services to the users, the emergency service/Healthcare service could provide some requirements to the 6G network, the 6G network should understand the service requirements and require appropriate 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents on devices to execute some tasks on demand, implementing cross-ecosystem requirement matching.
For example, when the emergency service centre is serving an elderly person, the 6G network receives the requirement from the emergency service centre to capture the live video of the elderly person. The 6G network will invoke the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent of the smart camera to execute tasks, i.e. get the video stream and transfer the received video stream to the emergency service centre.
6.46.2 Pre-conditions
Mary is a 70 years old woman who is living alone, and she subscribes to Operator A’s AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent management. Her smart camera, smart bracelet, and smart television registers to the 6G network.
1. The 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in smart camera indicates its video recording capability, fall detection capability when registering to the 3GPP network.
2. The 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in smart bracelet indicates its heartbeat monitoring capability when registering to the 6G network.
3. The 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in smart television indicates its remote video playing capability and video call capability when registering into the 3GPP network.
6.46.3 Service Flows
1a. The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in smart camera of Mary detects Mary falling at home and calls the emergency service centre automatically. Or
1b. The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent in smart camera of Mary detects Mary falling at home and sends a service request to the network for remote emergency guidance service. Then the 3GPP network invokes the remote guidance service of the emergency service centre.
2. The emergency service centre shares its requirement of monitoring the heartbeat of Mary and watch the live video of Mary to the 3GPP network to get the detailed situation of Mary.
3. The 3GPP network invokes the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent of Mary’s smart bracelet to execute one task, i.e. get the real time heartbeat of Mary, and sends the captured data which might be together with the preliminary analysis to the emergency service centre based on Mary’s consent.
4. The 3GPP network invokes the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent of Mary’s smart camera to execute one task, i.e. get the live video of Mary which especially focus on specific area based on the previous analysis result, and sends the video to the emergency service centre based on Mary’s consent.
5. The emergency service centre analyses Mary’s situation and share its requirement to the 3GPP network to make a video call to Mary to teach her how to handle her injuries.
6. The 3GPP network invokes the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent of Mary’s smart television to make a fully autonomous video call between Mary and emergency service centre based on Mary’s consent. The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on the smart television can intelligently volume up, translate the language to the dialect that Mary can easily understand, and show assistant video to help Mary understand the instructions from emergency service centre.
6.46.4 Post-conditions
Mary handled her injury correctly and waited for the paramedics to arrive.
6.46.5 Existing features partly or fully covering the use case functionality
TS 22.156 [28] defines the service requirement for the authentication of the digital assets. The content from the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents on AI devices could be considered as user’s digital assets.
TS 22.156 [28] defines the service requirements of the multimedia conversational communication, the communication between AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents can be considered as multimedia conversational communication.
In TS 22.261[14] clause 6.38:
The 5G system shall support mechanisms to identify a PIN, a PIN Element, an Evolved Residential Gateway (eRG) and a Premises Radio Access Station (PRAS).
The 5G system shall be able to support PINs with PIN Elements subscribed to more than one network operator (e.g. a PIN Element that is a MUSIM UE and subscribes to different operators respectively, one PIN Element subscribed to network operator A and another PIN Element subscribed to network operator B).
PIN’s mechanisms might be reused to fulfil part of functionalities.
6.46.6 Potential New Requirements needed to support the use case
[PR 6.46.6-1] Based on the user consent and operator’s policy, the 6G network shall be able to support the discovery of 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent (application) on the UE.
[PR 6.46.6-2] Based on the user consent and operator’s policy, the 6G system shall be able to support means for the network to invoke the 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent (application) on the UE.
6.47 Use case on proactive AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent for personal safety
6.47.1 Description
This use case describes a network-hosted personal AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent dedicated to proactively ensuring a user's physical safety. The agent integrates multiple data streams, e.g. the user's real time location, biometric data from a wearable device (heart rate, accelerometer), calendar information, and environmental data (e.g. crime statistics for an area), to build a risk profile for the user. Based on this profile and user-defined policies, the agent can take autonomous actions to mitigate potential threats, such as establishing communication with emergency services.
6.47.2 Pre-conditions
- The user subscribes to a "Personal Safety AI Agent" service from the MNO or an authorized third party.
- The user has provided explicit consent for the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to access and process personal data, including location, biometric data from a wearable device, and calendar information.
- The user has configured safety policies, defining emergency contacts, distress triggers (e.g. safe words, specific biometric patterns), and the scope of autonomous actions the agent is permitted to take.
- The 6G network provides secure and reliable hosting for the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and guarantees low-latency data paths from the user's devices (smartphone, wearable) to the agent.
6.47.3 Service Flows
1) Alex is walking through an unfamiliar part of the city after dark. His personal safety AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent is active in the network.
2) The agent continuously consumes data: Alex's location, his heart rate from his smartwatch (which is slightly increased), and the fact that his calendar is clear for the rest of the evening.
3) The agent cross-references this data with external sources, noting that the area has a high crime rate at this time.
4) Suddenly, Alex's smartwatch detects a rapid increase in heart rate and the accelerometer reports a sudden sprint.
5) The agent immediately activates a "high-alert" state. It sends an alert to Alex's phone: "Distress detected. Tap to confirm you are safe. Emergency services will be contacted in 30 seconds."
6) Simultaneously, the agent sends an urgent alert to his emergency family member, Chloe: "Distress signal detected from Alex. He is running. Awaiting his confirmation of safety."
7) Alex does not respond within 30 seconds. The agent automatically contacts the national emergency number, providing the dispatcher with Alex's real time location, his identity, and a brief summary, e.g. "Automated distress call from personal safety agent. User is running, high heart rate detected, unresponsive."
8) Emergency services are dispatched to Alex's location.
6.47.4 Post-conditions
- Emergency services are dispatched to the user's precise location with valuable context, potentially preventing harm.
- Emergency contacts are kept informed in real time according to the user's preferences.
- A log of the event, including all data and actions taken by the agent, is generated for review.
6.47.5 Existing features partly or fully covering the use cases functionality
This use case is different from other AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent use cases in the present document, such as "Personalized AI for health monitoring" (clause 6.5) or "Child health management assistant" (clause 6.23), which are primarily reactive and focused on health metrics. The difference here is the proactive and autonomous safety intervention. The agent synthesizes multi-domain data (location, biometrics, environment) to make predictive risk assessments and consumes network capabilities (emergency services) to take actions before a critical event occurs. The capability to autonomously contact emergency services is not covered in other use cases.
6.47.6 Potential New Requirements needed to support the use case
[PR 6.47.6-1] Subject to operator’s policy, regulatory requirements and user consent, the 6G system shall support a mechanism for a user-authorized AI application (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) in the Service Hosting Environment to autonomously initiate communication with emergency services on behalf of the user, i.e. the communication session is associated with the user's identity and location.
[PR 6.47.6-2] Subject to operator’s policy, regulatory requirements and user consent, the 6G network shall support a mechanism to log the data (e.g. sensor data, context information) used by an AI application (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) in the Service Hosting Environment, which autonomously decided to initiate communication with emergency services on behalf of the user.
[PR 6.47.6-3] Subject to operator’s policy, regulatory requirements and user consent, the 6G system shall support a mechanism for a user to provide policies which define the autonomous actions that can be taken by their authorized AI applications (e.g. AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents) in the Service Hosting Environment.
NOTE: For example, a user could provide a policy authorizing their AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to share location data with a pre-defined family member. A separate policy could authorize their AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent to initiate communication with emergency services on the user's behalf when the agent identifies a critical safety event.
6.48 Use case on service robot for power grid
6.48.1 Description
Service robot offers greater versatility by performing complex tasks that require dexterity, adaptability and interaction with human-centric environment. Thanks to major breakthroughs in AI, machine learning and advanced materials, service robots (e.g. humanoid robots) are poised for considerable growth over the next decade and present a massive investment opportunity.
According to a report released by Markets and Markets in January 2025, the global humanoid robot market is expected to grow from USD 2.03 billion in 2024 to USD 13.25 billion in 2029. Meanwhile, Goldman Sachs predicted in February 2024 that the total addressable market for humanoid robots could reach USD 38 billion by 2035. Since service robots are more flexible and capable of adapting to complex terrain, they are particularly appealing for tasks that are “dangerous, dirty, and dull”, for example, mining, disaster rescue, grid inspection, nuclear reactor maintenance, and chemicals manufacturing, etc. Customers are willing to pay a higher price for robots that can do dangerous jobs that people are reluctant to do.
For example, the service robots can be deployed in power grids, enabling inspection, maintenance and emergency response operations in extreme environments. As shown in Figure 6.48.1-1, it is the high-altitude ultra-high voltage AC project in southwest China, which covers 1316 kilometers of 1000 kV lines and four 1000 kV substations. With the high altitude (e.g. 3450 m) and mountainous terrain, it is hard for human technicians to go to the site. The service robots (e.g. humanoid robot) may mimic human movements and navigate the existing power grid infrastructures (e.g. narrow stairs, climb ladders), which are expected to play an important role in power grid inspection. The service robots can tolerate the hazards and continue working with the hand-operated tools, which enhances workers’ safety while ensuring service continuity.
Figure 6.48.1-1 The Ultra-high voltage line and 1000kV substation in southwest China
On the other hand, different levels of robot autonomy and intelligence [272] have been developed. For the robots with higher level of autonomy, they are growing fast with the utilization of large AI models (e.g. LLM, Vision-Language Model (VLM), Simultaneous Localization and Mapping (SLAM). With service robot as an example, the motion control, such as walking, grasping, turning, balance, posture control, reflexes and corrective actions, can be supported via embedded controller and chips for fast response. However, the large AI models for advanced autonomy are too heavy to run efficiently on embedded hardware of service robot. Offloading the computing-intensive “brain” of service robot to nearby 6G network (e.g. service hosting environment) can enable lightweight service robots with limited onboard power. To support this, the service robot needs to send multi-modal data (e.g. sensor data, LiDAR, video, voice, etc.) to the 6G network. These multi-modal data can then be processed via the large AI model to generate robot control commands and natural language responses appropriately. With the support of pervasive computing capability in 6G network, the service robot for power grid inspection can respond instantly to complex situations with real time perception-action loops as shown in Figure 6.48.1-2.
Figure 6.48.1-2 Illustration of service robot for power grid
6.48.2 Pre-conditions
PowerGrid Corp, a leading energy provider, manages over 300,000 km of transmission lines, 600 substations, and remote renewable energy plants across mountains, forests, and coastal zones. Due to climate change, PowerGrid Corp suffers from rising maintenance costs and dangerous inspection conditions. In order to improve operation efficiency, they purchase 500 service robots for grid inspection, maintenance and emergency response.
The 6G network is operated by Operator A. Operator A is responsible for the communication service provision around the power grid area. Moreover, Operator A owns sufficient computing power and supports edge intelligence. The PowerGrid Corp subscribes to the communication service and AI service from Operator A which provide low latency communication service as well as the large AI model inference service via service hosting environment for service robots in the field.
One day, a massive high-voltage power grid runs across snowy peaks and rugged valleys. Ice storms, falling rocks, and wildlife make this terrain not only remote but dangerous for human technicians. To ensure uninterrupted power delivery to millions of people, an service robot named Voltus is deployed to inspect, maintain, and report anomalies in the grid infrastructure.
6.48.3 Service Flows
Voltus is airlifted by drone to a remote grid substation. Upon landing, it connects to the 6G terrestrial network for communication. Meanwhile, it activates its simultaneous localization and mapping system to map the area using LiDAR and vision.
Voltus walks along the grid lines. A human technician in intelligent control centr[[SUGGESTION_START]]e[[SUGGESTION_END]] connects remotely and speaks to the service robot: “Voltus, inspect the substation.”
Voltus uses thermal imaging to detect overheating in transformers. Meanwhile, it takes video of the substation. The multi-modal data (e.g. the thermal image, video stream, sensor data and LiDAR data) are sent to the service hosting environment within 6G network.
Upon receiving the multi-modal data, the video recognition model in service hosting environment analyzes the input data and detects objects based on the visual and thermal patterns match.
On top of that, the large AI models (e.g. LLM, VLM, SLAM) in the service hosting environment perform reasoning and planning, and generates the robot control commands and natural language responses. For example, the LLM generate the analysis report: two insulators show thermal anomalies and one grounding wire is partially detached. The LLM in service hosting environment generates robot control commands: 1) tighten the loosened bolt using a multi-tool; 2) replace the two insulators.
The analysis report and robot control command are sent to Voltus. Based on the command, Voltus’s embedded system performs motion control, which executes walking, balancing and climbing a small pylon. Then Voltus replaces the cracked ceramic insulators from its utility belt.
When tightening the bolt, Voltus further queries the large AI model in the service hosting environment:“What torque should be applied to a 500kV line bolt with rust mitigation coating?” The large AI model in service hosting environment replies:“Apply 45 Nm torque using non-conductive wrench. Confirm line de-energized.” Upon receiving the reply from large AI model in service hosting environment, Voltus tightens the loosened bolt using a hand-operated tool accordingly.
After a while, a human technician in intelligent control centr[[SUGGESTION_START]]e[[SUGGESTION_END]] speaks to Voltus: “Voltus, confirm insulator replacement, send close-up.”
Voltus uses speech recognition and sends the annotated images to the human technician. Moreover, Voltus responses to the human technician using speech: “Insulator integrity verified.”
Upon receiving the responses from Voltus, the human technician checks the images and speaks to the Voltus robot: “Repair successful. Recommend re-inspection in 2 weeks.”
Upon completion of the substation inspection, Voltus can be airlifted by drone to another grid substation for inspection.
6.48.4 Post-conditions
Due to the service robots based grid inspection and maintenance, long term cost saving and higher operational efficiency can be achieved for the power grid.
Existing features partly or fully covering the use case functionality
Table 6.48.5-1: Gap AnalysisSpecifications and clause
Existing Requirements
Gap Analysis
TS 22.104 [64] clause A.2.2.3
A mobile robot essentially is a programmable machine able to execute multiple operations, following programmed paths to fulfil a large variety of tasks. This means, a mobile robot can perform activities like assistance in work steps, collaboration with other robots, e.g. for car assembly, and transport of goods, materials and other objects. Mobile robot systems are characterised by a maximum flexibility in mobility relative to the environment, with a certain level of autonomy and perception ability, i.e., they can sense and react with their environment.
Table A.2.2.3-1: Service performance requirements for mobile robots
Compared with the mobile robot in 5G system, the service robot in 6G system is able to perform complex tasks with the LLM assistance in service hosting environment.
6.48.6 Potential New Requirements needed to support the use case
[PR 6.48.6-1] The 6G system shall be able to provide the communication and AI service for users with the KPI requirements summarized below:
Table 6.48.6-1: Proposed KPIs for use case on service robot
Use case
Traffic type
Message/Frame size(Byte)
Transfer interval(ms)
Data rate(Mbps)
E2E latency(ms)
Reliability
Service robot
UL sensor data (NOTE1)
1250-12500
10
1-10
100-150ms (NOTE4)
99.99%
UL LiDAR
28800
100
27.6 (NOTE3)
DL Control command (NOTE2)
625-12500
50
0.1-2
NOTE 1: refers to the kinematic state, environment perception, manipulation status info except LiDAR to be sent from the service robot to the network to enable effective motion planning, object interaction and navigation.
NOTE 2: refers to the control command towards service robot, e.g. high-level task, action plans, motion strategy, gripper command, etc.
NOTE 3: the data rate of LiDAR is based on frame rate 10Hz, 28800 points/frame, 12byte for one point cloud, i.e 28800*16*8*10=27.6Mbps.
NOTE 4: E2E latency includes two parts: the round-trip latency for communication service and the latency for AI inference within the service hosting environment. Typical robot control loops require 100-150ms latency [273] for AI inference, communication and control. For example, the communication may take about 40ms while the AI inference may take about 100ms [274] for the service robot.
6.49 Use case on 6GS providing low-latency AI inference service
6.49.1 Description
Robots are expected to be widely used in future daily life, such as in casual interactive sports, education, family life (including household chores). A mobile AI-embodied robot can be an example to describe how the device can execute the specific task. The robot can be equipped with a number of sensors including cameras to collect the environment information around the robot, e.g. images taken at a certain frequency. To represent an advanced approach to replicating human-like intelligence and motor control, mobile AI-embodied robot design usually mimics both the human brain aiming at broad generalization and situational awareness similar to human cognitive functions (“Brain” illustrated in Figure 6.49.1-1) and cerebellum focusing on motor control, precision, coordination, and timing of movements (“Cerebellum” illustrated in Figure 6.49.1-1). Depending on the applications, the visual data. incl. images captured by robot sensors are fed into Brain, where a large VLM can be used. The Brain is expected to understand the input information (e.g. images, any additional multi-model information) and conduct corresponding analysis for task orchestration, generate instructions, action planning and afterwards sending the actions/instructions to a robot cerebellum, e.g. implemented with a vision-action model. The Cerebellum further takes the input from Brain, and further determines the discrete actions to, e.g. pick up the badminton in the court, open the refrigerator.
Figure 6.49.1-1: Functional units used for home robot
For mobile AI-embodied robots, usually a specific/complex task requires a large amount of AI reasoning. The traditional equipment with limited capabilities (such as limited size of LLM), computing resource and battery power may not be able to run these complex tasks locally for an ideal experience. Power consumption, heat dissipation, memory size and computing capability are the four main physical constraints to limit the LLM size supported by a terminal (e.g. phone, robots, AR/VR etc.). Power consumption, for example, limits the application of AI inference in mobile devices. Assume we have a chip which supports 1~5Hz multimodal inference for one 7B LLM [275], and the battery capacity is around 1 kWh, the robot can only operate for 2~4 hours under full load. The above issues can be well-resolved by offloading complex computational tasks (AI inference) from a device with limited capabilities and resources to a more capable remote processing node, which is also a developing trend in the market and can improve the battery charging cycle.
The Brain and Cerebellum can be deployed together (e.g. both in SHE) or separately (e.g. Cerebellum locally on robot, Brain in SHE as it is designed for action planning which requires a large amount of sophisticated AI reasoning/inference with VLM). The Cerebellum controlling action execution may involve invoking the software development kit in the robot. Considering the UE limited capabilities mentioned above, it is suggested not to deploy the Brain and Cerebellum together in the robot. The complex computing task can be offloaded from the robot to the processing node.
The robot can perform different tasks based on the given specific scenarios. For some interaction tasks, the robot receives the user prompts and sends the corresponding response. The service flow is described in clause 6.49.3. Corresponding to human reaction time, the required response time is 200ms~300ms for better user experience [276].
Many research articles show that the average human reaction time is 200 ms~300 ms. The article [276] from the International Computer Science Institute reveals that the differences across the languages within a range of 250 ms ~300 ms for response in conversation. The response time plays a critical factor to make conversations/interaction with a human being natural. The fastest a human can react to some stimulus with a vocal response under maximally favourable conditions is just below 200ms from [277]. One source document proposes an average reaction time of 228 ms for auditory stimuli and somewhat large 247ms for visual stimuli [278]. Based on the mentioned values, it is suggested that the response time (from the moment the robot is aware of certain changes: stimuli detected by sensory receptors and transmitted to the brain, through central processing via brain interpreting and deciding on a response, to motor execution time coordinated by cerebellum) is below 200 ms for better user experience.
The response time mentioned above can refer to the E2E latency in the context of this use case. Considering the Cerebellum is deployed on robots and the Brain on the network side (e.g. 6G CN, SHE), the E2E latency also includes the round-trip transmission latency between the robot and the Brain, the computing time in the Brain and the Cerebellum. When the Cerebellum and Brain are deployed together on the network side (e.g. 6G CN, SHE), the E2E latency calculation is similar.
The existing mechanism defined in 3GPP is the 6G network only acts as a pipe responsible for traffic transmission and the AS performs data processing. Considering the low latency requirement for the computing service, it is difficult to guarantee the E2E latency performance requirements, e.g. the interaction task mentioned above which is about conversation responses to the users, which is normally around 200 ms [276]. The communication resource and computing resource (i.e. 6G CN, or SHE) should be jointly managed and coordinated by 6G network, when providing computing service in order to support the E2E service performance (e.g. latency). 6G network is expected to control computing and communication resource[[SUGGESTION_START]]s[[SUGGESTION_END]] to fulfil the E2E latency required by the computing task needed by the low-latency AI inference service.
6.49.2 Pre-conditions
Operator OZ has deployed in 6G network 6G/3GPP services for intelligent interactive robots focusing on supporting low E2E latency which partially includes the AI inference time of the 3rd party-provided VLM/LLMs (e.g. “Brain”) on the network side for the robot to handle complex tasks (including sophisticated reasoning, decision-making, activity-planning). The Cerebellum is deployed in the robot locally for local intelligence/quick reaction. By design, the Cerebellum in the robot can collaborate with the Brain in the network.
Bob purchased a robot designed for interactive casual sports. Bob also has a subscription to Operator OZ for comprehensive 6G/3GPP services for the robot. The robot is able to access 6G service.
The Robot, as the user Bob’s playmate, can join in outdoor activities together, as shown in Figure 6.49.3-1.
6.49.3 Service Flows
Figure 6.49.3-1: Robot performing interactive outdoor activities, to show the E2E latency 6G system is expected to provide consists of the round-trip communication latency between Robot and the “Brain” (which can be a VLM hosted on the network side, e.g. 6G CN/SHE), as well as the latency of computing service.
NOTE: The interaction between user Bob and Robot is out of scope, what is worth noting is Cerebellum may also further process information (e.g. instructions, actions) received from Brain, before performing e.g. precise motor actions.
The robot activates its sensors (such as mic, camera) and is on standby for wake word. Bob speaks ‘Hey, let’s exercise!’ and the robot receives the voice.
The robot sends the audio and the local environment information (e.g. playground) to the in-network Brain by the 3GPP network.
The in-network Brain receives user prompts and the environment information, conducts the environment analysis based on the AI inference model and computing resources and obtain the response to Bob.
The robot receives the response from the Brain and replies to Bob ‘Hello Bob! What activity shall we do today? Running, cycling, or stretching?’ The E2E latency in the service flow contains the steps from step 2 to step 4 which describes how the robot will respond to the user prompt.
Bob selects one activity and speaks ‘Let’s go running’.
The robot sends the user instruction and local environment information to the in-network Brain.
The in-network Brain generates the initial task planning and it may contain the instruction: Guide the user to warm up for 5 minutes and the according the actions.
The Cerebellum in the robot receives the initial task planning information and determines the discrete actions for the execution. The robot performs dynamic stretch and Bob follows the robot to imitate warm-up motions.
The robot takes images in order to tracks Bob’s posture.
The robot sends the images to the Brain to validate the posture. The Brain checks whether the motion is correct. If the motion is correct, the Brain may respond the audio ‘Perfect! Keep going.’ If the motion is not correct, the Brain may respond the audio ‘Adjust your arms higher, Bob’ and provide the correct actions feedback.
The Cerebellum determines the correction for the next execution based on the updated task planning information. The E2E latency in the service flow contains the steps from step 10 to step 11 which describes how the robot will react to the change in the step 10 based on interaction between Cerebellum and Brain. The interaction between the Cerebellum and Brain is to get the changed environment information so that the Brain can update the planning action and send to the Cerebellum for further discrete actions. Considering that the E2E latency refers to the human reaction time, step 10 describes the change of the environment and step 11 describes how the robot will react to the change. The duration between the steps can be the reaction time and it is included in the E2E latency. In order to fulfil the requirements of E2E latency, the network provides the communication resource and computing resource in order to guarantee computing service performance.
The robot performs the demo correction to adjust Bob motion.
The warm-up is completed. The robot and Bob start running.
6.49.4 Post-conditions
The robot activates mobility to match Bob’s speed and complete the running task.
6.49.5 Existing features partly or fully covering the use case functionality
5G system enhancements for edge computing, as defined in TS 23.548 [137].
5GC supports the following connectivity models to enable Edge Computing:
- Distributed Anchor Point: For a PDU Session, the PSA UPF is in a local site, i.e. close to the UE location. The PSA UPF may be changed e.g. due to UE mobility and using SSC mode 2 or 3.
- Session Breakout: A PDU Session has a PSA UPF in a central site (C-PSA UPF) and one or more PSA UPF in the local site (L-PSA UPF). The C-PSA UPF provides the IP Anchor Point when UL Classifier is used. The Edge Computing application traffic is selectively diverted to the L-PSA UPF using UL Classifier or multi-homing Branching Point mechanisms. The L-PSA UPF may be changed due to e.g. UE mobility.
- Multiple PDU Sessions: Edge Computing applications use PDU Session(s) with a PSA UPF(s) in local site(s). The rest of applications use PDU Session(s) with PSA UPF(s) in the central site(s). Any PSA UPF may be changed due to e.g. UE mobility and using SSC mode 3 with multiple PDU Sessions.
5GC supports the Edge Application Server Discovery Function (EASDF), including the EASDF discovery and selection.
5GC supports Edge Computing in 5G System considering different connectivity models, including:
- EAS discovery and re-discovery.
- Edge relocation.
- Network exposure to Edge Application Server.
- Support of 3GPP application layer architecture defined in TS 23.558 [5].
- Support of AF guidance to PCF determination of proper URSP rules.
The existing features defined in 3GPP are about how to guarantee the communication service including the traffic routing path optimization. The computing service provided by the 6G network is not covered.
6.49.6 Potential New Requirements needed to support the use case
[PR 6.49.6-1] The 6G system shall be able to provide the communication and AI service (assuming AI inferencing in Service Hosting Environment) for users with the KPI requirements in Table 6.49.6-1 below.
Table 6.49.6-1: KPIs for physical AI robot to perform cognitive functions to interact with a human user via 6G network
Use case
Traffic type
Average packet size (Byte)
Transfer interval (ms)
Data Rate (Mbps)
Joint E2E latency (ms)
(NOTE 4)
Reliability
Support robot conscious awareness for interacting with human user
UL camera data
(NOTE 1)
[<1000]
[10]
[20-60]
(NOTE 2)
[<200]
(NOTE 3)
(NOTE 5)
[99.9 %]
NOTE 1: 6 RGB cameras are equipped for robot “Figure 02” [180].
NOTE 2: 20 Mbps is for six 1080p cameras, and 60 Mbps is for four 1080p cameras plus two 4K cameras. Data Rate is calculated from video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, and around 24Mbps for 4K 30Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real time video uploading of a vehicle as per YD/T 4778-2024 [182].
NOTE 3: For physical AI robot interacting with a human user, the robot is expected to mimic the similar basic human brain reaction time including conscious awareness/recognition and decision-making based on various stimuli. Such human brain reaction time ranges from mean auditory reaction time 140-160ms, touch 155ms, to visual reaction time 180-200ms [279].
NOTE 4: Joint E2E latency (i.e. round-trip communication latency, and AI inference latency in Service Hosting Environment). and UE is only considered to contribute to the communication service latency.
NOTE 5: The human to robot latency (vice versa) is not included.
6.50 Use case on real time video super-resolution service
6.50.1 Description
With the growing demand for immersive and high-quality video services (e.g. short videos, live streaming, XR content), end-users increasingly expect a consistently high-resolution visual experience, irrespective of device capabilities or network conditions. Video resolution has advanced from the Standard Definition (such as 480p) to HD (such as 720p), progressing to Full HD (such as 1080p), Ultra HD (such as 4k), and ultimately Full Ultra HD (such as 8k). Better video resolution is synonymous with an enhanced user experience. However, limited uplink bandwidth, mobile device processing constraints, and battery limitations often result in video content (e.g. from low-end mobile devices or in remote scenarios) being transmitted at suboptimal resolutions (e.g. 480p or 720p). As a result, viewers are unable to enjoy high-quality live broadcasts and cannot clearly see video details, leading to a lack of engaging interactions and significantly diminishing the user experience.
Video Super-Resolution is an effective technology for enhancing video resolution. It processes video to convert low-resolution streams into high-resolution output while maintaining low latency and high frame rates [285]. This technology also aims to enhance details while minimizing blurring and artifacts. Figure 6.50.1-1 shows the comparison of super-resolution on an old video. The left side displays the frame before applying super-resolution, whereas the right side presents the frame afterward. The post-super-resolution frame reveals significantly clearer details and a more diverse content display.
Figure 6.50.1-1: Comparison of video super-resolution on old video
Both traditional algorithms and AI models can help to achieve video super-resolution. Neural network-based video super-resolution models (e.g. BasicVSR [286]) have achieved significant breakthroughs and have become mainstream. The effectiveness of video super-resolution can vary depending on the specific model used or the type of video content. For instance, an AI model optimized for sports-related live streaming might perform poorly when applied to virtual character live streaming. To ensure high-quality video for users, it is important to select the appropriate AI model/ algorithm by analyzing video content, possibly through AI-driven content detection.
High-quality videos (e.g. high resolution) are becoming increasingly popular. The user can select different clarity on their applications via the User Interface to satisfy user experiences as shown in figure 6.50.1-2.
Figure 6.50.1-2: Resolution selection on APP via user interface
Video super-resolution technology can be carried out in the form of network-based video super-resolution or mobile-based video super-resolution. In practice, the computing resources deployed on the network side primarily play a proactive role by employing video super-resolution techniques to improve the quality of received video, i.e. network-based video super-resolution. In recent years, with advancements in user terminal computational power, especially in high-performance mobile phones, the consumer-level UE can also undertake video super-resolution with small-sized AI models, i.e. mobile-based video super-resolution.
Video super-resolution technology applies to a variety of practical use cases, such as local city live video streaming, video conferencing, real time monitoring of high-definition video, real time processing of drone aerial footage, cloud gaming, and video calls.
This use case specifically addresses scenarios where the 6G network offers real time video super-resolution services for live streams to UEs. Here 6G network performs network-based video super-resolution by utilizing computing resources deployed in the 6G network. It offers several benefits to video service providers, UEs, and 6G operators:
1. Lower Costs for Video Service Providers. A 6G network can expose computing resources deployed in the 6G network to video providers to help perform video super-resolution. When applying network-based video super-resolution, especially to some small and medium-sized developers/video service providers, they may lack the capacity to deploy a large number of servers with GPUs/NPUs, as this would entail significant deployment costs and maintenance expenses. Using network computing resources can help these providers with video super-resolution at lower expenses.
2. Reduced UE power consumption. It enables video service providers to rely more on network-based video super-resolution, rather than mobile-based video super-resolution, when enhancing video resolution. This approach helps lower power consumption on terminal devices, reduces heat generation during video viewing, and imposes minimal requirements on terminal devices, such as computing power.
3. Reduced Application Development Workload. User terminals differ widely in form and operating system (e.g. Android, iOS, Linux), so video super-resolution on UEs would require adapting and developing multiple applications for invoking different super-resolution models. Consequently, employing unified network-side video super-resolution can reduce the workload involved in UE application development.
4. Minimized End-to-End latency. Traditionally, video super-resolution for live streams involves first transmitting the video data to the video service provider's servers, performing video super-resolution there, and then sending the high-resolution video back to the UE. However, these servers are often situated at a considerable distance from the UE. By utilizing the computing resources deployed on the 6G network to process these videos, user latency can be significantly reduced, enabling more real time interaction on the UE side.
5. Enhanced Joint Coordination of Network Resources. Both communication control nodes, computing control nodes, and computing resources are deployed within the 6G network. 6G operators can utilize unified policy assurance and resource scheduling mechanisms to jointly manage communication and computing resources, instead of managing them separately. A globally optimized scheduling mechanism is clearly superior to two locally optimized ones, which helps 6G operators improve the utilization efficiency of communication and computing resources within the network.
6. New Service Opportunity. Beyond offering communication services, positioning services, and integrated sensing services, 6G operators can provide computing resources to applications on the UE, third parties, and even operator-owned video services to improve video resolution. 6G operators can also enhance high-resolution for the user when providing video call services (e.g. Video over 6G). These new resources and services introduce a new revenue stream.
6.50.2 Pre-conditions
The 6G network supports AI functions and deploys certain computing resources, enabling it to provide 6G Computing Service, AI service and communication service for video super-resolution.
Due to deployment cost constraints, the video service provider has decided to rent computing resources within the network to perform real time video super-resolution, thereby offering users live streaming services with high video resolution.
The 6G operator or 3rd party video service provider can pre-configure and store the following information inside the 6G network to help perform video super-resolution: AI models/algorithms for video super-resolution, AI models/algorithms selection rules, video service provider information and so on. These AI models/algorithms and selection rules can also be further updated to improve the service experience.
Alice is a live-streaming host for outdoor events, and Bob watches Alice's stream on the application on his UE. Both use the same video application. Bob, as a live streaming viewer, has selected a high-resolution preference (e.g. Full HD) for the video via the user interface on his mobile terminal. Such user preference is delivered to the 6G network and 6G network will take user preference into account when improving video resolution.
6.50.3 Service Flows
Figure 6.50.3-1: Real-Time Video Super-Resolution Service provided by 6G Network
The service flow for the real time video super-resolution service offered by 6G network is shown in Figure 6.50.3-1.
1. The third-party video service providers or 6G operator can pre-configure specific AI model/algorithm selection rules and related AI models/algorithms tailored for different types of content, such as scenery, sports, or games. The same AI model/algorithm applied to different types of video content can produce varying effects. Besides, the 6G network can pre-train AI models for real time video super-resolution and store them within the network.
2. Alice is streaming video outdoors via a 6G network, but due to limitations with her user equipment and network uplink bandwidth, the video is captured in standard or suboptimal resolution (e.g. 540p).
3. Alice's UE transmits the standard or suboptimal resolution live streams to the 6G network.
4. When the video stream is received, the 6G network considers the user preferences provided by Bob, indicating a higher resolution than the current one, and therefore, the 6G network decides to apply video super-resolution. 6G network coordinates the AI nodes, computing nodes, and communication nodes to offer real time video super-resolution services for this video stream.
The 6G network can determine network-based or mobile-based for the real time video super-resolution process, and further select an appropriate AI model/algorithm considering following aspects:
a) Network aspect: considering different network downlink communication performance and network computing load, either network-based video super-resolution or mobile-based video super-resolution can be performed. For example, when communication links performance between the 6G network and Bob is optimal, the 6G network can handle real time video super-resolution and deliver high-definition content directly. If communication links performance between the 6G network and Bob is suboptimal, low-resolution videos will be received from the network and maybe enhanced on the UE/client side utilizing the local computing resources;
b) Video processing aspect: a suitable AI model/algorithm should be selected for each specific video type, as the same AI model/algorithm has different effects on video content (e.g. scenery, sports, or games) regarding the colour, block size, and details of the video;
c) User aspect: for different user preferences (e.g. Full HD, Ultra HD) on video resolution, 6G network can accordingly choose different AI models or traditional algorithms to satisfy the user experience.
5. The 6G network selects and applies an appropriate AI model/algorithm for the real time video super-resolution service.
6. The 6G network selects the most suitable computing resources considering factors like computing load, node location and other potential information. Utilizing available computing resources, the network applies the AI model/algorithm to enhance the video resolution.
7. The enhanced high-resolution live streams, such as 1080p, are transmitted to viewers, including Bob.
8 The video is decoded and presented to the user, Bob.
9 6G network monitors the performance of real time video super-resolution and periodically sends reports of real time video super-resolution outcomes to the manager. Such reports may encompass various metrics, including AI model inference accuracy, measured by calculating pixel differences between the upsampled video and the original high-definition video. They may also include Motion Estimation, which assesses whether the model effectively restores details in fast-moving scenes, as well as QoE metrics. Such reports can be used by an operator or 3rd party video service provider to further update or retrain AI models, or update rules or algorithms in order to enhance the user experience for video services.
6.50.4 Post-conditions
Bob can enjoy high-resolution live streaming. The video service provider offers real time video super-resolution services to users at a reasonable cost. The 6G operator can offer real time high-resolution video to users when providing operator-managed video services.
6.50.5 Existing features partly or fully covering the use case functionality
None.
6.50.6 Potential New Requirements needed to support the use case
[PR 6.50.6-1] Subject to operator policy, the 6G network shall be able to manage and coordinate various network operations (e.g. AI model training/selection, computing resource selection, communication performance monitoring) upon receiving a request (e.g. combined 3GPP service that combines services such as 6G AI service and communication service) with the requested service requirement.
[PR 6.50.6-2] Subject to operator policy, the 6G network shall be able to support a mechanism to guarantee the user experience when providing combined 3GPP service (e.g. combines 6G AI service and communication service).
[PR 6.50.6-3] Subject to operator policy, the 6G network shall be able to monitor the performance (e.g. AI model inference accuracy) and report them to the 3rd party.
6.51 Use case on network-based intelligent assistance (e.g. for autonomous driving) by a network-native AI Agent
6.51.1 Description
The business market for solutions that provide navigation and assistance to traffic participants, particularly in smart cities is growing rapidly. For example, the overall market of Advanced Driving Assistance Systems (ADAS) is estimated to grow from USD 40.98 billion in 2022 to USD 186.29 billion in 2032 with a Compound Annual Growth Rate (CAGR) of 16.3% [287]. Furthermore, the global autonomous vehicle market size was valued at USD 1,921.1 billion in 2023 and is projected to exceed USD 13,632.4 billion by 2030, exhibiting a CAGR of 32.3% during the forecast period [288]. Finally, the global automotive AI market size, valued at USD 2.99 billion in 2022, is projected to grow to USD 14.92 billion in 2030 with a CAGR of 22.7% [289]. These very promising forecasts suggest that AI-driven navigation support for various traffic participants (vehicles, cyclists, pedestrians, etc.) could become a good business opportunity for 3GPP operators.
For a range of applications related to autonomous driving (e.g. taxis, consumer vehicles, transportation delivery, etc.), the fleets will potentially have service providers involved in testing, mapping, driver assistance, cloud services, and communication. To them, the 3GPP operators have the opportunity to provide network-based wide-area intelligent services and capabilities, integrated in 3GPP network and in an efficient, collaborative, and dynamical way.
There exist several advantages of providing the general intelligent assistance service via the 3GPP network:
Through sensing capabilities, 3GPP networks have timely access to unique and relevant information about the wide-area physical environment (e.g. terrain features, traffic patterns, weather characteristics). Such data together with the 3GPP network information (e.g. 3GPP service provisioning data, network configuration and deployment information, O&M data, subscription data, 3GPP monitoring data, 3GPP data analytics results) are not available at 3rd party clouds or at UEs. Therefore, a 3GPP network has the advantage in training and inferencing AI/ML models using these data to provide improved and timely assistance services.
Thanks to the wide-area connectivity and the capabilities to perform distributed AI tasks (e.g. distributed AI/ML model training and inference), the 3GPP network can perform various tasks more reliably and efficiently than UEs. For example, the 3GPP network can use a split ML model to process jointly multi-modal data from its distributed sensors, in order to produce a real time unified representation of the environment that is needed for this type of use cases.
By processing data closer to end users in the Service Hosting Environment and/or in 3GPP network, the 3GPP network can offer reduced service latency and enhanced data privacy compared with purely cloud-based solutions. This is crucial for real time and privacy-sensitive applications (e.g. for autonomous driving, collaborative robotics, in privacy-restricted areas).
Providing intelligent assistance services in some usage scenarios (e.g. autonomous driving, collaborative robots) requires integration of communication, AI, and sensing, which can be realized by the 3GPP network in a self-contained way.
Even though this use case is explained in the context of AD, the intelligent assistance service is based on a general set of capabilities of the 3GPP network that is applicable to support a wide range of applications and industry sectors. In the context of this use case, the intelligent assistance service is provided to the vehicle’s driving application (e.g. provided by the original equipment manufacturer (OEM)) which is responsible for taking the final decisions.
Figure 6.51.1-1 illustrates an example of UE integration in a vehicle’s onboard control system. The integrator can be the provider of the on-board control system for car manufacturers. The integrator has full control of what is onboard of the vehicle, and is responsible for enabling UE’s access to vehicle’s onboard sensors, etc. Based on [291], V2X App exchanges C-ITS messages with V2X AS or with other V2X Apps; OEM App (which integrates services from OEM AS into vehicle) is provided by OEM manufacturer, the driving application (which may be the “Driving Automation System” in [291]) in the vehicle interacts with the V2X UE via OEM App. Additionally, as the OEM App has access to the Human-Machine interface of the vehicle’s infotainment system, the driving application can use that interface to interact with the driver.
NOTE: The vehicle’s onboard control system shown in grey is out of 3GPP scope and for illustrative purposes only.
Figure 6.51.1-1: An example of the intelligent assistance service for autonomous driving.
Categories of intelligent assistance services offered by the 3GPP system:
The intelligent assistance service of the 3GPP network may (based on what is expressed in the Intent) involve providing various more specific types of services (e.g. related to Categories 1, 2, and 3 described below) to the driving application and/or the driver. Each of these specific services are characterized by their own requirements in terms of processed input data, management complexity, radio and computational resources. Based on these different requirements, the specific services could be grouped into three categories, e.g.:
Category 1: the network assistance is done based on (i) subscriber vehicle’s on-board sensing information and (ii) available 3GPP network information. This category of services focuses on improving the vehicle’s sensing capabilities. In this category of services, the 3GPP network provides selected algorithms and trained AI/ML models to the vehicle for local inferencing using the vehicle’s sensor data. The provided algorithms could add new sensing capabilities by extracting and processing new features from the vehicle’s available sensing data, or augment the existing sensing capabilities when provided models are better suited for local driving environment and conditions (e.g. terrain features, heavy snow, etc.). As detailed further, such AI/ML models and algorithms could be obtained, e.g. by re-training general AI/ML models (e.g. from OEM, 3rd parties) with wide-area 3GPP sensing data and 3GPP network data. When it comes to the deployment of the algorithms at the vehicle, the 3GPP network is still responsible for monitoring the algorithms’ performance to ensure that they meet the pre-defined performance specifications (processing latency, accuracy, reliability, etc.). This can be done, e.g. by sending periodically performance analytics data from the UE to the 3GPP network for evaluation such as comparing the local model’s output with the high-quality results available at the 3GPP network. Additionally, compared with services of Category 2 and 3 described below, this category of services offers relatively limited assistance, but introduces very low operational costs to the 3GPP network operator because the vehicle bears the cost of running the algorithms locally.
Category 2: the network assistance is done is based also on (iii) 3GPP network-based sensing data. In this category of services, the 3GPP network uses its own sensing capabilities to provide additional assistance to the vehicle (e.g. occluded-view assistance, infrastructure assisted environmental perception, etc.), possibly via UE-3GPP collaborative or split inference. This category of services offers more comprehensive assistance compared with Category 1 services, but it introduces moderately increased costs to the 3GPP operator (e.g. using and maintaining 3GPP sensing infrastructure, performing inference using sensing data, etc.).
Category 3: different from services in Category 2, in this category, the network assistance is done also using (iv) 3GPP network knowledge databases and external data sources (e.g. Internet, public databases). The 3GPP network provides assistance by solving complex tasks requiring access to external data (e.g. advanced routing which requires accessing information about traffic conditions in a big city and executing best-path optimization algorithms via executing training and inferencing of AI/ML models). This category of service capabilities introduces the most comprehensive and reliable assistance but introduces higher costs for the 3GPP operator (maintaining and updating network with external knowledge databases, processing much larger quantities of data).
Whenever a specific service requires providing an AI/ML model or algorithm for local inferencing, the integrator (with full control of the vehicle on-board control system) of a car manufacturer is responsible for integrating 3GPP intelligent assistance service (based on AI inferencing locally done in V2X UE) with the driving application in the vehicle.
For Category 2/3, the existing V2X framework and V2X-related specifications can be followed, such as architecture enhancements work in SA2 (e.g. TS 23.287 [301]), and SA6 V2XAPP (e.g. TS 23.286 [302] on application layer support aspects). For the intelligent assistance service in this use case, the collaborative inference results (e.g. intermediate results from the UE) are further processed/combined at the AI/ML endpoint in the 3GPP network (or in Edge Compute Domain), before being fed (as a new source input) to the autonomous driving application servers (in the cloud side, managed by the OEM) that will further interact with the driving application in the vehicle.
As the 3GPP Network controls delivery of 3GPP services, service requests shall be received here are parsed and acted on by performing authentication, authorization, policy control, various management (session, mobility, connectivity to services in DNs, etc.), etc. To enable the 3GPP intelligent assistance service, the objectives/intention of Intent-based service requests are received by an AI Agent in the 6G Network. The AI Agent interprets the Intent, uses its knowledge of 3GPP network (available services, network capabilities, specific services provided by the fine-tuned AI/ML models saved in the 3GPP CN model pool) to reason and plan specific services falling in Categories1/2/3.
As a number of 3GPP services (e.g. communication, sensing, AI inference, etc.) are needed to support objectives expressed in the Intent and there is dependency among the constituent 3GPP services as needs and conditions change, the 6G network, if with AI Agent, would need to in runtime orchestrate, configure, instantiate and adapt these 3GPP services dynamically to provide the desired service and experienced to the subscribers or the third-party applications.
Main service components and their functionalities in the 3GPP network:
AI Toolbox – a warehouse of various pre-trained AI/ML models, datasets, and algorithms which are stored by the 3GPP operator in the 3GPP network, and which are used to provide specific services (e.g. related to Categories 1, 2, 3). This may include, e.g. an AI/ML model fine-tuned for object detection in snowy conditions using a vehicle’s front camera, another AI/ML model for object detection using vehicle’s radar data, a dataset of environment sensing data collected by the 3GPP operator, a shortest-path search algorithm, a calculator, etc. Each of these models and algorithms are verified (by the 3GPP operator or a 3rd party company) to meet pre-defined performance specifications such as processing latency, inference accuracy, reliability, etc. The 3GPP operator can obtain these AL/ML models and datasets by (i) purchasing fine-tuned AI/ML models and datasets from a 3rd party source, or (ii) re-training general AI/ML models and collecting datasets using own environment sensing and network data (e.g. any meta-data in the 3GPP network, including the configuration data of the deployed 3GPP services and 3GPP network, subscriber data, 3GPP network analytics, information from external systems but stored in the 3GPP network, etc.). Furthermore, the 3GPP operator decides which specific services should be supported by the AI Toolbox, depending on the local market potential.
intelligent assistant – an AI Agent in the 3GPP network that understands the Intent and uses an AI interface to assist with digital tasks. It provides general intelligent assistance to the subscriber as a means to consuming the 3GPP services, i.e. it is responsible for (i) understanding the Intent of the subscriber (i.e. translating the need into technical tasks), (ii) evaluating how the 3GPP network capabilities could provide the desired intelligent assistance to the subscriber (e.g. based on the received Intent, 3GPP network status and capabilities, the available AI Toolbox, and the environment information), (iii) activating and executing selected services and functions from the AI Toolbox, (iv) monitoring performance of activated services (e.g. as described for Category 1 services, the network-side inference results using higher-performance models can be used as a reference to check the local inference accuracy by vehicles using smaller dedicated models) and applying corrections if needed, and (v) providing relevant assistance information to the subscriber, i.e. sending data related to the activated services that are helpful for solving the subscriber’s Intent, and which can be expressed in a format and language suitable for the subscriber.
To perform its tasks, the intelligent assistant should be able to intelligently interpret Intent (in order to understand the Intent and translate it into technical tasks, i.e. AI/ML instructions and/or actions), should have awareness of the physical environment and the network (in order to decide which specific 3GPP services out of many should be activated, and to evaluate if the Intent has been accomplished), and should be able to activate and monitor selected 3GPP services enabled by the AI Toolbox (including 3GPP sensing service and algorithms running locally at the subscriber’s side). The intelligent assistant is AI-based but does not have to be a LLM. For example, the 3GPP operator can deploy the intelligent assistant by adapting and re-training a general-purpose AI Agent from a 3rd party or open sources. Furthermore, the AI Agent could be re-trained to interpret Intents and provide assistance in more than one application or industry sector.
Main service components at the UE:
Intelligent Assistance Application Entity – pre-configured and provided by the 3GPP network operator. In general, it can be deployed on smart devices such as smart phones, in-vehicle on-board control system, etc. This application client provides the subscriber a service interface with the intelligent assistant (e.g. to relay subscriber’s queries to the intelligent assistant, to visualize the intelligent assistant’s response, to receive data from the 3GPP network, etc.). Furthermore, given the subscriber’s consent, the Intelligent Assistance Application Entity can receive a dedicated fine-tuned AI/ML model from the 3GPP network and use the vehicle’s on-board sensing capability to provide services of Category 1. It can also perform basic monitoring of locally deployed AI/ML models and send analytics data to the 3GPP network for evaluation.
6.51.2 Pre-conditions
A subscriber UE (e.g. a vehicle) has 6G connectivity. Optionally, the UE is equipped with on-board sensors (e.g. cameras, radars, LiDAR, Global Positioning System (GPS), etc.), has computing capabilities, and can receive AI/ML models from the 3GPP network for performing local inference based on on-board sensing data.
AI Toolbox stored in the 3GPP network contains AI/ML models (including AI/ML models trained with 3GPP network data and 3GPP sensing data from the deployed 3GPP network), algorithms, and datasets which are useful to provide the intelligent assistance expected by the subscriber.
The 3GPP network maintains real time awareness of the physical environment and of the network state which is needed to pre-select and execute specific services.
6.51.3 Service Flows
1. A subscriber UE registers to the 3GPP network and establishes a connection.
2. The subscriber requests (in the context of navigation) intelligent assistance service and the UE receives the Intelligent Assistance Application Entity from the 3GPP network.
3. A subscriber uses the received Application Entity to input an Intent (e.g. in a natural language) that indicates its on-board sensing capabilities.
Example: a vehicle sends the Intent “I am a vehicle of type X. I carry heavy objects. I would like to safely get to place A.” and lists its on-board sensor capabilities: a front camera, a GPS localizer, a rear radar.
4. Via the Application Entity, the intelligent assistant at the 3GPP network receives the Intent. Then, the assistant evaluates the Intent and decides which specific services out of all possible specific services offered by the 3GPP network are most relevant and useful to the subscriber in order to provide the required assistance. The intelligent assistant sends back to the subscriber a list of proposed specific intelligent assistance services, e.g. object detection, collision avoidance, parking assistance, emergency trajectory alignment, automated intersection-crossing, infrastructure assisted environmental perception, together with their performance specifications.
Example continued: the intelligent assistant responds with an adapted list of proposed specific services for the vehicle: (i) Category 1 specific service: a pre-trained ML model for detecting objects recorded by the vehicle’s front camera, and which is re-trained to be better adapted to local environmental and topological conditions, (ii) Category 2 specific service: obstructed-view assistance at intersections, done by the 3GPP network using 3GPP sensing and computing in 3GPP network (e.g. conduct AI model inferencing to generate obstructed-view assistance results), (iii) Category 3 specific service: safe routing that avoids steep inclines and abrupt turns, which additionally requires the 3GPP network to access external information about traffic conditions in the whole city to solve complex AI tasks.
5. The subscriber may use the Application Entity to select services in the list.
Example continued: the vehicle accepts services (ii) and (iii), but declines the service (i) because it already has a built-in AI/ML model for dangerous object detection with similar performance specifications.
6. Depending on the selected specific service, the 3GPP network provides necessary AI/ML models and data to the Application Entity on the subscriber’s side.
Example continued: the 3GPP network does not provide any pre-trained AI/ML model, because the vehicle declined the service (i).
7. The 3GPP network activates selected services, and the subscriber starts to receive from the 3GPP network the assistance information until the Intent is accomplished or cancelled. The assistance is provided via the subscriber’s Application Entity. The 3GPP network monitors the performance of deployed services in real time (e.g. to ensure smooth handovers, to manage radio and computing resources, etc.).
Example continued: the vehicle (e.g. Driving Application in Figure 6.51.1-1) receives intelligent assistance related to services (ii) and (iii) from the 3GPP network until the vehicle reaches place A.
6.51.4 Post-conditions
The subscriber is able to request, interpret, consume, and explore (i.e. view a catalogue of) the provided 3GPP intelligent assistance services to improve its safety and efficiency.
6.51.5 Existing features partly or fully covering the use case functionality
1. 5G Advanced (Rel-18) allows for positioning accuracy of around 10 cm for 90% of the cases given specific (i.e. good) channel conditions. For the proposed use case, reliability of positioning accuracy should be significantly improved, such that centimetre-level positioning accuracy of moving objects is available for more than 90% of the cases, in various channel conditions, and with small delay.
2. TS 22.261 [14] clauses 6.40.2.1 and 7.10.1 describe the communication service requirements for AI/ML model transfer in 5GS via direct network connection, the KPIs include split AI/ML inference, FL, and AI/ML model downloading between UE and AF/AS. The 5G network is regarded as a data pipeline to support 3rd party AI/ML applications.
Therefore, the current standards do not provide network-native real time intelligent assistance to subscribers (e.g. for autonomous driving) utilizing 3GPP sensing of the environment, though that could be implemented at an application layer as an isolated system. For the proposed use case, not only does it reply on sensing that must meet stringent accuracy and latency requirements, but also on 3GPP network-based/native complex problem solving (using AI) whilst providing the required reliability and latency (e.g. joint latency from AL/ML model inference and communication).
3. 5G and 5G Advanced (Rel-16, Rel-17, and Rel-18) consider some specific services mentioned in this use case (e.g. for V2X [[[SUGGESTION_START]]37[[SUGGESTION_END]]]). However, current 3GPP standards do not provide the general intelligent assistance service (applicable in different context, not just autonomous driving) that adaptively combines many specific services from 3GPP network, and which is available over a wide area (e.g. with hand-overs), and which meets stringent reliability and latency constraints (e.g. the intelligent assistant timely response to Intent, timely providing obstructed-view assistance results via guaranteed joint latency from inference and communication).
4. V2X specifications, such as architecture enhancement work TS 23.287 [301] (SA2), and V2XAPP work in TS 23.286 [302] on application layer support aspects (SA6). Corresponding use cases include tele-operated driving despite being not yet commercialized, automated valet parking, etc.
5. Rel-18, TR 28.912 [303] and TS 28.312 [147] define features of Intent Management Network which allows subscribers to express their network-oriented intents (typically human-readable) about, e.g. delivery of a specific communication service or desired properties of the network. TS 28.312 [147] has the following definition:
intent: expectations including requirements, goals and constraints given to a 3GPP system, without specifying how to achieve them.
TS 28.312 [147] clause 4.1.3 (“intent expectations for different types of management needs”) clarifies intent expectation is for “delivering network and service related object (e.g. network, service, slice)” and for “network and service related object (e.g. network, service, slice) performance”. Clause 4.5.1 (“intent expectation”) further clarifies the following:
The object may be a 3GPP managed object like a network slice, subnetwork (e.g. radio network) or other objects like a service.
6. 5G Advanced (Rel-18) defines Application Awareness (AA) capability, primarily for XR services. The awareness is achieved by exchanging additional information among network elements of the system, such as UE, RAN, UPF, CN, and AF. In 5G, AA helps the 3GPP network to better adapt radio resources and QoS (e.g. latency, scheduling, handovers) without compromising the XR service quality experienced by the subscriber. For the proposed use case, the AA capability should be significantly extended, including a capability to activate and adapt specific AI services in order to maintain high service quality.
7. 5G Advanced (R19) TS 22.137 [6] specifies in clause 5.2.1 the stage 1 requirement of 5G wireless sensing service to be provided by 5GS in a target sensing service area, as well as in clause 5.2.3 the stage 1 requirements on network exposure, specifically for 5G network to securely report sensing results (incl. the combined sensing result derived from the joint processing of the 3GPP sensing data and non-3GPP sensing data) to a trusted 3rd party.
6.51.6 Potential New Requirements needed to support the use case
NOTE 1: The intelligent assistance service depends on the context. For autonomous driving, the provided 6G services (e.g. communication, sensing, AI inference) support the autonomous driving application, e.g. collision avoidance, parking assistance, emergency trajectory alignment, automated intersection-crossing, etc.
NOTE 2: The mention of AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent doesn’t imply or preclude any architecture assumption or solutions.
[PR 6.51.6-1] Based on operator policy, the 6G system shall support mechanisms (e.g. AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) in the 6G network to provide 3GPP/6G services, which includes coordination of multiple 6G services (e.g. communication, sensing, AI service).
[PR 6.51.6-2] The 6G network shall support suitable means to support the training of AI/ML models in the Service Hosting Environment, using 6G network data (e.g. configuration data of the deployed 6G network/services, network analytics data) and 3GPP sensing data collected from wide areas, if needed.
NOTE 3: This requirement considers among others the availability of some of the above-mentioned 6G network data and 3GPP sensing data collected from wide area, when available in the Service Hosting Environment, or/and in the 6G network, necessary for training the intelligent assistance service-related AI/ML models.
[PR 6.51.6-3] Subject to operator policy, the 6G network shall be able to provide and expose AI inference service to the user/subscriber, and ensure required joint (inference, communication) latency.
6.52 Use case on smart support for data collection and fusion in multi-agent scenarios
6.52.1 Description
This use case considers a smart cooperation scenario for a group of robots to collaboratively build an information set (e.g. dataset or knowledge base in AI/ML) through data/sensor fusion [292], [293], [294] when the fusion of data from multimodal sensors is conducted by multiple robots / multiple agents collaboratively [295], [296].
NOTE: The term “fusion” in “data fusion”, “sensor fusion” and so on, is also exchangeably used as “integration” in this use case.
The term “smart” is intended to suggest a concept of consuming low-energy, energy-efficient, resource-efficient and/or situation-aware means of communication to support an intended fusion task for the group of robots.
The use of “levels of fusion” is expected to help the United Nations Sustainability Development Goals (SDGs) [87] in several aspects.
Given that the recent cellular technology enablers are designed in resource-efficient ways for various types of resources (e.g. radio resources, network resources, material, such as battery related), such considerations can also help provide affordable 6G services in the society, especially when certain groups of residents, patients, public-safety officers, or underrepresented need the communication services the most at a critical point in time in their everyday living.
There are various scenarios of multi-robot / multi-agent group operations in which a robot should be able to identify certain information (e.g. detecting an object, detecting multiple objects at the same time) or collect data that should be shared with other robots in that group in real time. Figure 6.52.1-1 shows two examples.
When a robot in a group begins to collect certain data (or information), the robot should determine what it should do with the data, such as whether to share the data without any pre-processing inside the robot (i.e. applications layer role utilizing some input coming from the communications layer), or to perform certain level of pre-processing before sharing the processed form of data with other participants (or participating robots) in the group for certain task.
(a)
(b)
Figure 6.52.1-1: Examples of using data collection (object detection and estimation) where objects are in different dimension/size and/or in different ranges. (a) Two distinct objects (A and B) of the same size at the different range (b) Two distinct objects (A and C) of different sizes at the same range (approximately).
Figure 6.52.1-2 shows an example where both communication opportunity needs (i.e. sensor data that are outcome of one of multiple levels of fusion process inside the originating robot) and communication opportunities (i.e. how much communication resources are likely to be available for a robot when there are multiple robots in place) are fluctuating, leading to a complex scheduling load onto 6G systems, such as at a node belonging to radio network of 6G system or to a core network of 6G system.
In order for 6G systems to be able to efficiently and reliably support the dynamic need of transmission opportunities, it is necessary to ensure that robots (as a UE) should be provided with a suitable means to share their intents (e.g. levels of fusion, desired amount of traffic to transmit at certain point in time).
Figure 6.52.1-2: Example of different levels of communications opportunity need (or transmission opportunity need) under a combination of normal and challenging (or extreme) communication conditions that fluctuate dynamically over time.
6.52.2 Pre-conditions
There are n robots (UEs) in a robot group: Robot 1, Robot 2, …, Robot n.
These n robots have capability of using direct network connections and indirect network connections upon availability.
Each robot has data collection capability (e.g. sensor data collection, 3D sensing) and data processing capability (e.g. pre-processing for AI-related operations).
6.52.3 Service Flows
Data Collection and Fusion: All participating robots (UEs) begin collecting data for a fusion cent[[SUGGESTION_START]]r[[SUGGESTION_END]]e, a trusted third party, to perform efficient data or sensor fusion.
Complex AI Traffic: Each robot runs multiple applications that generate different types of AI traffic with varying QoS characteristics. This includes both intra-robot AI operations (e.g. communication between a robot's sensing and processing parts) and inter-robot communication sessions, which are far more numerous and diverse.
Sharing Network Demands: The robots share information about their traffic demands and QoS characteristics-which change dynamically over time-with both the 6G system and the application service (the fusion cent[[SUGGESTION_START]]r[[SUGGESTION_END]]e).
Centralized Coordination: An AS, acting as the trusted third party, coordinates the robots' communication needs by performing tasks like pre-processing and task splitting. For example, the server can instruct a robot to wait or perform more pre-processing even if it's ready to send data, preventing it from initiating communication on its own.
Dynamic Traffic Management: As a result of this centralized coordination, the traffic characteristics and QoS requirements for the AI-related traffic will dynamically change over time.
These changes depend on various factors, such as the distance between robots or the distance between a robot and a specific object. The 6G network must be able to adapt to these changes to ensure optimal performance.
6.52.4 Post-conditions
All the participating robots (UEs) were able to enjoy stable and reliable communications, suitable for their own communications needs.
They were able to avoid wasting network resources at best, through the advanced feature of the 6G system on precise and timely adaptation of the changing situation on traffic demand along with their QoS characteristics.
6.52.5 Existing features partly or fully covering the use case functionality
Clock synchronisation: TS 22.104 [64]
clause 5.6.1 Clock synchronisation service level requirements
clause 5.6.2 Clock synchronisation service performance requirements
clause 7.2.3.2 Clock synchronisation requirements
NOTE 1: The types of sensor data and media that robots are collecting, pre-processing and sharing with each other and/or with edge cloud (or edge server, cloud server) are related to the need of fulfilling the above sets of requirements. Clock synchronization requirements are mostly related to Proximity-based Services (ProSe) communication scenarios.
Timing resiliency: TS 22.261 [14]
clause 6.36.2 General requirements to ensure timing resiliency
clause 6.36.3 Monitoring and reporting
clause 6.36.4 Exposure
NOTE 2: Timing resiliency is considered as a set of preconditions that ensure the “clock synchronization” especially when robots (as a UE) or the leader robot(s) (as opposed to “robot followers”) are served by at least one PLMN.
Multi-path relay: TS 22.261[14]
clause 6.9.2.1 support of a traffic flow of a remote UE via different indirect network connection paths
Service continuity: TS 22.263 [67]
clause 5.5 Service continuity
NOTE 3: Service continuity is not necessarily related to all types of sensor data and media.
6.52.6 Potential New Requirements needed to support the use case
[PR 6.52.6-1] The 6G network shall be able provide a suitable means with a very high efficiency and reliability, required by trusted third party (e.g. AS), to accommodate the dynamic changes of traffic demand and QoS characteristics in a group of trusted third parties (e.g. multiple applications running in multiple robots that are UEs).
NOTE 1: The dynamic changes of traffic demand and QoS characteristics might be caused e.g. by using different levels of data/sensor fusion within each robot of the robot group. The changes of traffic demand include changes of AI-related traffic type (e.g. raw data, pre-processed data, AI/ML model, inference result).
NOTE 2: Each robot can have multiple applications. There can be multiple intra-robot AI-related operation sessions between robot’s applications layer (e.g. “robot sensing part”, “robot processing part”, “robot actuating part”) and communications layer (e.g. “robot communication part”). Also, there can be many more inter-robot AI-related communication sessions, each of which might have different QoS characteristics and traffic demand characteristics.
[PR 6.52.6-2] The 6G network shall be able to provide a suitable means to allocate network resources (e.g. network slice) to a group of trusted third parties (e.g. applications running on multiple UEs/robots or third party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents), based on its availability and capability, considering dynamic changes of traffic demand and QoS characteristics.
[PR 6.52.6-3] Subject to operator’s policy, the 6G network shall be able to support dynamic QoS needed for a group of UEs (e.g. multiple robots or third party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents) when traffic characteristics change is predicted to occur or has occurred (e.g. change between AI/ML model transfer and AI/ML inference result transfer).
6.53 Use case on AI-driven smart factory with computing service
6.53.1 Description
In smart factory, equipment, such as AGVs, IoT sensors, industrial cameras and industrial robots, generates massive real time data but suffers from severely limited on-device computing resource and computing capability.
The deep integration of computing service in AI-driven smart factories drives the paradigm-shifting operational improvements: zero-downtime production becomes achievable through edge-based anomaly detection, sustainable manufacturing in enabled via smart scheduling, and agile innovation accelerates the product line deployment.
Computing service is required due to the needs of low-latency inference for defect detection, heavy training for digital twin simulations, and edge-cloud collaborative training for predictively maintenance analytics. Collaborative AGV obstacle avoidance and anomaly monitoring require extreme End-to-End latency and there is unattainable for complex AI inference on local devices due to the limitation on devices. Besides, computing service could help with the data-intensive workloads in the factory with energy efficiency extending the battery life of those devices.
This use case leverages the 6G Computing Service to enable intelligence and efficient AI-driven smart factory, where computing service helps with real time perception, decision making, and evolution.
6.53.2 Pre-conditions
The factory is equipped with devices that can connect to the 6G system. There are industrial robots manufacturing the products. There are AGVs carrying the products to the next production line. There are sensors monitoring the temperature, humidity, and vibration, etc.
There is a Service Hosting Environment AA near the factory that has pre-trained lightweight AI models deployed and some computing resource, which could support anomaly detection fulfilling the low latency requirement of the factory.
There is a Service Fosting Environment BB that is far from the factory and has adequate computing resource for AI training and inference for predictive maintenance analytics and new production line design.
AI models for predictive maintenance and new production line design are split and stored in Service Hosting Environments AA and BB.
6.53.3 Service Flows
Due to the limitation of the vibration sensors and cameras, they offload the computation task for anomaly detection to the 6G network. The 6G network evaluates that the latency requirement could be fulfilled when the task is offloaded to the Service Hosting Environment AA.
The data from vibration sensors and cameras are transferred to the Service Hosting Environment AA. Utilizing the AI models within AA, the faulty within the production line and safety within the factory are monitoring.
When it detects an anomaly, the network sends an alarm to the instructor’s UE so that the anomaly could be checked and fixed.
The smart factory would also like to monitor the manufacturing and predict the potential maintenance analytics with AI models. The smart factory would like to maintain the data security and privacy of its own manufacturing data. So that, the 6G network firstly offloads the task firstly to the Service Hosting Environment AA for data cleansing and partial model training, then transfer the intermediate result to the Service Hosting Environment BB for predictively maintenance analytics.
6.53.4 Post-conditions
Faulty and anomaly within the smart factory could be detected in time even though there is no human monitoring the production lines.
Based on the predictive maintenance analytics, maintenance could be carried out when necessary and shorten the downtime of the production lines.
6.53.5 Existing features partly or fully covering the use case functionality
There are requirements in TS 22.261 [14] clause 6.5 on efficient user plane to meet localization requirement like low latency, low bandwidth pressure, and improve the user experience. Those requirements focus on the offloading services to a Service Hosting Environment, and do not take the computing resource into consideration, and there is a lack of requirements to support data transferring between multiple service hosting environment endpoints.
There are requirements in TS 22.261 [14] clause 6.40 on AI/ML model transfer in 5GS supporting AI/ML operation splitting between AI/ML endpoints, AI/ML model/data distribution and sharing over 5GS and Distributed/FL over 5GS. It is supported to offload the computation-intensive, energy-intensive parts to the network endpoints, whereas leave the privacy-sensitive and delay-sensitive parts at the end device.
6.53.6 Potential New Requirements needed to support the use case
[PR 6.53.6-1] Subject to operator policy and regulatory requirement, the 6G network shall be able to support the selection of multiple Service Hosting Environments for 3GPP services and 3rd party services.
[PR 6.53.6-2] Subject to operator policy and regulatory requirement, the 6G network shall be able to support the coordination amongst multiple Service Hosting Environments for 3GPP services and 3rd party services.
6.54 Use case on AI-optimized smart call assistance for telecom networks
6.54.1 Description
A telecom operator integrates an AI-powered smart call assistance service into its network. This service leverages in-network AI (e.g. AI Agents in the 6G network) to optimize voice and call quality dynamically based on real time network conditions, user intent, and historical data. AI mechanisms including AI Agent in the 6G network proactively enhances call experiences, e.g. reducing dropped calls, latency, and jitter.
6.54.2 Pre-conditions
The telecom network has the proposed AI-powered 6G network with AI4NET capabilities, e.g. in forms of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents.
The AI Agent in the 6G network can analyse real time call quality metrics, predict potential call degradation, generate optimization proposals, make the adjustments and decide whether to update the models based on the feedback of user experience.
Smartphones and VoIP devices (UE) support AI-assisted call quality enhancement. AI-driven call quality optimization is handled by the network. In this use case, it’s assumed that the UE has AI capabilities that can detect calling conditions and user experience in real time.
NOTE: The collection and sharing of user experience data from the UE for AI-assisted call quality optimization are subject to explicit user consent and comply with applicable privacy regulations.
If the UE supports QoE feedback reporting, it will provide the user with a clear control to enable or disable this functionality.
AI models are trained using historical call data and network data, and are applied in real time to optimize performance based on current conditions.
6.54.3 Service Flows
A user initiates a voice or video call via a telecom network.
The AI capabilities deployed in the UE monitor call conditions and user experience in real time, analysing factors like jitter, packet loss, and background noise.
When quality degradation is detected by the UE utilizing AI capabilities the UE requests adjustments from the 6G network.
The AI Agent in the 6G network generates the call quality optimization proposal, e.g. dynamically adjusting codecs, increasing bandwidth allocation and packet prioritization, to optimize the call.
The Network AI Agent sends a response to the UE for the adjustments, and the Network AI Agent validates the effect of these, e.g. using network digital twin.
The UE with AI capabilities collects the real time user experience specific data. It continuously monitors call quality at the user level, and it can send feedback to the Network AI Agent if the user still experiences poor quality after the adjustments.
The Network AI Agent continuously analyses call quality-related data across the network, and continuously monitors call quality at the network level.
The user terminates the call.
The UE with AI capabilities summarizes QoE metrics from UE during the call.
If the call quality was poor throughout the call, the UE can notify the Network AI Agent which may decide whether the model update is needed or not.
If the Network AI Agent detects a persistent issue affecting multiple users, it decides for further analysis and model updates.
The Network AI Agent can continuously analyse aggregated call quality data across the network. The data comes from the UE.
If needed, the Network AI Agent trains and updates new AI models to improve future call optimizations.
The improved model is pushed back to the Network AI Agent for real time use in upcoming sessions.
After all updates and trainings are finished, the performance of the model update is observed when the next call from the user arrives.
6.54.4 Post-conditions
Calls experience lower latency, better voice clarity, and minimal disruptions.
AI models improve over time based on user experience feedback.
The network efficiently manages resources while maintaining high-quality.
6.54.5 Existing features partly or fully covering the use case functionality
TS 23.288 [114] supports the use case related to QoS and Policy Assistance Analytics, the QoS parameter and user QoE can be analysed and predicted only based on the request from the consumer, e.g. PCF. In addition, the automatically closed loop procedure to make sure that user QoE will be improved based on the analysis is not supported.
The general AI/ML training procedure is supported in TS 23.288 [114]; what is not supported is AI models’ re-training/ improvement based on user experience feedback.
6.54.6 Potential New Requirements needed to support the use case
NOTE: The mention of AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent doesn’t imply or preclude any architecture assumption or solutions.
[PR 6.54.6-1] Subject to user consent and operator policy, the 6G system shall be able to support mechanisms (e.g. AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) in the 6G system to enable real time call quality analytics and dynamic optimizations.
[PR 6.54.6-2] Subject to user consent, and operator policy, the 6G network shall be able to receive and analyse aggregated call quality data and apply enhancements using mechanisms (e.g. AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) in the 6G network.
[PR 6.54.6-3] Subject to user consent and operator policy, the 6G system shall be able to support continuous enhancement of mechanisms (e.g. AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) in 6G network, from user experience feedback, to improve policies.
6.55 Use case on shared embodied AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents
6.55.1 Description
Looking ahead, a business model like the current shared bicycles might emerge. Shared embodied AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents could be placed throughout the city, accessible to people as needed. For instance, individuals could rent an embodied AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent such as humanoid robot, robot dog or AGV to assist with tasks like moving heavy staff, companion, etc. This would not only increase the utilization rate of these intelligent agents but also make the technology more accessible to the public.
Moreover, such a development would bring about new demands for communication services. This kind of shared embodied AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents need to constantly report their status, location, and interact with each other and users, a high - speed, low-latency, and reliable communication network would be essential. 6G network needs to ensure seamless data transfer and real time interaction among these intelligent entities, facilitating their widespread application in our daily lives.
6.55.2 Pre-conditions
Company ShareRobot has a number of embodied AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents and distributes them at some spots of city for people to use. These embodied AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents have their own identification from application level which are assigned by ShareRobot. At the same time, as they need to report their status and location, they are equipped with communication module. These embodied AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents are registered to operator A with identification.
6.55.3 Service Flows
1. Bob has an embodied AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent, AGV robot Sam. Bob usually takes Sam with him while he goes shopping. Sam can help Bob with some heavy goods lifting. Both Bob and Sam are registered with Operator B.
2. One day, Bob is going to pick a mattress from mall, this piece of furniture is too big for Sam to pick. Bob saw the shared embodied AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent from ShareRobot at street side, and decided to use one of them to help Sam.
3. Bob scans the QR code on the shared AGV. On the pop-up page, it requires Bob to sign in with his mobile phone number. After signing in, it appears the attributes this shared AGV has, Bob looks through this and agree to use this one. Then Bob is asked if he agrees to permit access to the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents bonded to this phone number. Bob clicks agree and chooses Sam from the list of AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents bonded to his phone number. Then this shared AGV is activated and will require to be identified by the user’s operator, which is operator B.
4. Then shared AGV will set up connections with Sam, and share their attributes (e.g. related users, sensing capabilities, AI capabilities, service features) with each other. These two AGVs will be assigned to a group and to achieve some tasks together.
5. While all these are set up ready, Bob takes these two AGVs with him and these two AGVs pick up the mattress home.
6. After finishing the task, Bob scans the QR code again and clicks to end the service of this shared AGV.
6.55.4 Post-conditions
Bob took the mattress back home successfully with these two AGVs. The shared AGV is released and go back to the nearest ShareRobot spot. Bob pays for the usage of share AGV based on using time and task (e.g. the weight this AGV is carrying, distance of this AGV is moving, etc.). ShareRobot pays for the communication connection that this AGV used to Operator B based on the data traffic, as well as some service this AGV used if exist (e.g. computing service, sensing service, etc.).
6.55.5 Existing features partly or fully covering the use case functionality
The identification requirements can be partially covered by PINs and CPNs in TS 22.261 [14], clause 6.38. However, AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents have different attributes (e.g. users, capabilities), the 3GPP system is expected to support new identification mechanism to enable the association between AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent and user, as well as the association of the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent's own attribute information, enabling more flexible discovery, communication, and collaboration.
The 5G system shall support mechanisms to identify a PIN, a PIN Element, an eRG and a PRAS.
Subject to regulatory requirements and operator policy, the 5G system shall support an efficient data path within the CPN for intra-CPN communications.
The efficient communication and collaboration can be partially covered by 5G LAN-type service, as defined in TS 22.261 [14]. However, the group is generally defined by subscription and managed by operators or 3rd authorized party. Therefore, it cannot satisfy the dynamic requirement from AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents which require a certain level of flexibility and autonomy.
6.55.6 Potential New Requirements needed to support the use case
[PR 6.55.6-1] Based on regulatory requirements and operators’ policy, 6G network shall support dynamic identification of 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents.
NOTE: Dynamic means the identification of this AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent can be temporary assigned based on task, and this identification can be assigned by different operators based on different tasks.
[PR 6.55.6-2] Based on regulatory requirements, operators’ policy and agreement with 3rd party, 6G network shall support charging for services provided to 3rd party AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents (e.g. combination of communication, computing service, sensing service, etc.).
7 Integrated Sensing and Communication
7.1 General
Integrated Sensing and Communication (ISAC) facilitates new applications and services that require sensing capabilities. The service includes offering wide area multi-dimensional sensing that provides spatial information about unconnected objects as well as connected devices and their movements and surroundings.
Normative service requirements for ISAC have been specified in [6].
7.2 Use case on coordination of search and rescue missions in large disaster areas
7.2.1 Description
In large disaster areas, a high degree of coordination for the search and rescue operations is essential. In such situations, sensing can play an important role in providing helpful information to the Public Protection and Disaster Relief authorities and first responders by providing an integrated platform for real time monitoring and coordination which will support the efficient allocation of resources and the facilitation of decision-making in challenging environments.
Collecting data from a disaster area(s) involves the use of special equipment and devices supporting sensing to capture real time information about the affected area. Also, the base stations in the disaster areas, or temporarily deployable and tactical base stations, can be used for providing the required sensing services.
The collected environmental and devices sensing data can be used to generate real time maps for the affected areas allowing officials to prioritize and to direct the rescue efforts more efficiently. Furthermore, these maps can be used to monitor the evacuation processes, as well as the situations at the evacuation centres (e.g. to detect overcrowding at an evacuation centre).
7.2.2 Pre-conditions
A) After a wide area disaster (i.e. earthquake, fire, tsunami, etc.) there is major widespread destruction.
Base stations can provide sensing services even after a disaster, or new base stations can be installed to ensure the required capacity and/or coverage over areas where the mobile networks are is down.
Rescue teams are equipped to use special equipment and devices supporting sensing.
B) People are expected to gather at evacuation centres (rescue points).
The data on people flow is needed at evacuation centres to distribute disaster relief supplies.
7.2.3 Service Flows
A) Coordination of search and rescue:
- Rescue teams start searching using sensing devices.
- Sensing technology is used to analyse the environment, structure, location of the rescue team members, etc. of the disaster areas and generate real time maps.
- The generated maps will reflect severely affected areas, searched areas, location of the rescue team members, and unsearched areas.
- The rescue command centre can use the real time map information to optimize the dispatch of rescue teams.
B) People evacuate and gather at designated evacuation centres:
- By using sensing technologies, data about people flow can be monitored and provided to rescue teams.
- The rescue teams will provide the necessary relief supplies based on the level of crowding at the evacuation centres and distribute them accordingly.
- In the event of overcrowding at an evacuation centre, additional facilities will be installed (e.g. portable toilets, drinking water points) or a secondary evacuation centre will be set up to and people will be sent there.
7.2.4 Post-conditions
A) The Rescue Command Centre ensures the optimal allocation of resources for more efficient rescue operations.
B) The real time monitoring of crowds helps in providing the required supplies. In the case of overcrowding, appropriate measures can be taken.
C) The real time monitoring of the rescue team members helps the rescue command centre to allocate rescue team members to the most needed areas in a timely manner.
D) Knowing the real time location of the rescue team members, the rescue command centre can warn and direct the team members to avoid coming danger (such as building collapsing) and move to safety in a timely manner.
7.2.5 Existing features partly or fully covering the use case functionality
Sensing requirements for 5G are specified in TS 22.137 [6].
In this use case sensing is considered as an assistance method for search and rescue operations, therefore the performance requirements for service category 1 specified in Table 6.2-1 from [6] are required for detection of human within the target area.
Table 7.2.5-1: Applicable performance requirements for ETWS - Search and Rescue from Table 6.2-1 from [6]
Scenario
Sensing service category
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Sensing service description in a target sensing service area
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Object detection and tracking
1
95
10
10
N/A
N/A
10 [3]
5 [3]
1000
1
5
2
Indoor/outdoor (e.g. detection of human, UAV)
7.2.6 Potential New Requirements needed to support the use case
[PR 7.2.6-1] Based on operator policy, regional and/or national regulations, the 6G network in the area of the disaster shall provide secure mechanisms of collecting the sensing results with a specified level of accuracy that can be used to generate real time maps.
7.3 Use case on safety assistance for vulnerable pedestrians
7.3.1 Description
In a presentation by 5GAA at the 6G Use Case Workshop [7], a positioning accuracy of 1 meter was identified as necessary for vulnerable road users (VRUs). Additionally, their technical report [8] noted that VRUs might carry various devices to enhance pedestrian safety. Global Navigation Satellite System (GNSS)-based positioning alone is insufficient due to issues such as slow convergence, multipath in urban jungles, and susceptibility to jamming or spoofing. Therefore, rapid sensing through communication with roadside base stations and other infrastructure is essential. For instance, if a pedestrian begins moving toward a crosswalk at a red light, it is crucial to quickly detect this movement and send a warning to the UE they carry. This situation may occur even with typical pedestrians; those looking at their smartphones while waiting for the light often mistakenly interpret movements around them as an indication that the signal has changed and start walking. Furthermore, assessing whether a pedestrian can fully cross before the light turns red requires an instantaneous, quantitative understanding of their walking speed. With this data, it becomes possible to determine, based on the road width and remaining green-light time, whether the pedestrian can cross safely.
While speed detection can be achieved through side imaging, using radio waves with high rectilinearity, such as millimetre wave, to detect variations of the propagation time or to measure the Doppler shift creates a robust system that does not demand extensive computing power. Although pedestrians are the primary VRU group, the aging population has led to increasingly varied walking speeds, making it impractical to assume a single, typical walking speed. Real-time measurement in each instance is essential. In the case of wheelchair users. braking can be applied more quickly and reliably without human intervention. However, applying the optimal braking force to prevent forward pitching requires knowledge of the speed before braking, making it particularly important to accurately capture low-speed movements.
7.3.2 Pre-conditions
Good partnership and cooperation are established between the road supervision department and MNO A in City B. At the request of the supervision department, suitable base stations or small cells equipped with directional antennas are selected and installed near crossings. This setup enables MNO A to continuously detect the presence of VRUs who have subscribed to safety assistance services.
7.3.3 Service Flows
1. Bill, who lives in City B, is now 75 years old. Although he remains active, he can no longer conceal the decline in his mobility. At his family's suggestion, he has signed up for the safety assistance service option with MNO A.
2. One summer day, after enjoying a conversation with an old friend at his favourite bar, Bill started walking home. However, his thoughts were so absorbed in replaying the conversation that he attempted to cross the street at a red light without realizing it.
3. At that moment, a warning sound emitted from his mobile phone, stopping him in his tracks. A young person standing next to him, who happened to be looking at their smartphone while waiting for the light, also noticed the warning and helped him stop.
4. Just as Bill was thanking the young person, the light changed to green without him realizing. The young person quickly crossed the street, and Bill started crossing as well, but his phone emitted another warning sound.
5. This second alert made Bill realize that, at his current walking speed, he would not be able to make it across during the current green light. He decided to wait until the light turned green again. As soon as it did, he started crossing, and this time there was no warning sound, allowing him to cross safely.
7.3.4 Post-conditions
Thanks to the capability to detect and evaluate the mobility of vulnerable pedestrians and to alert them via their mobile devices, the safety level of highly vulnerable individuals is enhanced. Network-based sensing provides rapid, accurate, and robust results, which can also support autonomous vehicle driving by detecting and reporting the movement of these pedestrians.
7.3.5 Existing features partly or fully covering the use case functionality
GNSS can estimate a pedestrian's position with an accuracy of approximately several meters while they are walking.
By taking GNSS positioning measurements at multiple locations over several seconds or ten seconds, an approximate speed can be calculated.
Positioning technology using carrier phase for centimetre-level accuracy is currently being discussed for indoor applications
7.3.6 Potential New Requirements needed to support the use case
[PR 7.3.6-1] The 6G system shall be able to support the following KPIs.
Table 7.3.6-1: KPIs for safety assistance for vulnerable pedestrians
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency [ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Safety Assistance for vulnerable pedestrians
Outdoor (Crossing)
[95]
[1]
N/A
[0.5]
N/A
[0.2]
[0.5] x N/A
≤ [500]
≤ [0.1]
≤ [5]
≤ [5]
7.4 Use case for high-resolution topographical maps
7.4.1 Description
Higher definition topographical maps can be created using processing techniques to fuse measured data to create higher definition maps for example techniques such as SLAM have been investigated to optimise techniques and processing to generate solutions for varying scenarios [324]. In general SLAM involves layers of higher accuracy mapping data are overlaid and combined with the lower resolution topographic map data such as from NASA’s Space Shuttle Radar Topography Mission or other local aerial surveys using dedicated survey scanners, to create higher definition topographic maps. Ground based scanning systems such as LiDAR or ISAC receivers can capture features often obscured from aerial view and provide very detailed processed radar image data sets for specific target areas to be jointly processed with other detailed survey data and derive a combined survey result. The service provider operating techniques such as SLAM use these combination processing steps to enhance overall map geometry enhancing Digital Elevation Models, Digital terrain Models whilst capture details not observable and with greater resolution than from data obtained through other perspectives such as aerial views.
Through use of such overlay techniques, continuous additional sensor images can also be compared over time, in order to increase the accuracy and reliability of the observed features in the sensed area. Repetitive sensing of the same target area with precisely located sensors over regular longer intervals also enables the identification and tracking of new or changing topographic features to be captured and topographic maps to be updated also in a timely fashion. Combination of processed mobile sensor data sets with highly accurate fixed sensor scan datasets cumulatively reduce errors to enable highly detailed updates to the local topological maps.
Considering 6G mobile and fixed infrastructure sensing this provides advantages in the continuous or regular update of topographical maps, through resource efficiencies using integrated wireless sensing in the communication signals, larger areas of coverage when compared to dedicated survey solutions, more dynamic updates, enhanced localisation accuracy all with better image overlay and referencing through repetitive surveying of referenced sensing target areas.
Some 5G sensing use cases in [9] and the associated functional requirements and performance requirements in [6], capture the use of mobile network sensing measurements for the updating of topographical maps, through the exposure of the sensing results to an AS connected to the mobile network. The authorized third party AS may additionally combine this sensing result information and any associated contextual information with other non-3GPP sensing data e.g. LiDAR, camera data, and/or with other non-3GPP data e.g. localization, synchronisation data, to improve the detail of the AS output and enhancing the output data/product.
Enhancements in 6G mobile network sensing will provide for enhanced sensing performance for example in terms of measurement accuracy, measurement latency, measurement confidence level and measurement efficiency through enhanced wave form design, enhanced integration of 6G mobile network access node connectivity and coordinated resource utilisation across triggered sensing transmitters.
6G network sensing in the RAN will be provided by sensing transmitters and receivers which may be capable of enhanced sensing operation providing greater accuracy than achievable in 5G systems for example.
This use case is distinguished from previous use cases in [9] not only by the 6G network sensing service using more advanced and reliable sensing measurements but also due to sensing transmitters and receivers being able to provide enhanced sensing measurements with higher QoS and conforming to higher KPIs that enable the delivery of higher resolution sensing results to third party AS to provide enhanced or higher-resolution topographical maps.
7.4.2 Pre-conditions
An AS capable of producing topological maps from 6G network sensing result and other received non-3GPP network sensing information is connected to a mobile network.
The 6G network supports the third party AS through the acquisition of sensing measurements and processing of sensing data to produce sensing results from connected 6G capable sensing transmitters and receivers. The 6G sensing function identifies suitable sensing transmitters and receivers capable of supporting the application servers request for high accuracy sensing assistance data. The AS request may include an indication of a required sensing result accuracy in order to ensure production of high-resolution topological maps.
7.4.3 Service Flows
1. An authorized third party AS used for the production of topographical maps, activates a service request for the 6G network to provide a high resolution sensing service, the 6G network determines that through the processing of sensing measurement data of an enhanced accuracy and QoS, in accordance with the required sensing result accuracy, to support the production of high-resolution topological map for a specific area.
2. The 6G mobile network receives the server request and identifies for a specified area the authorised and authenticated 6G mobile network sensing transmitters and receivers capable of providing 6G sensing measurement data to the 6G network sensing service to support the enhanced accuracy and QoS required for the sensing results in accordance with the AS request.
3. The mobile network configures the identified 6G sensing measurement transmitters and receivers for the enhanced sensing measurement and with the required sensing mode e.g. monostatic, bistatic or multistatic, in order to obtain the sensing results in accordance with the requested service enhanced QoS. The 6G sensing results are transferred from the 6G network to the third party AS, for the production of high-resolution maps through combining the 6G sensing results with other sensing data for the specific area to derive HD survey data e.g. used in the production of high resolution topographical map.
7.4.4 Post-conditions
The third party AS processes the 6G mobile network sensing results produced from the selected 6G network sensing transmitters and receivers, to produce high resolution topological map.
In one use of the high-resolution topographical map, the map is forwarded to vehicles capable of high levels of autonomous driving capability e.g. L3-L5 autonomy [10], where higher levels of topological map accuracy are required.
Production of high-resolution topographical maps can be used for many other services e.g. civil engineering, for outdoor pursuits, assist emergency services, road traffic control, hazard prevention, etc.
7.4.5 Existing features partly or fully covering the use case functionality
Combining non-3GPP generated HD sensing measurements with 6G network sensing results e.g. as in [9] use case 5.28, may provide some limited ability to produce topological maps in a connected vehicle supporting V2X. However, the resolution, repeatability and complexity in achieving and maintaining such a solution is unreliable. In particular challenges exist with the reliability and repeatability in configuring, synchronising and processing data from both the 6G network sensing results with sensing results from other disparately connected HD sensors of varying sensing types, in order to produce reliable results whilst minimizing impacts on service quality and latency.
7.4.6 Potential New Requirements needed to support the use case
[PR 7.4.6-1] The 6G system shall be able to support the following KPIs.
Table 7.4.6-1: KPIs for high-resolution topographical maps
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level) (note)
Accuracy of velocity estimate by sensing (for a target confidence level) (note)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
High topology mapping
Outdoor
N/A
0.10
0.10
-
[0.4]
-
50
≤ 0.2
≤10
<1
NOTE: The accuracy KPIs in Table 7.4.6-1 are extrapolated from [323] Table C2 as considered for autonomous navigation and remote driving.
7.5 Use case on low-altitude UAV supervision
7.5.1 Description
With the development of UAV technologies, light and small civilian UAVs have played a great role in aerial photography, agriculture, mapping and other fields. Various commercial UAV applications are now becoming a reality. These UAVs typically operate at low altitudes and may produce a series of safety control problems, e.g. UAV illegal intrusion and UAV collision. Thus, how to realize the low-altitude UAV supervision is important and challenging in 6G.
In some scenarios, these UAVs may follow carefully planned routes that ensure efficient, regulated, and safe operation in designated airspace. To perform tasks like package delivery, surveillance, or environmental monitoring, commercial UAVs operate based on pre-determined flight paths that dictate their altitude, speed, and direction. For instance, a UAV delivering goods will follow a direct route from the dispatch location to the recipient, while a UAV assigned to environmental monitoring will travel from its station to a specific target area for data collection. Route design and optimization are crucial for safe and efficient UAV operations. These flight routes which are approved by UAV service operators, prioritize the shortest flight path, avoid restricted airspace, and ensure safe distances from obstacles such as buildings, trees, or other UAVs. Following a strict route minimizes the risk of accidents and enhances the reliability of UAV services.
Although commercial UAVs are equipped with sensors to assist with real time navigation, these sensors can be affected by environmental conditions like lighting, weather, or geographical obstructions. Such limitations can impair a UAV's ability to accurately determine its position, altitude, or velocity, which may lead to deviations from the approved flight path. Furthermore, for services such as good delivery, where a number of UAVs are involved in a given area, UAV collision might happen due to sensor limitations and lead to safety issues.
The existing UAV tracking technologies, such as ground-based radar systems and dedicated surveillance equipment, provide route monitoring. However, the widespread deployment of these systems faces challenges due to high installation and maintenance costs and limited availability of suitable installation sites.
As illustrated in Figure 7.5.1-1 [9], UEs connected to the 6G RAN entities can be configured to support sensing operations. This configuration enhances sensing coverage, provides additional positioning reference points for sensing measurements, and improves the accuracy and reliability of sensing results. These improvements are due to the higher density of UEs compared to base stations, which increases the likelihood that some UEs are positioned closer to the UAV than the 6G RAN entities (for example, with a UAV located between two 6G RAN entities and a UE located directly beneath the UAV). Additionally, certain UEs may be placed in reflection directions that provide a larger radar cross section (RCS) for the UAV, taking into account the UAV's RCS variations in different incident/reflection angles.
NOTE: The term “6G Radio Access Network (RAN)” does not imply any architectural assumption, e.g. whether 6G RAN is a new or evolved RAN (compared to 5G).
The 6G sensing processing unit can gather sensing data from one or multiple network infrastructures. Upon request, the 6G mobile network operator can provide UAV flight trajectory tracking services to trusted third-party applications, such as UAV service operators, regulatory agencies, Uncrewed Aerial System Traffic Management (UTM) systems, UAV itself, etc.
Figure 7.5.1-1: Low-altitude UAV trajectory tracing by 6G system
Thus, how to realize the low-altitude UAV supervision (e.g. UAV intrusion detection, UAV trajectory tracking) is important and challenging in 6G.
Sensing is an efficient technology for object detection by means of 6G radio signals, e.g. monitoring UAV illegal flying in a specific area. 6G network could provide sensing service by collecting sensing data, transmitting sensing data, processing sensing data, storing sensing data and support sensing result exposure to the third application platform. These sensing data has the following characteristics:
- These data may not necessarily belong to a specific UE while these data may be produced by the relationship between the network and the physical environment.
- These data may be produced by a UE or a base station in order to complete a specific task which needs multi-dimensional cooperation.
- These data may have a relationship with time and space which needs efficient on-demand transmission, storage and collection.
- These data may be collected from non-3GPP sensing sources which needs unified management in the 6G network.
To address these new data characteristics, the 6G network capability of data processing needs to be extended based on the 5G network including multi-source heterogeneous data collection, efficient and guaranteed large-scale data transmission, efficient data processing within the network, and unified data storage to support multi-node cooperative sensing and multi-node information convergence.
In the low-altitude UAV supervision scenario, the 6G network could be used for sensing the UAV intrusion such as a UAV illegally flying in a restricted area including government and company regions. In this scenario, the network security and data security need to be guaranteed due to the privacy issue. Furthermore, the historical data may be used to identify an illegal UAV. In addition to the communication property of the detected UAV itself, the data management in this low-altitude sensing scenario must be operated within the network.
In a word, the 6G network will break through the "pipeline" capability and go towards to a new type of information service network by realizing diversified data collection, transmission, processing, storage and exposure within the core network.
7.5.2 Pre-conditions
In this use case, a UAV Service Operator/UTM provides package delivery services within an area covered by a 6G network.
Network Operator NN provides 6G sensing service for UAV flight assistance service, including illegal UAV intrusion detection, UAV flight trajectory tracing, UAV collision prediction and etc. NN can make use of wireless base station to sense the airspace within their coverage area and report the sensing information (including tracked UAV and the environment around the UAV) to the Uncrewed Aerial System Service Supplier (USS)/UTM.
Company MM uses the USS/UTM to supervise the low-altitude UAVs and manage potential illegal intrusion into the restricted areas. MM has proved its restricted area information to the USS/UTM.
The USS/UMT uses 6G sensing service provided by the 6G Network Operator NN to detect potential UAV illegal intrusion and UAV collision prediction.
The UAV Operator/UTM provides specific details to the 6G Network Operator NN, including the characteristics of the UAV that will be tracked, along with details about the time and location for flight tracing. This information includes regulated flight paths as well as potential areas where the UAV might temporarily deviate from its route.
The 6G Network Operator NN can realize 6G data collection, 6G data transmission, 6G data processing, 6G data storage within the network, and provide sensing results to the USS/UTM/UAV.
7.5.3 Service Flows
For illegal UAV intrusion:
1. Company MM requests 6G sensing service for illegal UAV intrusion detection in the restricted area from the USS/UTM.
2. The USS/UTM transmits the request to the 6G Network Operator NN.
3. The 6G Network Operator NN selects the base stations located in the restricted area to collect and process initial sensing data by collaborative sensing. The selected 6G base station constantly collect sensing data of the location of UAVs near the restricted area and sends the sensing data to the 6G core network with a defined frequency to obtain the sensing result (i.e. the distance between the UAV and the border or motion trail).
NOTE 1: The term “6G core network” does not imply any architectural assumption, e.g. whether 6G core network is a new or evolved core network (compared to 5G).
4. The 6G Network aggregates, forwards and transmits the data generated by the base stations, and processes the data to obtain the sensing results.
5. The 6G Network exposes the sensing result to the USS/UTM. The USS/UTM could trigger to send warning messages to the UAV or intercept the illegal UAV directly based on the sensing results.
For UAV flight trajectory tracing:
1. When the scheduled time for tracking begins, the 6G Network Operator activates the UAV trajectory tracing service within the designated area until the tracking session ends. The UAV Service Operator then launches UAV#1, which takes off from the delivery source and heads toward the destination, following a pre-set flight path.
2. Using radio sensing, a network of 6G base stations and connected devices (UEs) detect UAV#1 and continuously gather data on its position and movement, such as distance, velocity and angle. These metrics, also known as 3GPP sensing data, are sent to a 6G processing unit for real time analysis.
3. During the flight, if UAV#1 leaves the coverage range of one base station and enters a new coverage zone, the 6G system could let the old base station stop radio sensing, and switch to a new base station for sensing UAV#1 until it is out of coverage. This transition is based on the UAV's estimated position and velocity, which the 6G processing unit calculates. The network can automatically adjust the sensing operations at base stations depending on this data or based on a pre-defined time frame. In certain cases, sensing handover may be triggered to maintain continuous coverage. For instance, if the current base station's connection weakens or if another nearby base station can offer better coverage for UAV#1, the system proactively shifts the sensing function to this new station to ensure uninterrupted tracking.
4. The 6G processing unit can aggregate sensing data from multiple sources, including RANs and UEs, to estimate UAV#1's location and velocity. Similar approach can also be applied to UAV#2 - UAV#N, in the case there are multiple UAVs providing service in the area. This real time information is then transmitted to the UAV operator and/or UTM, who monitors the UAV's trajectory.
5. If UAV#1 - UAV#N deviate from prescribed routes, the UAV Service Operator and/or UTM receives alerts, allowing them to take corrective action and redirect the UAVs as necessary.
For UAV collision prediction: While UAV#1 is delivering goods.
The UAV operator requests 6G Network operator ‘NN’ to assist take-off, landing and obstacle avoidance during the UAV’s flight. In addition, the UE on board this UAV also sends its UE’s identification, trajectory information, required alarm information (e.g. risk collision), and requirements for the received sensing service (e.g. sensing accuracy, refreshing rate, etc.) to Network operator ‘NN’.
Based on the request from the UAV, the Network operator ‘NN’ activates appropriate sensing transmitter and/or sensing receiver to transfer real time sensing data to the core network.
NOTE 2: In this use case, base station or UE is acting as sensing transmitter and/or sensing receiver. Other sensing operations (e.g. Monostatic sensing, Bistatic sensing) are not excluded and can be useful.
During the flight, Network Operator ‘NN’ tracks UAV#1 by coordinating the corresponding base stations to receive the updated sensing data along the flight path, and transmits the sensing results to UAV#1 and/or UTM periodically. The sensing result can include the estimated position and velocity of UAV#1, along with the estimated position and velocity of other objects, e.g. other UAVs, building, birds, etc. UAV#1 utilizes the sensing result to assist its flight. The UTM can also modify a UAV’s trajectory if environment change is identified and noticed.
Once a risk collision is detected or predicted, e.g. an unregistered UAV or bird is predicted entering the flight path and may collide with UAV within [300 ms], Network Operator ‘NN’ can further integrate alarm information of risk collision for precaution into the sensing result based on the UE’s request. Network Operator ‘NN’ may also recommend a flight path adjustment to the UTM to help with the UAV’s control decision.
After receiving sensing result with the alarm information from Network Operator ‘NN’, UAV#1 successfully bypasses the potential obstacles and continues its flight plan.
7.5.4 Post-conditions
For illegal UAV intrusion: The illegal UAVs are moved away from the restricted area. Potential privacy risks are avoided. Thanks to the wide-area and constant sensing capability of the 6G base stations, and the efficient data transmission, processing and storage by the 6G core network, the safety supervision of the low-altitude space of Company MM is improved.
NOTE: The term “6G core network” does not imply any architectural assumption, e.g. whether 6G core network is a new or evolved core network (compared to 5G).
For UAV flight trajectory tracing: UAV#1 follows the tracked flight route to deliver the package to its destination; any off-route movements are detected.
For UAV collision prediction: UAV#1 flies efficiently to its destination.
7.5.5 Existing features partly or fully covering the use case functionality
In TS 22.137 [6], there are requirements specified for 5G system on integration of sensing and communication.
Based on operator’s policies, operator’s control and regulation, the 5G system shall be able to collect 3GPP sensing data from sensing receivers for processing.
Subject to user consent, regulation, and operator’s policy, the 5G system should support the joint processing of the 3GPP sensing data and non-3GPP sensing data to derive a combined sensing result.
Subject to operator’s policy, the 5G network shall provide secure means for a trusted third-party to request 5G wireless sensing service based on specific parameters (e.g., refresh rate, period of time, sensing KPIs, geographical location) and to receive the corresponding sensing results.
However, the existing requirements don’t support the delivery of sensing results to an authorized UE as sensing service consumer.
7.5.6 Potential New Requirements needed to support the use case
[PR 7.5.6-1] Subject to operator policy, the 6G network shall enable the base station to send sensing measurement data to the core network, and enable the core network to aggregate, collect, process, and store sensing measurement data from base stations.
[PR 7.5.6-2] The 6G system should support energy-efficient sensing operations.
[PR 7.5.6-3] The 6G system should provide mechanisms to ensure sensing service is able to be provided with a given sensing system capacity.
NOTE: The term 'sensing system capacity' is the maximum number of targets that can be detected per unit area given sensing QoS requirements per target, which include localization accuracy and sensing service latency [11].
[PR 7.5.6-4] Subject to operator’s policy, regulation and user consent, the 6G network shall be able to provide sensing results to a UE for a specific service, where the UE is authorized by mobile network operator providing sensing service.
[PR 7.5.6-5] The 6G system shall be able to provide sensing with following KPIs [9] [11]:
Table 7.5.6-1: Performance requirements of sensing results for low-altitude UAV supervision
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
(Hz)
Missed detection
[%]
False alarm
[%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s]
Low-altitude UAV supervision
UAV intrusion detection
Outdoor
≥95
≤10
≤10
N/A
N/A
[10]
[≥5]
[≤1000]
[≤1]
[≤5]
[≤5]
UAV trajectory tracking
Outdoor
≥90
1-2
1-2
3-5
3-5
N/A
N/A
100~1000
≤1
≤5
≤5
7.6 Use case on environment object reconstruction
7.6.1 Description
Environmental object reconstruction offers significant potential with great societal and business impact, representing opportunities across wide range of sectors, from smart factories, homes, and transportation to components of/entire smart cities and countries. The 3GPP ISAC provides a non-invasive dual-functionality of communication and sensing data collection of environmental features for reconstruction. This sensing data collection is expected to unlock new services and support enhanced performance, deployment utilization, energy, and spectral efficiencies due to the nature of ISAC and its wide-area coverage and radio-signal availability. For static environmental objects, such as outdoor building/city construction and indoor machinery/wall, 3GPP wireless sensing is effective to provide a wealth of low-cost environmental information for both wide-areas and certain areas. Additionally, for monitoring dynamic targets of interest, such as vehicles, this is essential for enhancing environmental perception, especially in scenarios where non-line-of-sight (NLOS) conditions or poor visibility may limit traditional sensing methods.
The use case of environmental object reconstruction in 3GPP demands finer characterization of surrounding targeted objects based on wireless sensing signal to facilitate industrial innovation. Some applications include:
- Smart Transportation: The impact of autonomous driving is expected to be significant in terms of safety and comfort, and potentially high efficiencies with respect to traffic, logistics, energy, etc. Such application requires advanced knowledge of moving target detection and its trajectory with detection-to-track association [12], within a multi-object tracking context, including perception of micro-features of the surrounding objects such as micro-Doppler effects, precise classification, and accurate dimensions/orientations, etc. Rough localization of these objects is insufficient for ensuring transport safety and public confidence. Therefore, environmental object reconstruction by 3GPP wireless sensing provides assistance for interpreting the complex traffic scenarios and making rapid decisions.
- Smart City: a smart city can demand survey/digital twinning for a component/system of an entire city, which has motivated a number of smart city initiatives [13] to capture the dynamic nature of society. Initially based on cameras, these digital twin models can be greatly enhanced by environmental object reconstruction enabled by 3GPP wireless sensing.
- Smart Home: there are increasing interests for innovative applications of smart home, e.g. for fall detection, provided with better sensing privacy protection and reliability. Environmental object reconstruction at home is expected for finer sensing information of home objects in order to improve service stability for better consumer experience.
Typical environmental objects to be reconstructed can include the following:
Table 7.6.1-1: Representative dimensions of an environment object
Object Type
Dimensions
Building
Approximately ~400 m x ~200 m x ~20 m (L x W x H), static
Vehicle
Truck: 13 m x 2.6 m x 3 m (L x W x H), up to 120 km/h
Passenger vehicle: 5 m x 2 m x 1.6 m (L x W x H), up to 120 km/h
The 3GPP ISAC is expected to offer more detailed characteristics of an environmental object in the spatial domain, as described in Figure 7.6.1-1. The sensing target in the corresponding KPI table refers to a [segment/part] of the target object to be detected and/or tracked, whereas the size of each [segment/part] is comparable to corresponding spatial resolution. The percentage of missed detection/false alarm represent missed/falsely reconstruction of [segments/parts] of the object statistically. In summary, the closeness of environmental object reconstruction is categorized jointly in the sensing result by the estimation of positioning/velocity accuracy, missed detection and false alarm over [segments/parts] of a target object.
Figure 7.6.1-1: Segments/parts of a target object to be detected and/or tracked
Moreover, aggregating environment reconstruction results for surrounding environment objects shall be also supported by 3GPP ISAC in order to enable environmental digital twin (EDT). An EDT is enabled by 6GS after aggregation from diverse sensing data sources (e.g. 3GPP and/or non-3GPP sensing data), with variable sensing quality (e.g. low or high sensing KPIs) at time/spatial domains. Based on locally obtained 3GPP sensing data at UE, this UE can derive the characteristics of selective segments/parts, as described in Figure 7.6.1-2. Then the 6G core network collects and aggregates the information of individual environmental objects derived by one or multiple authorized UEs.
In addition, the 6GS shall also support the predicted reconstruction to ensure consistent and high quality of sensing services, when sensing result may be restricted spatially and/or temporally. For example, some segments/parts of objects may be missed at given locations or periods of time but they can be reconstructed back/restored sufficiently in real time, e.g. based on trained AI/ML model(s), as described in Figure 7.6.1-2. The 6G network will determine where prediction of reconstructions may be needed and adequate, and which/how UE(s) may be participated with deployed AI/ML models. The performance of reconstructed EDT is ensured by the 6G network eventually considering the capability of AI/ML models, processing delay, etc.
Figure 7.6.1-2: Prediction and aggregation of spatial and/or temporal segments/parts of a target object based on AI/ML model(s)
7.6.2 Pre-conditions
Map Provider A is a third-party service provider which can render and virtualize detected/tracked surrounding environmental objects within its application, offer real time 3D virtualization of objects (e.g. via 3D glasses), and display alerts for object-related warnings and information. Examples of alerts include "a car is approaching from the left street corner in 3 seconds".
Good partnership and cooperation are established between Map Provider A and MNO B in City C. Requested by Map Provider A for sensing service, suitable sensing transmitter and/or sensing receiver deployed in City #C are selected by MNO B to constantly sense environmental objects of City #C including buildings and vehicles. The sensing signal emitted from a sensing transmitter arrives at an environmental object whose micro-objects will reflect/diffract the signal to be detected by selected sensing receivers. MNO B constructs and updates the EDT based on the sensing data.
NOTE: For the ease of elaboration, base station or UE is acting as sensing transmitter and/or sensing receiver. Other sensing modes are not excluded and can be useful for environmental object reconstruction.
Charlie is a subscriber of MNO B and also a subscriber of Map Provider A for real time 3D virtualisation of his surrounding objects. Charlie would like to navigate unfamiliar city streets while wearing 3D glasses, which display surrounding objects in real time, for his interest and safety.
7.6.3 Service Flows
Figure 7.6.3-1: Notional environmental changes over time
1. Charlie is a touris, who is driving a car to enjoy the view around the City #C. He would like to navigate unfamiliar city streets while wearing 3D glasses, which display surrounding objects in real time, for her interest and safety. Map Provider A initiates a sensing request to MNO B for the latest information of environment reconstruction in the vicinity of Charlie, who is the subscriber of both Map Provider A and MNO B. The sensing objects, required by Map Provider A, include vehicles, city architecture, etc., around Charlie up to a certain range.
2. MNO B selects and configures the sensing transmitters and sensing receivers (e.g. UEs or base stations), and any applicable non-3GPP sensors, to facilitate the sensing operations. Both 3GPP and non-3GPP sensing data will be collected, aggregated, and processed by MNO B's network in accordance with environment reconstruction requirements. For example, based on locally obtained 3GPP sensing data at UE, this UE can derive the characteristics of selective segments/parts. Then the 6G core network collects and aggregates the information of individual environmental object derived by one or multiple authorized UEs.
3. Based on environment reconstruction requirements, MNO B is expected to perform further sensing operations dedicated to each individual object, to provide fine characteristics, in order to determine object type/dimension, monitor micro-motions like car turning, etc. Corresponding sensing operations may require cooperative and dedicated sensing resource allocations across the 3GPP system for Charlie. Thereafter sensing results of each object are delivered to Map Provider A. If some segments/parts of objects are missing at given locations or periods of time, they can be effectively predicted in real time, e.g. based on trained AI/ML model(s). For example, if the characteristics of individual environmental object derived by the authorized UE or core network are insufficient to meet the expected performance, due to the missing spatial and/or temporal data, the missing segments of the target object can be predicted. This can be achieved by the AI/ML models deployed either at the UE or within the core network.
4. The sensing results generated by MNO B, with more detailed characteristics of environment objects, are exposed to Map Provider A. This data enables Map Provider A to update Charlie's localized map, reflecting changes such as ongoing construction of the university campus, shapes/dimensions of targets, and real time tracking of movement direction/speed etc., as described in Figure 7.6.3-1.
5. Charlie receives a real time hazard display (e.g. a car is approaching from the left street corner in 3 seconds) from Map Provider A virtualized in his glass, tailored to Charlie's trajectory. This information is transmitted through MNO B's network, adhering to pre-defined latency requirements.
6. Luckily Charlie has a chance to visit City# C again. MNO B keeps detecting and tracking ongoing renovation by sensing for local university campus changes including new classroom buildings. During his visit, such renovation is completed roughly. By partnering with MNO B, Map Provider A can integrate and reconstruct the buildings into the real time 3D map. Charlie decides to pay a visit because of his interest and unique building design.
7.6.4 Post-conditions
User Charlie can safely travel in a new city with enjoyable information associated with his surroundings and necessary travel advice.
7.6.5 Existing features partly or fully covering the use case functionality
TR 22.837 [9] has described use cases to monitor micro doppler effect by ISAC caused by chest rise/fall during sleeping. The sensing results represent the human respiration rate.
In this use case, 3GPP ISAC is expected to detect and track more comprehensive characteristics of individual environmental objects, e.g. for a building, vehicle, robot, etc., with sufficient and accurate sensing information per object type.
7.6.6 Potential New Requirements needed to support the use case
[PR 7.6.6-1] The 6G system shall be able to provide sensing service by detecting and tracking the characteristics of individual environmental objects as described in the following Table 7.6.6-1.
Table 7.6.6-1: KPIs for environment object reconstruction
Scenario
Object Type
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Spatial resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Environment object reconstruction
Building (note 1)
95
[0.5-5]
(note 2)
[0.5-5]
(note 2)
N/A
N/A
[0.5]
N/A
[10,000-600,000]
[10-60]
[1-5]
[1-5]
Vehicle (note 1)
95
0.5
0.5
1.5
N/A
[0.5]
[0.5]
100
0.1
5
5
NOTE 1: a sensing target in above KPI refers to a [segment/part] of the target object to be detected and/or tracked, whereas the size of each part is comparable to corresponding spatial resolution of object reconstruction. The percentage of missed detection/false alarm represent missed/falsely reconstruction of parts of the object statistically.
NOTE 2: Considering a variety of dimensions, shapes, and functionalities of urban buildings, a range of KPI values are needed to measure and provide the flexibility of reconstruction accuracy.
[PR 7.6.6-2] Subject to regulation and operator policy, the 6G system shall provide a mechanism to predict characteristics of the environment and/or objects (e.g. shape and size).
7.7 Use case on road digitalization
7.7.1 Description
Many transportation and urban applications require real time and citywide traffic flow estimation, which is the basis for transportation planning and traffic control. Estimated traffic flow is generally represented by the number of cyclists/vehicles/pedestrians passing a reference location per unit of time and can be virtualized by a third-party application. Figure 7.7.1-1 is an example of a sensing area.
In order to enable real time navigation for automatic driving or traffic flow monitoring, it is necessary to define the upper limit of sensing object detection/tracking required by a 3GPP sensing service, since any 3GPP based sensing detection/tracking coexists with communication services and is not cost-free for operators by sharing. 3GPP system shall allocate limited but sufficient spectrum resources, power, base stations and/or road side units and etc. for sensing operations. Such kind of necessity can be represented by sensing target density.
For example, the number of vehicles that need to be simultaneously detected and tracked at a crossroad may be up to 1000 cars per [km2], including all stationary and moving vehicles occupying that crossroad temporally.
The assumptions of this use case are described in Table 7.7.1-1.
Figure 7.7.1-1: Sensing area
Table 7.7.1-1: Assumptions
Sensing target
Size
(Length x Width x Height)
Velocity
Distance between vehicles[96]
Service area
(Note 1)
Vehicle
5 m x 2 m x 1.6 m
Up to 5km/h(static/moving)
3 m
Crossroad
(note 1)
Up to 140 km/h
78 m
Highway
(note 2)
NOTE 1: One crossroad area is approximated by 1000 m dual three-lane carriageway (24 m), by assuming 250 m per direction at the crossroad.
NOTE 2: Based on an assumption of dual three-lane carriageway (1000 m x 24 m).
7.7.2 Pre-conditions
Good partnership and cooperation are established between Traffic Department A and MNO B. Traffic Department A subscribes to the 3GPP wireless sensing service from MNO B for the real time road digitalization.
In order to monitor traffic volume constantly, MNO B has deployed and activated base stations and road side units around traffic intensive areas, such as crossroads, urban roads and highways, in order to provide a wide sensing coverage and capability to detect and track all moving objects, including vehicles, bicycles and pedestrians effectively.
NOTE: For the ease of elaboration, the base station is acting as sensing transmitter and/or sensing receiver in this case. Other sensing modes can also be feasible and useful as well.
7.7.3 Service Flows
Figure 7.7.3-1: Target density at crossroad
Figure 7.7.3-2: Target density at highway
1. Traffic Department A receives a lot of complaints from local residents about heavy traffic at crossroads and highways due to ongoing city construction or bad route planning.
2. Traffic Department A requests MNO B to detect and track at least [100] sensing objects simultaneously including cyclists, pedestrians and vehicles at crossroads shown in Figure 7.7.3-1, and at least [50] sensing objects simultaneously including high-speed vehicles at overpasses shown in Figure 7.7.3-2. According to preferred sensing target density, base stations and road side units deployed by MNO B along these areas constantly monitor the road situation by appropriate sensing operations according to these requests.
3. MNO B collects 3GPP sensing data from base stations and road side units, and non-3GPP sensing data from sensors (e.g. radar, cameras) if available. Then MNO B provides Traffic Department A with real time traffic flow information, including vehicle volume of different directions within a unit time (e.g. every 10 minutes, every half hour), average queuing time for a vehicle/pedestrian as the crossroad, etc.
4. For more demanding traffic monitoring, Traffic Department A requests MNO B to detect and track more objects simultaneously at given crossroads and overpasses during rush hours. MNO B adapts to the request by updating the configuration parameters of sensing operations.
5. Based on sensing results exposed by MNO B for road digitalization, Traffic Department A figures out solutions to improve the utilization of public roads at real time by finer spatial/temporal traffic control and traffic divergence.
7.7.4 Post-conditions
Thanks to the network-wide coverage, the bird's-eye-view of the base station, road digitalization is enabled by capturing real time information of the road environment.
7.7.5 Existing features partly or fully covering the use cast functionality
TS 22.137 [6] has described object detection and tracking by a number of sensing service categories. However, there is essential KPI missing from each category at given sensing accuracy and service latency. For example, Category 3 does not elaborate how many vehicles from a sensing service area would like to be detected and/or tracked by 6GS. On the other hand, sensing, data communication and other 6G services enabled for the same service area shall share limited 6G spectrum, power, and entities effectively. Therefore, this use case is proposed to clarify preferred sensing target densities, from sensing service perspective, for object detection and tracking within a few typical urban transport scenarios.
In summary, the capability to provide suitable mechanisms for a trusted third-party to request the sensing target density is not covered.
Table 7.7.5-1: Existing performance requirements for 5G Wireless sensing as defined in TS 22.137 [6]
Scenario
Sensing service category
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Sensing service description in a target sensing service area
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Object detection and tracking
3
95
1
1
1 [3], [4]
1
1 [3], [4]
1 x 1 [3]
100 [2], or 1000 (note); 5000 for detection in highway
0.05 to 1
2
2
Indoor/outdoor (e.g. detection and tracking of human, animal, UAV)
NOTE: To realize 1 m granularity tracking, when the velocity resolution is 1 m/s, the maximum corresponding sensing service latency is 1 s.
7.7.6 Potential New Requirements needed to support the use case
[PR 7.7.6-1] Subject to regulation and operator's policy, 6G system shall efficiently support sensing target density requested by the third party for a given sensing service.
[PR 7.7.6-2] The 6G system shall be able to support the following KPIs:
Table 7.7.6-1: Performance requirements for road digitalization
Scenario
Service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Sensing target density
（note 3）
Sensing target Velocity
Max sensing service latency
[s]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Road digitalization
Outdoor
95
0.5 (vehicle)
1（pedestrian）
N/A
0.5
N/A
0.2
0.2
m/s
≤
750
vehicles
Per
1000 m x 24 m
≤100
pedestrian
per
1000 m x 24 m
(note 1)
Static/moving,
up to 5 km/h
1~5
≤ 0.1
≤ 5
≤ 5
Outdoor
95
3
N/A
3
N/A
1
1 m/s
≤72 vehicles
per
1000 m x 24 m
(note 2)
Moving,
up to 140 km/h
1~5
≤ 0.1
≤ 5
≤ 5
NOTE 1: One crossroad area is approximated by 1000 m dual three-lane carriageway (24 m), by assuming 250 m per direction at the crossroad.
NOTE 2: Based on an assumption of dual three-lane carriageway (1000 m x 24 m).
NOTE 3: The definition of sensing target density is described in clause 3.1 of the present document.
7.8 Use case on intelligence leveraging nearby entities for real time awareness
7.8.1 Description
In our daily living environments, numerous situations arise where a physical entity—such as a device, vehicle, or piece of equipment—needs to be aware of events or changes occurring or anticipated to occur in the vicinity where it will soon be present. For instance, a delivery robot navigating urban streets can benefit from timely information about a traffic jam forming on its route, while an autonomous vehicle entering a parking area could benefit from real time occupancy updates. The quality of such information, including aspects like timeliness, accuracy, and relevance, is often significantly enhanced through collaboration with nearby entities—devices or systems with similar sensing and processing capabilities—that are closer to the event or location of interest. By sharing real time data or situational updates, these nearby entities can help each other access more accurate, immediate information, ultimately improving responses and decision-making processes in dynamic environments.
7.8.2 Pre-conditions
In an indoor (or outdoor) jobsite environment, there occasionally exist certain sensing areas of interest, which are shared by human workers and AMRs working and moving around.
As in Figure 7.8.3-1, AMR A (which is a UE) is able to observe and monitor an area of interest (say zone X) using its 3GPP sensing feature to certain extent; however, the quality of observing and monitoring is not enough due to some reasons: for example,
because the sensing performance is poor (e.g. due to relatively long distance); and/or
because zone X is often not directly observable via sensing service used by some AMRs, such as AMR A, that need to know of the situation at the zone.
AMR B (which is a UE) is closer to zone X, the area of interest for AMR A, and is able to observe and monitor that zone more accurately or reliably.
7.8.3 Service Flows
Figure 7.8.3-1: AMR A (UE) is able to obtain more reliable and accurate information on the situation that is happening and will be happening (predictive information) with the help of another UE (i.e. AMR B) in a timely manner. The nearby node that helps AMR A can be a UE (primary focus) or a base station
AMR A is in motion along its planned trajectory in an outdoor environment.
The trajectory shares an area with human (e.g. pedestrians, human workers) where human workers walk in, stay and walk out.
While the speed of AMR A is limited to speed level A if AMR has limited information on its proximity (e.g. to an extent of tens of meters), AMR is allowed to increase the speed up to level B if AMR is able to obtain live information on its proximity and if the particular area of interest is considered clear. This specific area is inside the area covered/reachable by AMR B that is capable of joint communication and sensing.
[Case 1] AMR B (UE) is aware that certain AMR(s) is approaching and responds by sending the situational information (e.g. humans or objects are present) using 3GPP sensing service so that the AMR(s) can prepare their necessary actions (e.g. reducing speed, re-planning their routes).
[Case 2] AMR B (UE) periodically broadcasts the situational information (e.g. humans or objects are present) using 3GPP sensing service so that the AMR(s) can prepare their necessary actions (e.g. reducing speed, re-planning their routes).
NOTE: The situational information can include: sensing result, processed data from sensing result, or inferred information from sensing result (e.g. indication that “objects” are present, or indication of the level of “crowdedness”). The situational information can be prepared by the AMR itself (e.g. on-device AI/computing) or by an edge server or a group of servers.
AMR A receives the information that AMR B has sent. The received information can include situation information (e.g. what is present, such as a plural of human workers are present) and/or information of environment / surrounding situation of particular sensing areas of nearby UEs (e.g. AMR B) of a UE (AMR A) (e.g. what is happening, or what is expected to happen, such as one robot is approaching to the area of interest and will be within that area in X seconds so that the AMR A can decide what to do, e.g. prepared to stop or prepare to change the route).
AMR A decides what to do (e.g. reduce speed, prepare to stop, re-plan the route e.g. due to “over-crowded blah…”).
AMR B sends the situational information, e.g. the number of humans or objects is increasing or decreasing in the particular area of interest, so that AMR A can manage its maneuver.
Also, this information can be utilized by the 6G network to adjust the network resources needed for stable operation of AMRs. For example, when there are many AMRs using the network resources within a limited area, prediction information can be utilized to use network resources more efficiently.
7.8.4 Post-conditions
AMR A (UE) is able to obtain more accurate information about the scene of interest through collaboration with AMR B (UE), such as understanding what is happening where, what is expected to happen, and how soon.
AMR A is able to plan ahead to reduce speed to a suitable level of deceleration (e.g. to remain balance of physical loads in forklift), to re-plan the travel route in the job site, or to prevent collision.
7.8.5 Existing features partly or fully covering the use case functionality
The following includes some examples:
The 5G system supports the network exposure to an authorized third party (e.g. TS 23.501 [140], TS 23.502 [30], and TS 23.503 [141]).
The 5G system supports the assistance to AI/ML Operations in the Application Layer (e.g. TS 23.501 [140], TS 23.502 [30], and TS 23.503 [141]).
There are sensing related service requirements for 5G system specified in TS 22.137 [6].
There are AI/ML related service requirements for 5G system specified in TS 22.261 [14].
Support of QoS prediction information for intelligent physical systems (such as UE supporting V2X applications, automated mobile robots) in TS 22.186 [66] and TS 22.104 [64].
5G system features that support the network data analytics (predictions or statistics) services as specified in some of stage-2 specifications (e.g. TS 23.228 [142], TS 23.501 [140], TS 23.502 [30], and TS 23.503 [141]).
7.8.6 Potential New Requirements needed to support the use case
[PR 7.8.6-1] Subject to operator’s policy, the 6G system shall provide a means to activate and deactivate exposing sensing results that a UE (AMR) can use for prediction about the environment situation (e.g. presence of multiple human workers) of a particular sensing area of interest at a particular time of interest to nearby UEs (e.g. AMRs), if requested by a trusted third party.
NOTE 1: This requirement is intended to describe multiple UEs (AMRs) collaborating to provide useful information for each other in areas shared by human and AMRs. For example, a UE (AMR) will have ample time to stop or slow down, using the information on presence of multiple human workers in a few seconds.
[PR 7.8.6-2] The 6G system shall be able to provide a means to enable efficient use of sensing resources for stable sensing operation.
[PR 7.8.6-3] The 6G network shall be able to provide a means to ensure a latency upper-bound, requested by trusted third party, when providing sensing results to nearby UEs (e.g. AMRs) about environment situation.
NOTE 2: The latency depends on different types of applications in various verticals, such as factory, mining and on how fast the AMR is moving in the zone of interest.
7.9 Use case on detection of ships on the coast or in rivers
7.9.1 Description
Maritime and inland waterway transportation are the primary means of cargo transport worldwide. Over two-thirds of global international trade volume and approximately 90% of China's import and export freight are transported by sea and river routes.
Ship detection, illustrated in Figure 7.9.1-1, plays an important role in port management, ship traffic, maritime rescue, cargo transportation and so on. There are following important issues on the ship detection:
Size of ship differs a lot. The radar cross section (RCS) of ships differs a lot among different types and sizes of ships [191], which has important effect on the capability of ships to be detected. 6G system shall be able to detect different ships with different RCS value and sizes.
Ships are widely distributed in the river, requiring large sensing coverage area.
In summary, ship detection in nearshore waters and rivers is a crucial scenario. Since base stations are primarily land-based, large sensing coverage is required to ensure effective ship detecting at nearshore areas.
Figure 7.9.1-1: Shipping monitoring
7.9.2 Pre-conditions
Network Operator MM provides “smart ship detection and tracking” service with 6G system.
Network Operator MM has deployed 6G base stations along the river between City A and City B to monitor ships, collaborating with the river management department of government.
Henry subscribes to this smart ship detection and tracking service from Network Operator MM.
7.9.3 Service Flows
1. Henry frequently operates a ship on the river connecting City A and City B to transport goods.
2. The river management department has temporarily established a forbidden zone for ships on the river to protect the ecology of river, because the noise and disturb made by ships would cause severe damage to creatures in the river. The information of the forbidden zone is shared to Network Operator MM.
3. The 6G base stations on the sides of the river, deployed by Network Operator MM, could transmit and receive the sensing signal. The 6G network could generate the sensing results based on the measurements of 6G base stations on the sides of the river.
4. Based on the sensing results, 6G network could sense and track Henry’s ship. Within the sensing service with high accuracy and resolution, 6G network could derive the position and velocity information of Henry’s ship.
5. When Henry is operating his ship near the forbidden zone, an alert is sent by Network Operator MM because he is too close to the forbidden zone. Then Henry notices the alert and steers his ship away from the forbidden zone.
7.9.4 Post-conditions
Henry delivers the goods safely and the ecology of the river is protected. Thanks for the 6G system with ship tracking and ship recognition function, no ship would enter the forbidden zone, and the ecology of the river could be protected.
7.9.5 Existing features partly or fully covering the use case functionality
In TS 22.137 [6], the basic procedure of sensing has been described, including capability reporting, data collection, data transmission, data processing, and capability exposure, listed below:
The 5G system shall be able to provide sensing service to detect, and/or track one or more objects (e.g. UAVs, birds) and the environment around the object(s).
Based on operator’s policies, operator’s control and regulation, the 5G system shall be able to collect 3GPP sensing data from sensing receivers for processing.
Subject to operator’s policy and regulation, the 5G system shall be able to provide secure means for a trusted third-party to receive sensing results with contextual information.
7.9.6 Potential New Requirements needed to support the use case
[PR 7.9.6-1] The 6G system shall be able to provide sensing service with larger coverage as described in the following table:
Table 7.9.6-1: Performance requirements for 6G ship detection and tracking
Scenario
Sensing service category
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Sensing service description in a target sensing service area
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Ship detection and tracking
1
95
≤100, (note 2)
NA
NA
NA
≤100
NA
5000
1
5
5
Detection of ship on the near shore waters and offshore waters
2
95
≤10
(note 2)
NA
NA
NA
≤10
NA
5000
1
5
5
Detection of ship on the harbour entrances, harbour approaches, and port. and coastal waters
3
95
≤2~10
(note 3)
NA
NA
NA
≤2
NA
5000
1
5
5
Detection of ship on the inland waterway (e.g. river, lake)
NOTE 1: The typical size (Length x Width x Height) of ship is 270 m x 30 m x 30 m in both sensing service category#1 and #2, and 100 m x 15 m x 15 m in sensing service category #3, according to reference [91]
NOTE 2: The KPI values for detection of ship on the sensing service category#1 and #2 are referred to [192].
NOTE 3: The KPI values for detection of ship on the sensing service category#3 are referred to [193].
7.10 Use case on advanced modern city transportation system
7.10.1 Description
New modes of transportation such as Electric Vertical Take-Off and Landing (eVTOL) aircrafts and UAVs integrate into our daily life. The eVTOL Aircraft market size is projected to grow from USD 1.2 Billion in 2023 to USD 23.4 Billion by 2030, at a CAGR of 52.0% from 2023 to 2030 [92]. At the same time, the global autonomous vehicle market size is projected to grow from USD 1,921.1 billion in 2023 to USD 13,632.4 billion by 2030, exhibiting a CAGR of 32.3% during the forecast period [93]. It is believed that the advanced modern city transportation system includes the smart traffic system and low-altitude transportation system at least.
For smart traffic system, the number of transportation vehicles will increase significantly in future. These vehicles will become increasingly intelligent and traffic system will cover a larger space from two-dimensional to three-dimensional. So, the ITS in the future will be an important cornerstone for comprehensive automatic driving. That is, ITS can leverage the sensing capabilities provided by the 6G system to obtain the current positioning, velocity and identifier information of specific participants in the traffic system and should also be capable of foreseeing potential knowledge about future traffic conditions.
The 6G system could at least obtain a series of 3GPP sensing data and non-3GPP sensing data to have a view on transportation system, knowing the environmental information and state (such as position and velocity) of each vehicle. Futhermore, as illustrated in Figure 7.10.1-1, if the 6G network could predict the evolution of environment, and position and velocity for each vehicle in future, the predicted position and velocity information could be transferred to a driver to avoid an accident. For example, if the predicted position and velocity information of an approaching truck could be told to one car who has a large blind area due to occlusion of buildings, the car could proactively slow down before seeing the approaching truck to avoid accidents.
In conclusion, the prediction in 6G system is requested by a service consumer and derived by 3GPP sensing data or 3GPP sensing data and non-3GPP sensing data in history, describing the state in future of sensing target(s) and/or the environment object(s), such as position and velocity in future.
Figure 7.10.1-1: Predicted future state of vehicles
7.10.2 Pre-conditions
In City A, Network Operator MM has deployed base stations, as an important part of ITS, which could utilize sensing results and prediction information to manage traffic. The deployed bases stations could transmit and receive sensing signals to generate 3GPP sensing data.
Alex is an ordinary citizen of City A, and he subscribes to the sensing service from the 6G system. His car also accesses the ITS and could receive management information from the ITS.
7.10.3 Service Flows
1. Alex drives his car on the road of City A. He subscribes to the road sensing service from the 6G system, requiring tracking the position of his car, and monitoring the environment ahead of his car.
2. When Alex begins to drive his car on the road, 6G system starts two joint sensing operations for Alex. The sensing operation#1 is assigned to base station M to track the position of Alex’s car, with performance requirements for tracking. The sensing operation#2 is assigned to the UE on Alex’s car to monitor the environment in front of the car, with performance requirements for environment monitoring.
3. Based on the 3GPP sensing data provided by base station M working for sensing operation#1, 6G network generates sensing results including the position and velocity information of Alex’s car at the current time point.
4. Based on the 3GPP sensing data provided by UE on Alex’s car working for sensing operation#2, 6G network could keep generating the sensing results about state of the other vehicles on the front of his car.
5. The 6G system merges the sensing data from sensing operation#1 and sensing operation#2 together. With the sensing data from operation#2, the 6G system would be able to generate the prediction information including the position and velocity information of Alex’s car for the next 10 seconds, by analyzing the potential actions of Alex facing the current environment given the current state of Alex’s car.
6. Subject to regulatory requirements, operator’s policy and user’s consent, the 6G network transfers the prediction including the predicted position of Alex’s car at the next 10s to the ITS. The ITS could tell Alex to slow down otherwise he would have high probability of colliding with an approaching truck located at blind area of Alex.
7.10.4 Post-conditions
Thanks to the ITS and the 6G system, traffic flows smoothly through an intersection used to be congested. Alex enjoys a safe driving experience.
7.10.5 Existing features partly or fully covering the use case functionality
In TS 22.137 [6], The basic procedure of sensing has been described, including capability reporting, data collection, data transmission, data processing, and capability exposure, listed below:
The 5G system shall be able to provide sensing service to detect, and/or track one or more objects (e.g. UAVs, birds) and the environment around the object(s).
Based on operator’s policies, operator’s control and regulation, the 5G system shall be able to collect 3GPP sensing data from sensing receivers for processing.
Subject to operator’s policy and regulation, the 5G system shall be able to provide secure means for a trusted third-party to receive sensing results with contextual information.
Sensing result: processed 3GPP sensing data requested by a service consumer.
Accuracy of positioning estimate describes the closeness of the measured sensing result (i.e. position) of the target object to its true position value. It can be further derived into a horizontal sensing accuracy – referring to the sensing result error in a 2D reference or horizontal plane, and into a vertical sensing accuracy – referring to the sensing result error on the vertical axis or altitude.
Accuracy of velocity estimate describes the closeness of the measured sensing result (i.e. velocity) of the target object to its true velocity.
The definition of sensing result in TS 22.137 [6] is processed 3GPP sensing data requested by a service consumer, and sensing result related definition of accuracy of positioning estimate is the closeness of the measured sensing result (i.e. position) of the target object to its true position value. The sensing result related definition of accuracy of velocity estimate is velocity the closeness of the measured sensing result (i.e. velocity) of the target object to its true velocity. The word of “measured” and “true position/velocity value” means that the sensing result in TS 22.137 [6] describes state in history which has happened, not future.
As far as whether the user consent is needed for provided PR 7.9.6.1, the related PR could be found in TS 22.137 [6] as follows:
Subject to user consent, regulation, and operator’s policy, the 5G system should support the joint processing of the 3GPP sensing data and non-3GPP sensing data to derive a combined sensing result.
Based on operator’s policies, operator’s control and regulation, the 5G system shall be able to collect 3GPP sensing data from sensing receivers for processing.
The PRs defined in TS 22.137 [6] is the consolidation results merged with multiple PRs in TR 22.837 [9]. In the discussion of 5G sensing, if the non-3GPP sensing data is not used to derive sensing results, the user consent is not needed. For example:
[PR 5.2.6-5] The 5G system shall be able to support means to enable the core network to process 3GPP sensing data for obtaining sensing results.
[PR 5.6.6-1] Subject to operator policy, the 5G system shall be able to collect 3GPP sensing data and yield sensing result from the data for detection of outdoor objects.
What’s more, in the use case related to above PRs in TR 22.837 [9], the related sensing targets would be pedestrians, cars, and animals, these sensing data is not related to user self at all. No user consent is needed in such scenario when the sensing data and sensing result are not related/controlled/managed/owned by user.
And if the non-3GPP sensing data is needed, extra user consent would be needed for providing some extra non-3GPP sensing data. The related PRs could be found as follows:
[PR 5.21.6-3] Subject to user consent and regulatory requirements, based on operator policy, the 5G system should be able to support the combination of the 3GPP sensing data and non-3GPP sensing data to derive combined sensing result.
What’s more, in the mentioned use case 5.21 in TR 22.837 [9], the sensing target would be user itself, and home of user. Thus, the user consent is needed because the data privacy of user would be considered.
In this use case, at least for now only 3GPP sensing data is used in the service flow, user would not need to give consent to provide non-3GPP sensing data, and the sensing result mentioned in this use case would be pedestrians/cars showing on the road, which is not managed by user. Thus, in this use case, it is believed that user consent would not be needed to derive prediction information from 3GPP sensing data which is not related to user or non-3GPP sensor, which may need extra user consent to contribute data.
Instead, the privacy of sensing targets should naturally be protected. Thus, the privacy of sensing target would be maintained in PR1. Similar wording is found in TS 22.261 [14] for privacy:
The 5G system shall allow roaming services to be provided by a roaming services provider in charge of managing roaming agreements, by mediating between two or more PLMNs, while maintaining the privacy and 5G security of any information transmitted between the home and the serving PLMN.
Thus, in the PR 7.10.6.1, the wording of “while maintaining the privacy of the sensing target(s)” is also added.
7.10.6 Potential New Requirements needed to support the use case
[PR 7.10.6-1] Subject to operator’s policy and regulation, the 6G network shall be able to provide a sensing service to derive predicted location and/or velocity of sensing target(s), while maintaining the privacy of the sensing target(s).
[PR 7.10.6-2] Subject to user consent, regulation, and operator’s policy, the 6G network shall be able to provide secure means to expose the prediction of location and/or velocity of sensing target(s) to a trusted third-party.
7.11 Use case on stored sensing data handling
7.11.1 Description
Sensing data transfer, such as target detection and tracking, is certainly important within sensing useful scenarios. However, we cannot ignore the stored sensing data transfer scenarios due to the specified service requirements. And there is such scenario that do not have high level requirements for sensing services. For example, in meteorological sensing, weather changes are a relatively slow process and data does not need to be obtained in real time. Another example is for public safety, where a public safety organization may request sensing data for a certain area over a certain period of time, such as traffic flow statistics. Accurate reflection of traffic conditions can be achieved through data accumulation over a period of time.
The 6G network shall support the above mentioned stored sensing data transfer and reuse scenarios. Because in this way, the network can provide more diverse types of sensing services for different needs and meet the specific needs of various fields.
7.11.2 Pre-conditions
Figure 7.11.2-1: Stored sensing data handling
As shown in Figure 7.11.2-1, the third party#1, third party#2 and third party#3 subscribe sensing service from the 6G network operator.
The 6G network operator provides the sensing service in an area by integrating some sensing transmitters and receivers (base stations and sensing terminal UEs).
The sensing transmitters and receivers (base stations and sensing UEs) can support stored sensing data transfer.
7.11.3 Service Flows
Third party#1 is a real time map application, it requests the 6G network to provide sensing data in a certain area for road map reconstruction. Third party#2 is a public traffic management department, and it needs to have traffic flow statistics information. Third party#2 also requests the 6G network to provide sensing data for a certain area over a certain period of time.
Based on the requirements from third party#1, the 6G network starts sensing operation by using a set of sensing transmitters and receivers (base station or UE) in appointed area and the sensing transmitters and receivers start to generate 3GPP sensing data and transport that data to the 6G network so that the 6G network can process sensing data and transfer the sensing result for third party#1 in time. The collected sensing data can be stored in the 6G network for other usage purposes.
Based on the requirements from third party#2, if the required sensing data is already stored in 6G network (e.g. collected when providing service for third party#1), the 6G network can provide the sensing result for third party#2 using the stored sensing data. Otherwise, the 6G network can collect the required sensing data when the network load is not high.
The 6G network starts to collect, process historical sensing data and calculate sensing result for third party#3 and send sensing result to third party#2.
7.11.4 Post-conditions
With support of 6G network, third party#1 can have sensing service in certain area and third party#2 and #3 can have sensing result based on stored sensing data in certain area.
7.11.5 Existing features partly or fully covering the use case functionality
None.
7.11.6 Potential New Requirements needed to support the use case
[PR 7.11.6-1] Subject to regulatory requirements, operator’s policy, and user consent, 6G network shall support the use of stored sensing data to provide a sensing service.
7.12 Use case on improving the credibility of visuals using sensing
7.12.1 Description
AI technology for image and video generation has been improving year by year. In the future, it may become difficult to distinguish between real camera footage and generated content, which could complicate analysis in criminal investigations and other areas. Therefore, it is beneficial to use sensing technology to implement mechanisms for determination of whether the footage/video is real or generated.
This use case proposes to use sensing and AI for verification of the credibility of visuals in the following steps:
A sensing signal is transmitted from the sensing transmitter and received by the sensing receiver after being reflected by an object (assuming that the sensing transmitter and receiver are base stations or UE devices).
The collected data is processed within the core network by using AI, to apply the required privacy measures and to filter out the usable data which can be shared/sent to a trusted 3rd party.
Further data processing about the object's shape and speed is obtained through analysis performed by a third party.
7.12.2 Pre-conditions
- the police are undertaking criminal investigation at an incident location and need to verify some received unofficial footage and video,
- at the incident location, UEs and BSs supporting sensing, served by Network Operator A and has collected sensing data.
7.12.3 Service flows
The police request footage and video verification from the incident location by using the collected sensing data by Network Operator A
The collected sensing data being processed and filtered by Network Operator A, the result is sent to Police (or trusted 3rd party) to analyse the data and confirm the authenticity and the credibility of the footage and video.
Analysed results sent back confirming whether the footage and video are consistent with the actual scene or not.
7.12.4 Post conditions
The credibility of the footage and videos is enhanced, leading to more efficient analysis for the police criminal investigation
7.12.5 Existing features partly or fully covering the use cases functionality
TS 22.137 [6], defines under clause 5.2.4 the following requirements for Security and privacy:
The 5G system shall support encryption, integrity protection, privacy of the 3GPP sensing data, non-3GPP sensing data and sensing results, to protect the data inside the 5G system.
The 5G system shall provide a mechanism to protect identifiable information that can be derived from the 3GPP sensing data from eavesdropping.
These requirements related to privacy protection shall be also applied during sensing data processing and storage.
7.12.6 Potential New Requirements needed to support the use case
[PR 7.12.6-1] Subject to regulation and operator policy, the 6G network shall ensure that only authorised entities are able to access the stored collected sensing data and results.
[PR 7.12.6-2] Subject to regulation, operator policy and user consent, the 6G system shall support mechanisms to protect data privacy during the processing of sensing data.
NOTE: The processing of sensing data is compliance with applicable regulations, and other pre-defined service criteria.
7.13 Use case on enhanced XR user navigation
7.13.1 Description
While immersed in an XR application, a user cannot see the real environment or can only see the area covered by the camera or eyes' field of view. The XR application needs to ensure the user's safety by alerting them to avoid collisions with static and dynamic objects in the surroundings.
Current XR applications use visual detection, based on the device cameras, to estimate a map of the environment (static objects) and dynamically detect approaching objects, people, or animals, alerting the user in case of a possible collision.
The visual detection is limited to the cameras' or eyes' field of view (typically limited to the front area); hence, it would miss dynamic items that, for example, approach the user from the back. Moreover, the cameras' visual detection may have reduced performance in dark or overexposed lighting conditions; reflecting or transparent surfaces may also pose challenges to the visual detection, thus hindering user safety.
Wireless sensing can be used to provide additional detection, complementing the visual detection, and hence providing a more robust operation that is independent of the camera's field of view and lighting conditions.
7.13.2 Pre-conditions
A user is using an XR application, a VR training application in a factory workshop, with the user wearing an immersive Head Mounted Display (HMD) connected to a NPN which provides data connectivity and the Sensing Service.
The user’s surroundings are not physically bounded, hence e.g. AGV in a factory. For this sensing service, it is expected that the sensing area around the user, and the detection should be continuous, highly reliable and low latency. Since the user/UE is the intended consumer of the Sensing Results, processing of the sensing data at the UE would ensure low latency for the sensing results.
7.13.3 Service Flows
First the user enables the XR application, maps the area by using the HMD cameras and starts using the XR application. Then, the application invokes the sensing service to enable RF sensing for detecting moving objects in the user surroundings.
Then, the network operator confirms that this user is authorized for the sensing service, configures the sensing service.
The UE then performs sensing operations, acting as both the sensing transmitter and receiver (i.e. monostatic sensing) or the UE may act as the sensing receiver by receiving sensing signals from surrounding sensing transmitters (i.e. bistatic sensing).
The UE processes the RF sensing data alone or jointly with the non-3GPP sensing data obtained from sensors in the HMD (e.g. visual detection data from cameras, accelerometer data, etc[[SUGGESTION_START]].[[SUGGESTION_END]]). During processing, the UE may leverage the info. provided by the network in deriving the sensing results.
Also, the processing of the RF sensing data (and/or joint processing with non-3GPP sensing data) may leverage AI/ML algorithms for the detection. For example, the AI/ML model training may be performed offline in the network and the appropriate compressed model downloaded to the UE ahead of time. With the sensing data and AI/ML techniques, the sensing results are derived which in this case would be that the objects in the environment around the user are accurately detected even when out of view of the camera.
An AGV approaching the user from the back is detected by the sensing service and notifies the XR application, which alerts the user to take the appropriate safety actions.
7.13.4 Post-conditions
Thanks to the Sensing Service, the user was alerted to a potential hazard not detected by the visual system, resulting in improved safety for the user.
7.13.5 Existing features partly or fully covering the use case functionality
Sensing: Release 19 ISAC requirements relating clauses 5.2.1 to 5.2.5 of TS 22.137 [6] can be leveraged for this service. Examples of existing requirements that apply include:
Based on operator’s policies, operator’s control and regulation, the 5G system shall be able to collect 3GPP sensing data from sensing receivers for processing.
Subject to user consent, regulation, and operator’s policy, the 5G system should support the joint processing of the 3GPP sensing data and non-3GPP sensing data to derive a combined sensing result.
Positioning: The processing of the sensing data, especially in a bistatic setup, may require knowledge of the sensing transmitter and receiver positions. The positioning information can be derived from the 3GPP positioning service with requirements in clause 6.27.2 and high precision positioning in clause 7.3.2 of TS 22.261 [14].
7.13.6 Potential New Requirements needed to support the use case
[PR 7.13.6-2] The 6G system shall support the following KPIs:
Table 7.13.6-1: Performance requirements of sensing results for enhanced XR navigation
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing Resolution
Max sensing service latency
[ms]
Refreshing Rate [s]
Missed Detection [%]
False Alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
10 m2
99.9
(note 2)
≤0.5
(note 1)
0.5
(note 1)
0.5
N/A
0.1- 1
(notes 1, 2)
0.5
(note 1)
≤100
0.1 - 1
(note 2)
1%
1%
NOTE 1: This positioning accuracy and sensing resolution are required for the detection of proximal objects within a safety area of the user
NOTE 2: The KPIs are based on the Hazard Prevention in Industrial Environments for “Prediction of workers’ and machines’ actions” and “Detection of worker’s location” in [94]
7.14 Use case on collaborative robots using digital twinning
7.14.1 Description
NOTE: The use case described in this clause is based on [95].
Over recent years, Digital Twinning became significantly more important in complex scenarios where a digital representation of the physical world, in combination with processes to be automated in that physical world, allows to organise the sheer amount of data. Such capabilities eventually enable closed-loop automations where the DT application takes full control of the actions in the physical world without human interactions. The present use case focuses on such scenario where robots and assembly line modules collaboratively aim to solve a joint task, e.g. finding and transporting an object/person to a different location, investigating an area of interest or delivery of goods, which is conducted in an autonomous and closed-loop manner. While existing autonomous robots have a range of built-in sensors, e.g. video, LiDAR, radar, it is assumed in this use case that sensing based on signals from the 6G system is mainly used to expose sensing results to the DT application for precise mapping of the environment to coordinate movements and actions of all robots on the ground. It is assumed that all robots act as a traditional UE and that they can reach the DT over the 6G network.
Figure 7.14.1-1 illustrates the proposed scenario. A DT application coordinates three robots on the ground, UE1, UE2 and UE3, and receives raw sensing data from their built-in sensors as well as the sensing results from the 6G system. The 6G system obtains the sensing results by performing a range of sensing operations which can be implemented using Monostatic, Bistatic or Multistatic sensing as described in [6]. These modalities of implementing sensing operations are differentiated by the number and relative deployment of the sensing transmitters and sensing receivers, as well as deployment relative to network entities (e.g. UEs, Transmitter Receiver Points (TRPs)). The goal is for the two robots (UE1 and UE2) to lift up a large steel bar from the ground and allow the third robot (UE3) to drill a hole in the wall through existing carved out holes in the steel bar. Upon completion, UE1 pushes bolts into the hole and tightens the bar with nuts. All the described actions are fully coordinated by a DT application with the robots only executing action commands sent by the DT application.
Figure 7.14.1-1: Illustration of the use case on collaborative robots based on digital twinning
In modern flexible factories, the assembly line frequently changes to accommodate the production of various products (e.g. different car parts, different car models). Therefore, it is crucial for the DT application to be aware of these changes. While 6G system-based sensing can help acquire this information, the sensing required for robot control differs from that needed to detect changes in the assembly lines. The former may necessitate a precision sensing processing capable of pinpointing the location of robots and the objects they handle, whereas the latter may require a holistic sensing processing capable of detecting large-scale changes in the assembly line and the factory floor. When distinct KPIs are used in relation to sensing data processing, interactions and synchronization are crucial for achieving flexible applications for factories. For instance, depending on the results of the holistic sensing processing, the DT application may need to update the parameters of the sensing operations and processing.
7.14.2 Pre-conditions
The DT application has been granted access to the exposure service for sensing results from the 6G network.
Furthermore, the following pre-conditions exist:
Each robot acts as a UE and has the appropriate identification to attach to the 6G network
Both TRPs are connected to the core network in the 6G system
All UEs have successfully registered to the network
UE1 and UE3 are attached via TRP1 and UE2 via TRP2
The deployment provides Monostatic, Bistatic and Multistatic sensing operations in all its UE/TRP combinations.
Any non-public information in this use case is removed before sensing results are exposed to the DT application.
7.14.3 Service Flows
The DT application requires a range of sensing results to control the collaborative task accurately. This includes a) the information about the environment where the robots will perform their collaborative task, b) location and orientation of the robots within the environment, and c) the location and orientation of the target object, i.e. the steel bar. To achieve this, it is foreseen that the DT application requests separate sensing services for a), b) and c). The sensing service coordinates and executes the requests and coordinates the UEs and TRPs involved, their sensing processing, sensing mode as well as KPI requirements. The sensing data from UEs and TRPs is used to produce the sensing results expected by the DT application. This includes the identification of objects, their categorisation as well as any high-layer abstraction of sensing data, e.g. 6 Degrees of Freedom (position and orientation).
7.14.4 Post-conditions
Based on the sensing results exposed from the 6G network, the DT application was successfully able to coordinate the robots to lift up the steel bar, drill the holes in the wall and mount the steel bar securely against the wall.
7.14.5 Existing features partly or fully covering the use case functionality
Mechanisms for sensing operation configuration are in scope of ongoing 5GA work, without explicit 5GA service requirements.
7.14.6 Potential New Requirements needed to support the use case
New functional requirements for this use case are:
[PR 7.14.6-1] Subject to operator policies, the 6G Network shall provide mechanisms for configuring a sensing operation with a single or multiple sensing modes from all sensing modes supported (e.g. bistatic, monostatic, multistatic).
NOTE: Mechanisms for sensing operation configuration are in scope of ongoing 5GA work, without explicit 5GA service requirements.
[PR 7.14.6-2] The 6G Network shall provide suitable mechanisms for the exposure of sensing results in a synchronised manner with other types of traffic (e.g. audio, video, haptics) to the sensing service consumer.
New performance requirements for this use case are:
[PR 7.14.6-3] The 6G system shall be able to support the following KPIs:
Table 7.14.6-1: Proposed KPIs for collaborative robots using digital twinning
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing
(for a target confidence level)
Accuracy of velocity estimate by sensing
(for a target confidence level)
Sensing resolution
Max sensing service latency [ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Collaborative Robots
(NOTE
Indoor/Outdoor
[95]
[≤ 0.1]
[≤ 0.1]
N/A
NOTE Related use cases are captured in clause 7.2 of TR 22.837 [9] and clause 6.2 of TS 22.137 [6].
7.15 Use case of infrastructure collapse monitoring
7.15.1 Description
The damage inflicted by infrastructure collapse depending on its location and severity can range from minor disruptions, such as traffic congestion and vehicle damage upon a highway in city or loss of produce, machinery in a farmland in rural area, to more profound repercussions affecting local economies, national defense, and other critical sectors. In severe cases, these disasters can result in significant casualties, posing a substantial threat to both human lives and property safety [194] [195] [196][197].
Infrastructure collapse disasters result in significant loss of life and property both in urban and rural areas every year. Monitoring of infrastructure collapse has become an increasingly critical public safety need. Infrastructure collapse monitoring can be divided into two scenarios: pre-event monitoring and post-event monitoring.
Pre-event monitoring contributes to the mitigation and prevention of geological disasters. However, post-event monitoring is also indispensable, because infrastructure collapse can occur suddenly and without warning and pre-event monitoring may not always detect all impending incidents. As shown in Figure 7.15.1-1, these infrastructure collapse disasters, e.g. ground collapses, could threaten human travel safety and even life safety, without warning on time, especially for a moving vehicle driver with high-speed on the road, or a farmer driving a tractor through dense crops with limited visibility, who needs sufficient time and distance to stop before falling into such a disaster area.
Figure 7.15.1-1: An illustration for infrastructure collapse disasters
However, monitoring of such random, sudden and uncertain infrastructure collapse could be addressed by 6G network, which could provide wide sensing coverage utilizing a large number of existing base stations to monitor these geological hazards. 6G network could detect changes in road conditions, and farmlands such as occurrence of landslides or ground collapses, under different weather conditions in a large area and long time period. When the infrastructure collapse happens, 6G network could derive and expose sensing result to road regulatory authority and/or the map service provider on time to ensure human travel safety and life safety, as shown in Figure 7.15.1-2. For example, an unusually large shift in the ground triggers an alert to the navigation software of moving vehicle, thus the vehicle could slow down to a stop even before the geological change enters the driver’s field of view.
Figure 7.15.1-2: Infrastructure collapse monitoring
7.15.2 Pre-conditions
For urban area road condition monitoring:
In City A, in response to the regulatory body's demand for sensing services, MNO ‘MM’ has carefully selected suitable base stations around or along the road to sense the infrastructure collapse. These base stations not only continually monitor moving objects such as vehicles and pedestrians but also continuously assess the condition of the road. The sensing signals emitted by the base stations reach the road surface and are bounced (reflected) back to the base stations. There are also some cameras linked directly with deployed base stations, and some millimeter wave radars are deployed on the RSU (Road Side Unit) alongside the road.
A third party map provider provides infrastructure collapse monitoring services, and Mary subscribes such service to keep safe while driving.
For rural area farmland monitoring:
In Rural Area B, in response to the regulatory body's demand for sensing services, MNO ‘NN’ has carefully selected suitable base stations within to cover large farmlands to sense the infrastructure collapse. These base stations not only continually monitor moving objects such as tractors, harvesters and farmers but also continuously assess the ground condition of the farmland. The sensing signals emitted by the base stations reach the land and are bounced (reflected) back to the base stations.
A third party map provider provides infrastructure collapse monitoring services, and farmer Jack subscribes to such service to keep safe while driving and operating machinery in farmland.
7.15.3 Service Flows
For urban area road condition monitoring:
1.This Friday after work, Mary drives home to visit her parents who live in the countryside.
2. Mary is driving on the highway, where there are no streetlights along the roadside. Due to limited visibility, Mary is driving cautiously.
3. The 6G network actives cameras during the day, collaborating with millimeter wave radars to monitor infrastructure collapse during the day. Considering that the performance requirements for sensing has been satisfied, 6G network stop base station to transmitting and receiving sensing signal to save energy consuming. In the evening, the 6G network disables the camera and enable base stations to transmit and receive sensing signal to keep monitoring infrastructure collapse.
4. The base station deployed at the roadside detects that ahead on Mary's route, and an emergency of ground collapse has occurred, causing the road to slide down to the bottom of a cliff. Benefiting from the sensing operations of base station along side the roads, the 6G network could derive a sensing result that collapse happens 2000m ahead of Mary.
5. MNO MM exposes the sensing results to the third party map provider. The map providers incorporate the locations of ground collapses into the high-definition dynamic maps and transmit warning messages to vehicles approaching these areas. Mary receives the warning message.
6. After receiving the message, Mary will choose to exit the highway at the nearest exit.
For rural area farmland monitoring:
Jack is driving his tractor in farmland through dense crops to estimate the yield. Due to limited visibility caused by dense crops, Jack is driving cautiously.
The 6G network enables the pre-selected base stations to transmit and receive sensing signal to keep monitoring for infrastructure collapse.
The base station deployed at the farmland detects that ahead on Jack’s route, and an emergency of ground collapse has occurred, causing a sinkhole in the farmland. Benefiting from the sensing operations of base stations within the farmland, the 6G network could derive a sensing result that infrastructure collapse happens 2000m ahead of Jack.
MNO NN exposes the sensing results to the third party map provider. The map providers incorporate the locations of ground collapses into the high-definition dynamic maps and transmit warning messages to vehicles approaching these areas. Jack receives the warning message.
After receiving the message, Jack will choose to change his route for a safe exit out of the disaster area.
7.15.4 Post-conditions
Thanks to infrastructure collapse monitoring, Mary and Jack arrive at their homes safely. Every driver could exit the effected road on time, and no one gets injured. Likewise, every farmer could reroute out of farmland with sinkholes and no casualties incurred.
7.15.5 Existing features partly or fully covering the use case functionality
In TS 22.137 [6] The basic procedure of sensing has been described, including capability reporting, data collection, data transmission, data processing, and capability exposure, listed below:
The 5G system shall be able to provide sensing service to detect, and/or track one or more objects (e.g., UAVs, birds) and the environment around the object(s).
Based on operator’s policies, operator’s control and regulation, the 5G system shall be able to collect 3GPP sensing data from sensing receivers for processing.
Subject to operator’s policy and regulation, the 5G system shall be able to provide secure means for a trusted third-party to receive sensing results with contextual information.
In the TS 22.137 [6], the following requirements related to non-3GPP sensing data is found:
Subject to user consent, regulation, and operator’s policy, the 5G system shall be able to collect non-3GPP sensing data from authorized non-3GPP sensors and securely provide it to 5G network.
Subject to user consent, regulation, and operator’s policy, the 5G system should support the joint processing of the 3GPP sensing data and non-3GPP sensing data to derive a combined sensing result.
These consolidated PRs are derived from following PRs defined in TR 22.837 [9]:
[PR 5.4.6-1] Subject to user consent and national or regional regulatory requirements, based on operator policy, the 5GS shall support a mechanism to receive uplink non-3GPP sensing data from authorized non-3GPP sensors.
NOTE 1: This requirement assumes there is some functionality in the 5GS to discern and interpret the acquired 3GPP and non-3GPP sensing data.
[PR 5.21.6-2] Subject to user consent and regulatory requirements, based on operator policy, the 5G system shall be able to collect non-3GPP sensing data from trusted parties.
[PR 5.21.6-4] Subject to user consent and regulatory requirements, based on operator policy, the 5G system shall be able to expose the combined sensing results to a trusted third-party service provider.
In the above requirement, the user would control whether the non-3GPP sensors are used and whether non-3GPP sensing data collected are used to provide combined sensing results, thus user consent is needed. But in some scenario, only using the non-3GPP sensors is not enough, 6G network would also like to send some configurations (such as zoom in for camera) and authorization to non-3GPP sensors to optimize the sensing results based on combination of 3GPP sensing data and non-3GPP sensing data, when the user consent is given. And the channel for 6G network communicates with non-3GPP sensors would be same as the discussion of PR in TS 22.137 [6]:
Subject to user consent, regulation, and operator’s policy, the 5G system shall be able to collect non-3GPP sensing data from authorized non-3GPP sensors and securely provide it to 5G network.
The configuration and authorization would be sent by same channel of the existing PR. Considering TS 22.137 [6] has already said that the authorized non-3GPP sensors is used, it would be necessary that 6G network would provide the configuration and authorization to utilize non-3GPP sensors better.
7.15.6 Potential New Requirements needed to support the use case
[PR 7.15.6-1] The 6G system needs to be capable of sensing the infrastructure collapse with following KPIs.
Table 7.15.6-1: Performance requirements of sensing results for infrastructure collapse monitoring
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency [ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Infrastructure collapse monitoring
Outdoor (e.g. Detecting sudden collapse on infrastructure such as highway, railway, road, flyover, rural areas, farmland)
[95]
[4]
[N/A]
[N/A]
[N/A]
[4]
[N/A]
TBD
[ 1]
[1~2]
[1~2]
7.16 Use case on multi-sensor fusion based sensing for UAV takeoff and landing
7.16.1 Description
Logistics companies are looking to UAVs to save manpower, costs, and time as well as improving quality and efficiency. So far, UAVs have been mainly used for short distance deliveries. For example, they deliver medicines or blood donations to hospital; they fly samples to the laboratory in the chemical industry; and they fly spare parts to the production line in the automotive industry. In the German city of Bonn, for instance, DHL has been testing the delivery of medicines to a hospital across the Rhine River [198].
UAVs may operate in complex environments such as urban areas, forests, and mountains. These environments pose challenges for traditional navigation and sensing systems. Multi-Sensor Fusion based sensing can overcome these challenges by integrating data from different sensors to provide a more accurate representation of the environment. For example, cameras can provide visual information, Radar and sensing base stations, which have the same sensing process logic, can detect moving targets and its distance. By fusing these data sources, UAVs can better navigate through complex terrains and avoid obstacles during takeoff and landing.
Although latest technological improvements have helped UAVs to be more suitable for these new scenarios, there are still many challenges [199], including:
1. Performance deterioration of satellite based sensors: Tall buildings in the urban area could lead to the urban canyon effect, which creates an NLOS environment where the UAV GNSS positioning accuracy deteriorates greatly [200], especially in take-off and landing phases.
2. Constrained sensing capabilities: Currently, UAVs are typically using sensors like cameras and millimetre wave radar for obstacle avoidance, but these sensors can only sense the targets within a short distance (<60m) and couldn’t be used during night time or in bad weather, which constrains the UAV’s flying speed and service time.
Multi-Sensor Fusion based sensing which calculates the sensing result from the different kinds of sensing data from multi sensors and sensing assistance information can enhance the accuracy and reliability of navigation, which is crucial for UAV safe takeoff and landing. Meanwhile, Multi-Sensor Fusion based sensing can help UAVs detect obstacles, assess the landing area, and adjust their flight path in real time, thereby reducing the risk of collisions and ensuring a smooth landing.
In general, Multi-Sensor Fusion based sensing plays a crucial role in assisting UAV takeoff and landing by enhancing safety, navigating complex environments, and enabling autonomous operations. With the continuous advancement of 6G technology, we can expect to see further improvements in Multi-Sensor Fusion based sensing in 6G systems, leading to more reliable and efficient UAV operations.
7.16.2 Pre-conditions
Figure 7.16.2-1: Multi-Sensor Fusion based sensing for UAV takeoff and landing
As shown in Figure 7.16.2-1, the UAV operator provides a package delivery service in an area which is covered by 6G network. The UAV operator subscribes to the UAV flight takeoff and landing supporting service from the 6G network operator.
The 6G network operator provides sensing service in an area by deploying different sensors (including camera, radar, and sensing base station) around the UAV landing area. And those sensors can report respective sensing data to 6G network.
7.16.3 Service Flows
1. The UAV operator plans to use one UAV to deliver packages, and the UAV operator/UAV requests the 6G network to provide UAV flight takeoff, landing and obstacle avoidance supporting services. In addition, the UE on board this UAV also sends its UE’s identification, trajectory information, required alarm information (e.g. risk collision), and requirements for the received sensing service (e.g. sensing accuracy, refreshing rate, etc.) to the 6G network operator
2. The 6G network operator activates the UAV flight takeoff supporting function and triggers the 3GPP sensing and non-3GPP sensing (directly or via agent) around UAV takeoff area.
3. Different sensors then send the different (3GPP and none-3GPP) sensing data to the 6G network to start sensing data fusion processing.
4. The 6G network calculates the sensing result based on the received sensing data and provides the fusion sensing result to UAV operator/UAV to assist UAV in takeoff phase.
5. Once the UAV takes off successfully, the UAV operator/ UAV triggers the 6G network to stop UAV flight takeoff and landing supporting service.
6. During the flight, once a risk collision is detected or predicted based on the sensing result (e.g. an unregistered UAV or bird is predicted to enter the flight path and may collide with UAV), the 6G network can deliver information of risk collision for precaution. The 6G network may also recommend a flight path adjustment to the UAV operator/UAV to help with the UAV flight.
7. When the UAV flies back above the load area, the UAV operator/UAV triggers the 6G network start UAV flight takeoff and landing supporting service again. The 6G network starts multi-sensor fusion based sensing and provides fusion sensing result to the UAV to support landing.
7.16.4 Post-conditions
UAV delivers the package successfully with the support of 6G network.
7.16.5 Existing features partly or fully covering the use case functionality
SA1 has identified a series of requirements related to non-3GPP sensing data and captured them in TS 22.137 [6].
5.2.1 General
Subject to user consent, regulation, and operator’s policy, the 5G system should support the joint processing of the 3GPP sensing data and non-3GPP sensing data to derive a combined sensing result.
5.2.4 Security and privacy
The 5G system shall support encryption, integrity protection, privacy of the 3GPP sensing data, non-3GPP sensing data and sensing results, to protect the data inside the 5G system.
And there is a non-3GPP sensors related requirement captured in clause 4.2 of TS 22.137 [6].
These non-3GPP sensors could include radar camera or Wi-Fi sensing. While the mechanism of these types of sensing is not considered in this specification.
7.16.6 Potential New Requirements needed to support the use case
[PR 7.16.6-1] Subject to regulatory requirements, operator’s policy, and user consent, the 6G network should support suitable means to collect non-3GPP sensing data from third party.
[PR 7.16.6-2] The 6G system shall be able to provide sensing service by enabling multi-sensor fusion based sensing as described in the following Table 7.16.6-1.
Table 7.16.6-1: Performance requirements for Multi-Sensor Fusion based sensing for UAV takeoff and landing
Scenario
Object Type
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Spatial resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
UAV takeoff and landing
Outdoor
[95]
[0.1]
(note 1)
[0.1]
(note 1)
[1]
[1]
[0.5]
[1]
[100-500]
[0.2]
[5]
[5]
NOTE 1: The KPI values for accuracy of positioning are centimetre-level sourced from reference [199], [200]
NOTE 2: The KPI values of this use case are effective for sensing in specific area for UAV takeoff and landing.
7.17 Use case on enabling non-3GPP wireless sensing
7.17.1 Description
In 5G, sensing allowed ‘combined sensing results’ that used 3GPP and non-3GPP sensing data. At the same time, wireless sensing is a 3GPP service defined on NR-based capabilities:
5G Wireless sensing: 5GS feature providing capabilities to get information about characteristics of the environment and/or objects within the environment (e.g. shape, size, orientation, speed, location, distances or relative motion between objects, etc.) using NR radio frequency signals, which, in some cases, can be extended by information created via previously specified functionalities in EPC and/or E-UTRAN.
The goal of this use case is to support indoor scenarios with the same 6G sensing service as provided by NR-based sensing services, whose requirements are captured in TS 22.137 [6].
This use case considers only non-3GPP wireless sensing, not other non-3GPP sensors (video, audio, LiDAR, etc.).
Just as 802.11 wireless LAN access [201] has proved a valuable way to support 3GPP services in indoor scenarios often with poor 3GPP access coverage, this use case considers use of 802.11bf [201] wireless sensing capabilities as well. 802.11 wireless access coverage is generally present where communication services are often accessed, in homes, businesses, vehicles, schools, etc. As 802.11bf [201] sensing capabilities are adopted and deployed as part of future 802.11 base stations and terminal devices, there will also be an excellent opportunity to extend 3GPP sensing services to these generally indoor environments.
The goal of IEEE Sensing standardization is quite aligned with the aims in 3GPP. It aims to provide a unified framework to support a variety of applications such as presence detection, motion detection and gesture recognition supporting a wide range of verticals e.g. healthcare and smart environment automation. IEEE 802.11bf [201] is in draft standard status currently and has been implemented and deployed. It is compatible with existing 802.11 standards and can operate in different scenarios. One fundamental scenario requires an upgrade of either a single or multiple access points, to support monostatic, bistatic and multistatic sensing scenarios. Support by client-based systems is also possible, which provides improved resolution and better support in environments where there is only a single access point. Work continues in IEEE to improve these capabilities, for greater sensing precision, capacity, leveraging improved channel states, wider frequency bands and support for more antennas.
Work in ETSI ISG ISAC investigates use cases and technical feasibility for ISAC development and standardization that could occur in 6G. ETSI ISG ISAC in [95] identifies 6G use cases, sensing types, channel models, architectures and deployment considerations, KPIs and evaluation assumptions. Most of the work is still ongoing. However, GR ISC 001 [95] identified potential advanced 6G use cases dependent on ISAC and the corresponding technological gains. For each use case, the potential requirements provided are centred on the 6G system. Notably, over 50% of the identified use-cases require the 6GS to receive, process, fuse, and store sensing data from non-6GS entities indicating the crucial role of non-3GPP sensor data to complement existing system capabilities.
Most of the requirements included in TS 22.137 [6] are already satisfied by IEEE 802.11bf [201]. This use case analyzes where there are gaps in support and proposes requirements for the 6G system to fill these gaps.
7.17.2 Pre-conditions
In this use case, it is assumed that access points have been deployed that supports IEEE 802.11bf [201].
NOTE: Scenarios in which terminal equipment (that is also a UE) also support IEEE 802.11bf [201] are not discussed in this use case.
It is also assumed that a network operator supports sensing service functionality, as described in TS 22.137 [6]. These sensing services support specific requirements that can be supported through interworking with IEEE 802.11bf [201] as described below.
The use case is presented in an abstract manner, as it embraces many of the use cases in TR 22.837 [9].
The ‘sensing service consumer’ (SSC) is a third party, a customer of an MNO, that receives sensing services. Sensing results are exposed to the third party.
7.17.3 Service Flows
The SSC identifies a need for sensing in a given area of interest.
The SSC requests this service from the MNO.
The SSC, once authorized by the MNO, provides information concerning what sensing results are needed and other parameters concerning the sensing service.
The MNO controls the suitable WLAN sensing stations to acquire non-3GPP sensing data.
The MNO processes the non-3GPP sensing data to create a sensing result.
The MNO exposes the sensing result to the SSC.
The MNO accounts for the sensing service provided to the SSC in the form of charging data.
7.17.4 Post-conditions
The SSC receives sensing data results. The SSC does not know or care whether these results were acquired using 3GPP or non-3GPP access. The MNO is able to enable 6G wireless sensing service to indoor locations that are served by WLAN.
7.17.5 Existing features partially or fully covering the use case functionality
Table 7.17.5-1: Feature comparison of TS 22.137 [6] and 802.11bf [201]
Ref.
TS 22.137 [6] Requirement
Applicability, IEEE 802.11bf [201] …
5.2.1
The 5G system shall be able to provide sensing service to detect, and/or track one or more objects (e.g. UAVs, birds) and the environment around the object(s).
Provides sensing capabilities to detect the range, velocity and motion of objects of interest, principally for indoor scenarios.
Based on operator’s policies, operator’s control and regulation, the 5G system shall be able to collect 3GPP sensing data from sensing receivers for processing.
Collects sensing data from sensing receivers for processing according to the architecture.
The 5G system shall be able to provide 5G wireless sensing service in a target sensing service area location using sensing transmitters and sensing receivers.
Defines a wireless sensing area of interest to support applications such as presence detection and gesture classification.
Subject to regulation and operator policy, the 5G network shall be able to activate, configure, and deactivate 5G wireless sensing based on parameters such as location and network conditions (e.g. network load).
Configures parameters for timing and both associated and non-associated stations. Sensing measurement procedures exchange these parameters both for trigger-based and non-trigger-based modes.
Subject to operator’s policy, the 5G system may be able to use sensing assistance information to derive the sensing result.
Supports information to be provided to define sensing behaviour, including range, velocity, and motion are based on channel state information, timing and bandwidth.
Subject to user consent, regulation, and operator’s policy, the 5G system shall be able to collect non-3GPP sensing data from authorized non-3GPP sensors and securely provide it to 5G network.
Supports this capability.
Subject to user consent, regulation, and operator’s policy, the 5G system should support the joint processing of the 3GPP sensing data and non-3GPP sensing data to derive a combined sensing result.
Supports the capability of gathering non-3GPP sensing data. This could be combined with 3GPP sensing data to derive a combined result.
The 5G system shall support continuity for 5G wireless sensing service (e.g. for sensing a moving object).
Supports sensing capabilities in a given service area but does not in itself support service continuity. To support service continuity for sensing for a moving object, further support would be needed (by the 6G system) to selectively use specific stations that support IEEE 802.11bf. [201]
Subject to operator’s policy, the 5G System shall be able to provide the 5G wireless sensing service in case of roaming.
Does not itself support roaming. This would have to be supported by the 6G system.
Subject to regulation and operator’s policy, 5G network shall provide prioritization among 5G wireless sensing services as well as prioritizing between communication and sensing services.
Supports prioritization including the point coordination function interframe space (PIFS) mechanism for measurement exchange. This supports different types of traffic using the same MAC resources.
Subject to local regulation, the 5G network shall enable UEs without 5G coverage to use unlicensed spectrum to provide 5G wireless sensing service.
Supports the use of unlicensed spectrum (only).
Subject to regulation, the 5G network shall enable UEs supporting V2X application to perform 5G Wireless sensing when not served by RAN using the allowed ITS spectrum and unlicensed spectrum.
Does not support V2X requirements. The interior of the vehicle could be supported, but this is out of scope of V2X.
5.2.2
Subject to regulation and operator’s policies, the 5G network shall be able to configure and/or authorize or revoke authorization of sensing transmitter(s) and sensing receiver(s) for 5G wireless sensing service.
NOTE 1: Such configuration and authorization can be based on sensing transmitter or sensing receiver location, specific time, sensing duration, sensing accuracy, target sensing geographical area, establishing of communication to transfer sensing data, etc.
NOTE 2: Such configuration and authorization can also include the selection of multiple sensing transmitters/receivers for 5G wireless sensing services.
Does not support authorization of this kind itself. This could be supported by the 6G system.
The 5G network shall be able to provide a mechanism for an MNO to configure UEs supporting V2X applications to support 5G Wireless sensing service when not served by RAN.
Does not support V2X requirements.
Based on location, the 5G network shall be able to ensure that sensing transmitters and sensing receivers use licensed spectrum only in network coverage and under the full control of the operator who provides the coverage.
NOTE 3: The above requirement does not apply for public safety and V2X networks with dedicated spectrum, where 5G wireless sensing can be allowed out of coverage or in partial coverage as well.
Does not support licensed spectrum, so will never use licensed spectrum without operator control.
5.2.3
Subject to operator’s policy, the 5G network shall be able to provide secure means to report sensing result to a trusted third-party requesting information about a target object when specific requested conditions are met.
NOTE 4: These conditions could be e.g. the target object distance from the restricted area border or entering restricted area,.
Supports acquiring sensing data based on conditions (trigger-based sensing.) This includes the area and other characteristics in which sensing data is acquired. IEEE 802.11bf does not support reporting of sensing results to third parties. This could be added by the 6G system.
Subject to operator’s policy, the 5G network shall provide secure means for a trusted third-party to request 5G wireless sensing service based on specific parameters (e.g. refresh rate, period of time, sensing KPIs, geographical location) and to receive the corresponding sensing results.
Supports non-trigger-based sensing for unassociated stations, within a period of time. There is also a protected sensing measurement frame which is encrypted and authenticated only by associated stations. The sensing data can be acquired based on specific parameters including refresh rate, period of time, etc.)
Subject to operator’s policy and regulation, the 5G system shall be able to provide secure means for a trusted third-party to receive sensing results with contextual information.
Does not support reporting of sensing results to third parties. This could be added by the 6G system.
Subject to user’s consent, regulation and operator’s policy, the 5G network may provide secure means to expose to a trusted third-party the combined sensing result derived from the joint processing of the 3GPP sensing data and non-3GPP sensing data.
This requirement in 5G is also supported in 6G, though the implications are expanded by the new definitions.
Subject to operator’s policy, the 5G network may provide secure means for the operator to expose information towards trusted third-party on whether a given sensing service is available and the estimated quality of the given service for a certain geographic area and time.
This requirement in 5G is also supported in 6G, though the implications are expanded by the new definitions since ‘sensing service’ now includes sensing results from non-3GPP sensing data.
Subject to operator’s policy, the 5G network may enable secure means for a trusted third party to provide sensing assistance information.
Does not support exposure of interfaces to third parties. Supports a means to configure sensing capabilities based on parameters. These parameters could be determined by the 6G system, as provided by a third party.
5.2.4
The 5G system shall support encryption, integrity protection, privacy of the 3GPP sensing data, non-3GPP sensing data and sensing results, to protect the data inside the 5G system.
Supports IEEE 802.11 security mechanisms. The 6G system could protect acquired sensing data and sensing results.
The 5G system shall provide a mechanism to protect identifiable information that can be derived from the 3GPP sensing data from eavesdropping.
Does support a mechanism to protect confidentiality of data, including non-3GPP sensing data.
The 5G network shall limit the exposure of the sensing results only to a trusted third-party authorized to receive that sensing results.
Does not support exposing sensing service information to third parties. This could be added by the 6G system.
The 5G system shall support appropriate sensing KPIs of 5G wireless sensing for both situations where consent can be obtained, and where it cannot.
Supports KPIs for sensing in the form of KPI requirements including range coverage, field of view, range resolution, angular resolution, velocity resolution, accuracy, probability of detection, latency, refresh rate and number of simultaneous targets. Does not support consent or obtaining it. The 6G system could support consent aspects.
Subject to regulation and user consent, the 5G network may be able to link sensing results with 3GPP subscriber identity of a UE for a sensing target associated with that UE served by the same network.
NOTE 5: The purpose of this requirement is to ensure that association of 3GPP subscriber identity and sensing results is possible only with user consent and according to regulatory requirements.
Does not support linkage of sensing results with 3GPP subscriber identity. The 6G system could support this capability.
5.2.5
The 5G system shall be able to support charging for the 5G wireless sensing service (e.g. considering sensing KPIs, duration).
Does not support charging. This could be added by the 6G system.
The performance characteristics of IEEE 802.11bf [201] differ from the requirements in TS 22.137 [6]. This use case does not suggest that the existing sensing performance requirements change. The performance comparison in Table 7.17.5-1 identifies that certain scenarios in which 3GPP wireless sensing service could be used are more or less advantageous using IEEE 802.11bf [201].
The performance capabilities of IEEE 802.11bf [201] in Table 7.17.5-2 below, is compared to performance requirements in TS 22.137 [6]. Though IEEE 802.11bf [201] supports only a subset of the requirements, it is important to note that it supports a significant set of scenarios.
Editor's Note: Performance levels provided in the figure are from papers published at the 'requirements' phase of the IEEE 802.11bf [201] standardization process. The draft standard is now under review, a necessary step before any specification is issued as an IEEE Standard. Performance evaluations are taking place based on the results achieved by standards-compliant implementations of the complete technical specification. It is expected that further detail and adjusted values will become available before the end of 2026.
Table 7.17.5-2: Performance comparison of TS 22.137 [6] and 802.11bf [201]
Scenario
Sensing service category
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Sensing service description in a target sensing service area
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Object detection and tracking
1
95
10
10
N/A
N/A
10 [3]
5 [3]
1000
1
5
2
Indoor/outdoor (e.g. detection of human, UAV)
2
95
2
5
1
N/A
1
1
1000
0.2
0.1 to 5
5
Outdoor (e.g. detection of human, UAV)
3
95
1
1
1 [3], [4]
1
1 [3], [4]
1 x 1 [3]
100 [2], or 1000 (NOTE 3); 5000 for detection in highway
0.05 to 1
2
2
Indoor/outdoor (e.g. detection and tracking of human, animal, UAV)
3A
[FFS]
0.5-2 [328]
0.5-2 [328]
0.1-0.3 [328]
0.1-0.3 [328]
0.5-2 [328]
0.1-0.3 x 0.1-0.3 [328]
[FFS]
minimum sensing interval 0.0005 [329]
[FFS]
[FFS]
Indoor (e.g. detection of human, animal, object, etc.)
4
99 for public safety, otherwise, 95
0.5
0.5
1.5 for pedestrian,
15 for vehicle, otherwise, 0.1
1.5 for pedestrian
0.5
0.5 x 0.5
for factories
100 to 5000
0.1
1
3
Indoor/outdoor (e.g. detection and tracking of human, animal, UAV, AGV, vehicle)
4A
[FFS]
0.2-2 [328]
(NOTE 7)
0.2-2 [328]
(NOTE 7)
0.1-0.3 [328]
0.1-0.3 [328]
0.5-2 [328]
0.1-0.3 x 0.1-0.3 [328]
[FFS]
minimum sensing interval 0.0005 [329]
[FFS]
[FFS]
Indoor (e.g. detection and tracking of human, animal, moving object, etc.)
Environment monitoring
5
95
10
0.2
(NOTE 4)
N/A
N/A
N/A
N/A
60000
60 to 600
0.1 to 5
3
Nature of environments monitored by sensing (e.g. rainfall, flooding monitoring)
Motion monitoring
6
95
N/A
N/A
N/A
N/A
N/A
N/A
60000
60
5
5
Human motions and activities obtained by sensing (NOTE 5)
6A
[FFS]
0.2 [328]
0.2 [328]
0.1 [328]
0.1 [328]
0.03 m (0.5m range)
0.1m (2m range)
1.5-3 m/s x 1.5-3 m/s [328] (NOTE 7)
[FFS]
minimum sensing interval 0.0005 [329]
[FFS]
[FFS]
Human motions and activities obtained by sensing (NOTE 5)
7
95
0.2
0.2
0.1
0.1
0.375
0.3
5 to 50
0.1
5
5
Human hand gestures obtained by sensing (NOTE 6)
7A
[FFS]
<1
<1
0.1 [328]
0.1 [328]
0.03 m (0.5m range)
0.1m (2m range)
1.5-3 m/s x 1.5-3 m/s [328] (NOTE 7)
[FFS]
minimum sensing interval 0.5 ms [329]
[FFS]
[FFS]
Human hand gestures obtained by sensing (NOTE 6)
NOTE 1: For sensing service categories to which UAV, human or vehicle is a sensing target, the typical size (Length x Width x Height) of UAV is 1.6 m x 1.5m x 0.7m, the typical size of human is 0.5m x 0.5m x 1.75 m, and the typical size of vehicle is 7.5 m x 2.5 m x 3.5 m.
NOTE 2: The safe distance between pedestrian/vehicle and power transmission station/line is 0.7m / 0.95 m.
NOTE 3: To realize 1m granularity tracking, when the velocity resolution is 1 m/s, the maximum corresponding sensing service latency is 1 s.
NOTE 4: This value is derived from the water level where people feel difficulty in walking.
NOTE 5: To achieve human motion monitoring, different accuracy KPI is needed to measure different human motions. E.g. respiration rate accuracy (2 times/min) is a KPI used to measure the accuracy of sleep monitoring, sit-up rate accuracy (3 times/min) is a KPI used to measure the accuracy of sports monitoring.
NOTE 6: Category 7 has more stringent requirements (e.g. for KPIs such as positioning accuracy and sensing resolution) compared to other categories and typically requires more radio resources.
NOTE 7: The variation in performance metric depends on the use case - more detailed sensing tasks have stricter requirements.
7.17.6 Potential New Requirements needed to support the use case
NOTE 1: The applicability of this feature is limited to non-3GPP sensing stations that are operated and trusted by the network operator. This is analogous to ‘trusted non-3GPP access.’
The main new implication expressed by this use case is captured by broadening the definitions of the terms 6G wireless sensing beyond 5G wireless sensing, and sensing results, to include non-3GPP sensing data with or without 3GPP sensing data. The implication of this change is that the existing TS 22.137 [6] requirements can be satisfied by other radio access technology than NR-based sensing. The claim is not that all requirements can be satisfied in this way, however, since 802.11bf [201] capabilities have certain limitations (in terms of performance), so they are not suited for some requirements such as use outdoors, for tracking UAVs, etc.
The following requirements fill the gaps identified in clause 7.17.5, where support is lacking in the existing standard. The text of each requirement is almost the same as that in TS 22.137 [6]; the additions and changes are explained in the NOTE which follows. Additional NOTEs are copied as they were present in existing requirements.
[PR 7.17.6-1] Subject to operator’s policy, the 6G System shall be able to provide the 6G wireless sensing service in case of roaming.
NOTE 2: As the term 6G Wireless sensing does not specifically mention 3GPP radio access technology, this requirement applies to a sensing service using 3GPP sensing data and/or non-3GPP sensing data.
[PR 7.17.6-2] Subject to regulation and operator’s policies, the 6G network shall be able to configure and/or authorize or revoke authorization of non-3GPP sensing station(s) for 6G wireless sensing service.
NOTE 3: Such configuration and authorization can be based on non-3GPP sensing station(s) location, specific time, sensing duration, sensing accuracy, target sensing geographical area, establishing of communication to transfer sensing data, etc.
[PR 7.17.6-3] Subject to operator’s policy and regulation, the 6G system shall be able to provide secure means for a trusted third-party to receive sensing results derived from non-3GPP sensing data.
[PR 7.17.6-4] The 6G system shall be able to support charging for the 6G wireless sensing service (e.g. considering sensing KPIs, duration).
Editor’s Note: The above requirements are FFS.
7.18 Use case on safe & economic UAV transport
7.18.1 Description
In rural regions, the scarcity of labour and the limited access to services necessitate the adoption of alternative, efficient transportation methods. UAVs hold significant potential in this regard. However, the current regulatory framework represents a challenge, as obtaining operational permits is both time-consuming and expensive, often requiring preparation of extensive risk mitigation measures. Beyond Visual Line of Sight (BVLOS) refers to the situations in which the human operator of the UAV does not have direct visual contact with the UAV. Consequently, the regulatory aspects become more stringent. Realization of large-scale BVLOS UAV operations depends on effectively addressing risks associated with telemetry and tracking in order to substantially reduce both ground and mid-air collision risks. The ISAC technology shows great promise in enhancing the safety of telemetry and tracking systems, thereby facilitating safer integration of UAVs into the airspace.
Many countries explore new goods transportation methods that are quicker, more sustainable, less reliant on labour to cope with urban road congestion, labour shortages, as well as to reduce pollution. UAVs present a promising solution, yet their economic viability remains a challenge. To foster the growth of the UAV-based transportation sector, a system should be established that supports safe long-haul BVLOS flights with low operational cost and sufficient drop-off points.
The Netherlands faces challenges in its sparsely populated northern regions, where an ageing and less mobile population requires access to essential services such as pharmacies and groceries that are often found at far distant locations. The situation is especially critical in the Wadden islands area in the north of the country with limited ferry services (e.g. only 3 roundtrips per day). There is growing support in the Dutch parliament to use UAVs for the urgent delivery of medicines to these islands.
While UAVs could potentially fly anywhere, widespread approval by the regulatory authority is unlikely in the near future. Hence, the envisioned UAV infrastructure for 2030 (as illustrated in Figure 7.18.1-1) includes parcel drop-off points/ vertiports and 100 m airstrips for fixed-wing UAVs. The fixed-wing UAVs, capable of flight speeds up to 200 km/h (in the direction of the wind) and carry-load capacity up to 150 kg, will make use of small airstrips of 100 m long. Slower moving UAVs (< 70 km/h) with lower load capacity (< 15 kg) aiming at express deliveries (< 60 minutes) will exploit parcel drop-off points/vertiports that are strategically located within communities.
Figure 7.18.1-1: Illustration of future UAV ground infrastructure in the Netherlands
The Dutch airspace, heavily utilized by crewed aircrafts, does not allow a separate UAV-only airspace. The increase in reported near-miss (<10 m distance) incidents in the Netherlands is concerning as illustrated in Figure 7.18.1-2. In addition to the reported incidents, numerous unauthorized UAV flights were detected, e.g. solely in Amsterdam 23.000 unauthorized UAV flights (predominantly recreational flights) were detected per year that conflict with professional UAV regulations. Therefore, stringent control over airspace for uncrewed traffic is imperative for safety.
Figure 7.18.1-2: Incidents reported on unsafe UAV operations in the Netherlands
Active radars are used to detect and classify UAV (among other things like birds) near restricted or controlled airspaces. However, the range of such systems is limited to 15 km. Beyond these areas, the absence of detection capabilities compromises air safety. Both the UAV operator and the government require reliable verification systems as current telemetry relies on GNSS which is known to be vulnerable to e.g. spoofing. Although this can be mitigated by an operator taking over visual (i.e. first person view) control of the UAV, it affects the economic model which depends on an operator simultaneously managing 15 up to 30 UAVs. Moreover, GNSS failure can result in a group failure of UAVs in a corridor/area. In addition, it is expected that recreational UAV flights will fly unauthorized near professional UAV operations. Although on board systems of professional UAV likely comprise obstacle detection and avoidance sensors, this information needs to be shared with the uncrewed air traffic control to ensure the safety of professional UAV flight paths. While previous generations of mobile networks have support for localization, these are only supported by advanced triangulation of directly involved cells potentially with beamforming. The requested capability goes beyond this, requiring verification of location of the UE also by non-serving cells (sensing). The sensing should facilitate BVLOS flights even in more demanding scenarios e.g. when UAVs fly less than 100 m apart from one another at high speeds.
Currently, the primary navigation system enables UAVs to follow a predefined, approved flight path. Onboard sensors, such as GNSS, ensure accurate navigation, with this data relayed to the UTM system. However, for safety reasons, it is considered necessary to implement an independent secondary system to validate location data a UAV reports to the UTM. The envisioned ISAC system described in this use case may act both as the primary or the secondary system.
Figure 7.18.1-3: Airspace management and the potential role of ISAC
Airspace management can be categorized in three distinct types based on their locations, as illustrated in Figure 7.18.1-3:
In the RED zones, classification of non-compliant objects is essential. This involves classifying any UAV, other flying objects, and birds that are not transmitting their RemoteID or transponder signals. This requirement is crucial in high-risk locations such as airports and city centr[[SUGGESTION_START]]e[[SUGGESTION_END]]s. The system should detect these objects when they move above rooftop level.
In the BLUE zones, detection of non-compliant objects is often enough. This focuses on detecting any UAVs, other flying objects, and birds that are not transmitting their RemoteID or transponder signals, particularly around high-risk locations.
In the PINK zones, detection and validation of compliant UAVs are adequate. This entails detecting and validating UAVs that are transmitting their RemoteID or transponder signals within controlled traffic regions, UAV corridors, or operating BVLOS with a connection to any mobile network. In these areas the ISAC system serves as a secondary/back-up system to ensure safety.
Coverage and market considerations:
The pink zones represent the largest coverage area, necessitating extensive detection and validation capabilities.
High-end detection and classification in the red and dark blue zones are essential for maintaining safety in specific, high-risk locations.
Interoperability between these zones and UAVs enhances overall quality and contextual awareness.
The market targeting solutions for high-end detection and classification is likely more niche compared to that targeting broader/basic detection of unauthorized flights.
In the pink zones, economic factors may dictate that only UAVs can detect other unauthorized UAVs that have intentionally disabled their RemoteID.
Figure 7.18.1-4: UTM-space operation concept
When an operator flies BVLOS, the safety of the operation relies on accurately tracking the UAV's location within the UTM-Space, as shown in Figure 7.18.1-4. If the UAV's location is determined inaccurately, it risks deviating from its intended flight path, potentially causing accidents. The user equipment already has access to GNSS position data, which is relayed to a central data base known as Common Information Services (CIS). The UTM-Space Service Provider is responsible for offering various services to UAV operators as well as updating UAV's flight path/location within the CIS. In areas with low concentration of economic activity a UTM-Space Service Provider may not be economically viable. A simplified UTM structure connecting Ground Control directly to CIS may offer an approach to enable these UAV flights. The Air Navigation Service Provider performs a similar role for crewed traffic. In the Netherlands, this is task is managed by Luchtverkeersleiding Nederland for civil aviation and by Commando Luchtstrijdkrachten for military aviation. The technical requirements of this use case are summarized in Table 7.18.1-1.
Table 7.18.1-1: Technical requirements of safe and economic UAV transport
Category
Requirements
Note
Mobility
< 200 km/h
Fixed wing can achieve 120 km/h with rear wind this increases to 200. Person carrying UAV and Manned traffic are excluded as these are possibly faster.
Flights per day in NL (estimated)
> 200.000
See the note for calculation for 2030
Connection density (km2)
20
GNSS failure can happen in a 1km2 cluster
Location update
< 1 s
To stay within limits of air corridor, height information should be deducted from other sensors
Accuracy
< 50 m X,Y > 99.99% of time
Low accuracy as it not the primary means of navigation. As the secondary system is used for safety the confidence should be very high. If the confidence is not so high, it can result in false positive and unnecessary mission termination.
Synchronization
< 10 ms
Without synchronization no sensor fusion is possible
Third party sensing
Yes
To allow interoperability with other sensors/radar point cloud data
Service availability
99.999%
Similar to core network of operators
NOTE: Euro control expects a 70 fold increase in UAV air fleet compared to commercial crewed fleet - conservatively translating this to NL (only accounting commercial flights operations of 200 per hour) this leads to 200 000 million.
This use case is based on a use case developed in other SDO [202] proposes the use of ISAC as a supporting safety system for widescale (cooperative) small/medium BVLOS UAV operations below 200 m flight ceiling. Independent redundant systems are integral to safety in airspace and are a prerequisite for the large BVLOS UAV operation. ISAC offers high value for this emerging market segment, which is expected to have the largest volume below a 200 m height ceiling with parcels less than 2,5 kg.
Today's available technologies and procedures for UAVs are designed for limited human operated flight, holding back the potential of the BVLOS UAV transport market. Examples include laborious procedures (e.g. flight permit and clearance), insufficiently resilient/secure technologies (e.g. GNSS, Automatic dependent surveillance-broadcast ADS-B systems) and no independent real time oversight of flight data in lower airspace (e.g. onboard UAV sensors).
Despite studying several UAV specific use cases, the requirements identified in TR 22.837 [9] did not address the highly valuable safety role below a flight ceiling of 200 m. More specifically:
The most stringent Cat4 offers a confidence interval of 99 %, with 3 % missed detection and 1% false alarms. These requirements are too low to serve as a backup (secondary) safety system. However, the provided accuracy measures can be relaxed, as airspace separation can be planned > 50 m, and it is a secondary system (the primary is the UAV itself).
Due to its safety critical nature and requirements from air traffic regulators the proposed use case sets higher requirements on Sensing Service Reliability (99.999%) and Confidence Level (99.99%) compared to the ones specified in Table 6.2-1 of TS 22.137 [6]. More stringent requirements increase the economic potential / value of the proposed use case as it enables flight automation and reduced involvement of human operators of UAVs.
The size of the UAV is assumed to be too large to support widescale BVLOS UAVs. Based on this, today's commonly used UAVs with their compact size and smaller reflective surface area would likely remain undetected.
The vast majority of packages are less than 2,5 kg and accordingly more carbon-efficient. Increased weight or larger UAVs impose more regulations/restrictions to reduce ground risk. Consequently, the proposed dimensions of a UAV may only be applicable to a niche market.
The proposed architecture design is deemed too closed, with processing only inside the 5G system. There should also be room for a specialized certified third party to process all raw RF sensing data (and other data sources, including the drone itself to meet integrity and liability requirements.
The TS 22.137 [6] does not relate to the existing UTM space procedures in conjunction with onboard UAV sensors, localization services, and communication to the network. In the lower airspace, assuming cooperative UAVs (of which parts may fail), leveraging the terrestrial network for connectivity and localization as a pre-condition is logical. Besides, today BVLOS UAV flights cannot be conducted in mixed airspace (crewed & uncrewed) or in high risk areas (e.g. above cities) without careful, laborious planning involving significant human involvement. In order to achieve safe and economic UAV transport one human operator per 10-30 UAVs would be required.
7.18.2 Pre-conditions
A UAV is coming towards an airspace.
ISAC nodes comprising sensing transmitters and sensing receivers are deployed at airspace to support airspace management.
The UAV is provisioned to communicate their sensor data set locally, securely, privately and legally with the airspace ISAC system.
ISAC post-processing is optimized to identify the selected subjects & information.
7.18.3 Service Flows
For the case when the GNSS onboard the UAV failed.
Step 1: GNSS fails due to one or more of the following:
high-rise buildings;
poor weather conditions;
GNSS interference of any other kind.
Step 2: Mismatch between sensing data & underperforming GNSS is detected by UAV for 30 s.
Step 3: Sensing data becomes primary data source for telemetry of UAV (over GNSS) - declaration of an emergency.
Step 4: Centralized System/UTM-Space provider automatically increases the guard distance between the UAV and other air traffic to 10 s.
Step 5: Operator has to take over the operation - deciding over mission continuation/alteration/safe landing within 30 seconds.
Step 6: If Operator has not taken over mission control - return to safe heliport procedure on safe height is automatically triggered.
For the case when UAVs detect unauthorized recreational flights in the corridor:
Step 7: UAV on board sensors detect anomaly in the air.
Step 8: Anomaly data (e.g. point cloud, images) is continuously shared with UTM Space provider.
Step 9: In case of collision risk, UAVs adjust their flight path within permitted flight area.
Step 10: Anomaly data is centrally fused with the data from other sensors (including network sensors) to improve and classify risk.
Step 11: Flight corridor is adjusted according to the assessment.
7.18.4 Post-conditions
Airspace ISAC system has detected the UAV.
By using the sensing data from the airspace ISAC monitoring system, safety of the UAV operation is improved.
7.18.5 Existing features partly or fully covering the use case functionality
TS 22.137 [6] does not relate to the existing UTM space procedures in conjunction with onboard UAV sensors, localization services, and communication to the network. Some of the performance requirements specified in Table 6.2-1 of TS 22.137 [6] are partially valid for the proposed use case (e.g. 0.1% Missed detection introduced in case of category 2 sensing service).
TR 22.837 [9] did not address the highly valuable safety role below a flight ceiling of 200 m
7.18.6 Potential New Requirements needed to support the use case
[PR 7.18.6-1] The 6G system shall support the following KPIs:
Table 7.18.6-1: Performance requirements for Safe and Economic UAV transport
Scenario
Sensing service availability [%]
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing Resolution
Max sensing service latency
[ms]
Refreshing Rate [s]
Missed Detection [%]
False Alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Safe and economic UAV transport
99.999
99.99
≤ 50
50
5
5
10
0.5
≤ 100
0.1 - 1
0.1%
(notes 2, 3)
1%
(note 2)
NOTE 1: The listed positioning accuracy and sensing resolution are required for the detection of proximal objects within a safety area of the UAV and validate the reported location of the UAV itself.
NOTE 2: Safety impact of missed detection is substantially higher compared to the false alarm, here fore the Missed Detection is assigned a more stringent value compared to the False Alarm.
NOTE 3: To enable safe operation of BVLOS UAV flights in mixed airspace (crewed & uncrewed) and in high risk areas (e.g. above cities) the value for missed detection of 0.1% corresponds to the lower value of the missed detection range specified in Table 6.2-1 of TS 22.137 [6] for sensing service category 2.
7.19 Use cases on network assisted smart transportation
7.19.1 Description
Network Assisted Smart Transportation refers to use cases where the mobility of persons or connected vehicles such as cars or drones is assisted by the network. The assistance can be in the form of help e.g. sensing, navigating and positioning of humans or vehicles. Examples can be found in Autonomous Drone Transport, Smart Intersections, and Assisted Vehicles.
Figure 7.19.1-1: Network-assisted Smart Transportation example scenario
Autonomous Drone Transport
In Autonomous Drone Transport, flying drones are carrying goods in urban areas. The drones are equipped with sensors (camera, GPS, etc.) and a processor. Through the onboard 6G device they get a reliable communication link and that allows them to be positioned by the network. Over the communication channel the drone can receive timely information about nearby flight paths and activities and can receive recommended actions for fast and high energy efficient paths. The drone can also receive its corrected position as well as map data from the network. It can also, depending on need, offload tasks such as image processing from the camera feed and share the resulting data on the surrounding environment with the network. The users and deliverers of the drone transport service can count on a reliable, efficient, just-in-time delivery that can be tracked at any time, and people in the streets can trust that no collisions will occur by the drones passing over their heads.
Smart Intersections
Mobile communication infrastructure featuring sensing capabilities would enable a wide-coverage and well-connected radar mesh. Such network can be seen as a set of wide area sensors, that will add value to its users at nearly no extra cost. Safety and efficiency of urban traffic can be improved by means of real time spatial and temporal sensing of critical roads and intersections to support the vehicle when its onboard sensors do not have line of sight. For example, even before a vehicle gets to a junction, it will be made aware of the conditions around the junction (such as vulnerable pedestrians or other vehicles coming around the corner) so that the vehicle can take the right precautions to avoid collision or accidents. Different from monitoring with video cameras, such sensing approach would allow perception even in complete darkness, or in adverse weather conditions like rain or fog (with certain performance limitations). Also, privacy concerns for citizens would be reduced as no actual video or picture would be used. Finally, sharing the sensed real time data with traffic control centres can help to improve traffic flow management in smart cities.
Assisted Vehicles
Vehicles today have many onboard sensors to support applications such as cruise control, pedestrian detection etc. In the 6G era, the enabling of advanced automotive features such as autonomous driving and autonomous coordinated manoeuvring is envisioned through leveraging the wide area sensors from the network, in addition to on-board vehicle sensors. AD would allow vehicles to be navigated through challenging traffic conditions and terrains without the need for human interaction.
In addition, the autonomous coordinated manoeuvring feature would allow multiple vehicles to autonomously navigate through roads and highways in a coordinated fashion to ease traffic congestion and improve the traffic flow. These advanced automotive features would require a comprehensive detailed knowledge of the environment surrounding the vehicles as well as high precision localization and positioning. This would be enabled by the fusion of the wide-area sensor information provided by the network, vehicle on-board sensors and/or even sensors embedded in the transport infrastructure.
Situational awareness for mission critical applications
Just like for advanced mobility, situational awareness is vital in mission critical operations such as those seen in public safety. This includes firefighting, law enforcement, and emergency medical services. It involves understanding the environment, ongoing events, and the projection of these into the future, to be used as input to decision making and to manage risks.
For supporting improved situational awareness for mission critical use, the 6G system can be used to transfer, generate and process relevant information. Sensor information coming from devices worn by public safety personnel can be used to monitor health status, track position and mobility, and to provide instant audio-visual information for scenario understanding. The 6G system can also provide location services, either for specific users or to monitor the flow and density of people in an area. Sensing is an additional service that can provide information for scenario understanding through object detection and tracking, or through collection of mobility information related to vehicles and people in an area.
Additionally, mission critical use of the 6G system in resource constrained scenarios leads to a need to differentiate or prioritize services, e.g. between public safety users and non-public-safety users, or even among public safety users. In some scenarios, a service to a user may need to be terminated to free up resources for a specific public safety user. Such prioritization is not only valid within a specific service domain, but also between services that are relying on the same radio or system resources, e.g. positioning, sensing and communication.
Sustainability impact analysis
Energy resources: Less energy/fuel would be consumed due to driving via optimized routes, however the additional sensing equipment will require energy therefore energy consumption needs to be considered while developing the required solutions.
Material resources: Need for waste treatment is expected to reduce due to decreased number of accidents. In addition, if the application of sensing technology leads to producing less or smaller vehicles, less material will be needed.
Emissions: Improving the traffic flow and assisted driving should lead to reduction of emissions.
Health: Reduced transport-related accidents should lead to enhanced safety and well-being,
Trustworthiness: Preserved privacy is enabled through network-assisted mobility compared to video-based solutions, however there are still potential risks for privacy intrusion associated to localization data, which can be mitigated with the help of privacy preserving technologies, and an increased risk of cyber-attacks or unintended errors which may lead to accidents.
Work & income: With self-driving vehicles the job opportunities for drivers are reduced. Potential improved efficiency from freeing resources e.g. by providing the ability of working during transport/driving.
Infrastructure: Safer road transport systems are a result of the use-case and the access to reliable transports increases. However, building network infrastructure to meet the requirements for high reliability of services can be costly.
7.19.2 Pre-conditions
- Vehicles and drones using the service are equipped with UEs.
- The service is offered in a well-defined service area/service volume.
These use cases are fulfilled within the service volume as shown in Table 7.19.2-1, a defined volume where the service is offered, that the specific use case would require (e.g. an airspace volume for drones, ground level for cars).
Table 7.19.2-1: Service Volume
Scenario
Service volume
Autonomous Drone Transport
Area 10 km*10 km
Height: 150 m
Smart Intersections
Area 200 m*200 m
Height: 50 m
Assisted Vehicles
Area: Circle with 110 m radius with vehicle in centrum.
Height: 50 m
7.19.3 Service Flows
In this use case, illustrated in Figure 7.19.1-1, vehicles (cars, AGVs, drones, etc.) are relying on the network assisted Smart Transportation service that includes network nodes and devices for localization of connected and unconnected objects and for determination of their properties such as size and velocity, contextual info, trajectories etc. Vehicles have a reliable connection to the network, with services available in a well-defined service area. Networks measure the physical environment in traffic scenarios and analyse detected objects, and aggregate data of device positions and from this large data set extract information to relay to vehicles. Position data shared with vehicles can include position of VRUs, such as pedestrians and cyclists.
This can be done on several levels; networks can provide raw or processed environmental and position data, or navigational support ranging from collecting and sharing of data, over navigation assistance, to full operation, leveraging on beyond-communication capabilities. The network can thereby with the network assisted Smart Transportation service, support vehicles with different levels of autonomy and different modes of operation and enable smart transport in urban areas.
Furthermore, there is a possibility to use the understanding of the physical environment around nodes and devices to improve communication to vehicles by tailoring beams, avoiding blockers, etc.
Some examples of the services provided by networks are: network assistance info (warnings, trajectories, object locations), network assistance map (digital 3D map data), network navigation (path/route recommendations) and context-aware communication (path selection, scheduling).
7.19.4 Post-conditions
Connected vehicles using the service get up to date traffic, position and map information, thereby increasing traffic safety.
7.19.5 Existing features partly or fully covering the use case functionality
Based on operator policy, 5G system shall be able to provide means to predict and expose predicted network condition changes (i.e. bitrate, latency, reliability) per UE, to an authorized third party.
- Network positioning available in 5G standard as in TS 22.261 [14] and TS 22.125 [35].
Table 7.19.5-1: Positioning performance requirements
Scenario
Accuracy
(95 % confidence level)
Availability
Heading
Latency for position estimation of UE
UE Speed
Corresponding Positioning Service Level in TS 22.261 [14]
Horizontal accuracy
Vertical accuracy
8K video live broadcast
[0.5 m]
[1 m]
99%
1s
[<120 km/h]
5
Laser mapping/
HD patrol
[0.5 m]
[1 m]
99%
1s
[<120 km/h]
5
4*4K AI surveillance
[0.1 m]
[<60
km/h]
Remote UAV controller through HD video
[0.5 m]
[1 m]
99%
1s
[<120 km/h]
5
Periodic still photos
[0.1 m]
[1 m]
[<60 km/h]
NOTE: The positioning accuracy in this table is not related to navigation or safety.
- Sensing requirements for 5G as specified in TS 22.137 [6]. Dependent on what object to give assistance to the requirement on the sensing service will vary. For the drone/vehicle use case described above sensing service category 2 or 3 specified in Table 6.2-1 performance requirements in TS 22.137 [6] would be required.
Table 7.19.5-2: Performance requirements for 5G Wireless sensing (Table 6.2-1 from [6])
Scenario
Sensing service category
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Sensing service description in a target sensing service area
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Object detection and tracking
2
95
2
5
1
N/A
1
1
1000
0.2
0.1 to 5
5
Outdoor (e.g. detection of human, UAV)
3
95
1
1
1 [3], [4]
1
1 [3], [4]
1 x 1 [3]
100 [2], or 1000 (note 3);
5000 for detection in highway
0.05 to 1
2
2
Indoor/outdoor (e.g. detection and tracking of human, animal, UAV)
NOTE 1: For sensing service categories to which UAV, human or vehicle is a sensing target, the typical size (Length x Width x Height) of UAV is 1.6 m x 1.5 m x 0.7 m, the typical size of human is 0.5 m x 0.5 m x 1.75 m, and the typical size of vehicle is 7.5m x 2.5m x 3.5 m.
NOTE 2: The safe distance between pedestrian/vehicle and power transmission station/line is 0.7 m / 0.95 m.
NOTE 3: To realize 1m granularity tracking, when the velocity resolution is 1 m/s, the maximum corresponding sensing service latency is 1 s.
NOTE 4: This value is derived from the water level where people feel difficulty in walking.
7.19.6 Potential New Requirements needed to support the use case
The Network Assisted Smart Transportation is a service offered by the operators to users to allow for e.g. UAVs, cars, etc. to get awareness of the service area surrounding them. This awareness is created by positioning and sensing of objects within this service area and sent to users.
The following requirements for Network Assisted Smart Transportation are derived from the use cases above and are valid within the service volume defined in Table 7.19.2-1.
[PR 7.19.6-1] The 6G system shall be able to offer this Network assisted Smart Transportation service by ensuring the KPIs for communication, location and sensing according to Table 7.19.6-1 are fulfilled simultaneously.
[PR 7.19.6-2] The 6G system shall be able to prioritize communication, sensing and positioning together used in Network Assisted Smart Transportation.
Table 7.19.6-1: Performance requirements
Communication KPIs
Spatial KPIs
Scenario
User experienced data rate
RTT latency
Communication Service availability
Connection density
Location accuracy
Sensing accuracy
Network assisted smart transportation
[1-10 Mb/s]
[40 ms]
99.99%
(note 1)
104 devices/km2
[1m] * [1m] * [1m] with 90% probability
(note 2)
category 2 or 3
(notes 2, 3)
NOTE 1: within service volume
NOTE 2: within 99% of the service volume
NOTE 3: Category 2 or 3 in Table 6.2-1 in TS 22.137 [6]
7.20 Use case on sensing assisted communication in industry park
7.20.1 Description
Sensing would be the important features of 6G system, and could provide much more rich environment information, including the location and velocity information of environment objects. This data could not only be used for sensing service but also could be used to manage and update network operation.
For example, which is shown in Figure 7.20.1-1, in one specific area in which sensing results could be used to optimize communication service. When one UE is moving from one cell to another cell, the location information of that UE, as well as the spatial information of surrounding environment objects, could be derived as sensing results by 6G system. With the sensing results about UE and environment, 6G network could direct UE to transmit radio signal with more accurate beam direction toward the target base station to reduce the time and information exchange during handing over procedure.
Figure 7.20.1-1: Hand over with position information of UE
7.20.2 Pre-conditions
In a port industry park, MNO MM has deployed base stations, which could transmit and receive sensing signal to derive sensing results by collecting 3GPP sensing data and non-3GPP sensing data from other sensors linked with the base stations. These sensing results could be used by 6G system to promote the communication performance. Alex is a delivery robot operator, working in this industry park. His work is controlling several robots to transfer goods according to working requirement from port to the industry nearby.
7.20.3 Service Flows
1. In the working time of daylight, a lot of goods has been placed in the port. Alex controls 10 delivery robots to transfer these goods to the industry nearby. 6G network providing large-bandwidth and low-delay communication service for Alex’s robots. The base stations in this park, starts to transmit and receive the sensing signal to monitor the position and environmental information of Alex’s robots.
2. Some delivery jobs are assigned to Alex, and Alex needs to control robot to go to port and back to industry repeatedly.
3. MNO MM begins to use the sensing results about the robots and surrounding environmental objects. 6G network delivers the sensing results to serving base station and robots to optimize the beam management procedure and other procedures. The robots use the position and orientation of itself and environment information to choose the best uplink transmitting beam without whole beam management procedure. So as downlink receiving beam does by surrounding base stations.
4. When one of robots is deciding uplink beam, it finds that the sensing results of surrounding environment objects are insufficient. Thus, this robot requests sensing operations in 6G system to providing more detailed sensing results for assisting communication. More resources are assigned for providing more accurately sensing. Given the high accurate and trustable sensing results then, the communication could be ensured with lower delay and energy consuming.
5. In the evening, the delivery work has been finished. The robots keep standing by statically. No sensing results are needed to assist robots’ work and no frequent handing over is requested.
7.20.4 Post-conditions
Thanks to 6G network, MNO MM ensures that Alex enjoys stable and efficient sensing and communication service.
7.20.5 Existing features partly or fully covering the use case functionality
In TS 22.137 [6], the basic procedure of sensing has been described, including capability reporting, data collection, data transmission, data processing, and capability exposure, which are listed below:
The 5G system shall be able to provide sensing service to detect, and/or track one or more objects (e.g. UAVs, birds) and the environment around the object(s).
Based on operator’s policies, operator’s control and regulation, the 5G system shall be able to collect 3GPP sensing data from sensing receivers for processing.
Subject to operator’s policy and regulation, the 5G system shall be able to provide secure means for a trusted third-party to receive sensing results with contextual information.
Subject to regulation and operator policy, the 5G network shall be able to activate, configure, and deactivate 5G wireless sensing based on parameters such as location and network conditions (e.g. network load).
Subject to regulation and user consent, the 5G network may be able to link sensing results with 3GPP subscriber identity of a UE for a sensing target associated with that UE served by the same network.
NOTE: The purpose of this requirement is to ensure that association of 3GPP subscriber identity and sensing results is possible only with user consent and according to regulatory requirements.
In the above requirements, legacy network is already able to link the sensing results with identity of a UE, which can be delivered to the UE for assisting communication. In this use case, in one same area, a group of UEs could be provided same sensing result of environmental characteristics for communication service. Thus, it is believed in this use case that sensing results could also be linked with service area.
7.20.6 Potential New Requirements needed to support the use case
[PR 7.20.6-1] Subject to operator’s policy, regulation and user consent, the 6G network shall be able to expose sensing results to UE which is authorized by the network operator to use the sensing results for a specific service (e.g. communication service).
NOTE: As an example, UE could use the provided sensing results (e.g. environment characteristics around UE) to optimize communication service.
[PR 7.20.6-2] Subject to operator’s policy and regulation, the 6G system shall be able to link sensing results with communication service area for communication service.
7.21 Use case on autonomous driving based on network-assisted sensing
7.21.1 Description
Autonomous vehicles are equipped with various non-3GPP sensors, including LiDAR, radar, cameras, and ultrasonic sensors. These sensors work together to create a comprehensive understanding of the vehicle's surroundings. The data from these sensors is processed by the vehicle's onboard computer, which uses algorithms and artificial intelligence to interpret the information. The computer then makes decisions about steering, acceleration, and braking to navigate the vehicle safely.
However, there are several scenarios where a vehicle's onboard sensors (e.g. radar, LiDAR, camera) may fail to detect the environment effectively, e.g.:
Heavy rain, snow, fog, or dust can obstruct cameras, LiDAR, and radar, reducing their effectiveness.
Large vehicles, buildings, or other obstacles can block the vehicle's sensors, creating blind spots.
In dense urban areas with heavy traffic, pedestrians, and complex intersections, onboard sensors may struggle to track all objects accurately.
At high speeds, onboard sensors may have limited reaction time due to their range and processing constraints.
Sensors can fail due to hardware issues, software bugs, or physical damage (e.g. a dirty or broken camera).
Onboard sensors can only detect objects within their line of sight, making it difficult to anticipate hazards around corners or over hills.
In such cases, it is advantageous to utilize sensing data obtained via the 3GPP network, alongside data from the vehicle's onboard non-3GPP sensors, to be processed locally for deriving the result.
While the vehicle can detect sensing data by itself if 3GPP sensing is supported, integrating 3GPP sensing directly into the vehicle may require significant modifications to the vehicle's hardware and software systems, which can be complex and costly; hence, it can’t be assumed that all the vehicles support 3GPP sensing.
No matter whether the vehicle, or a UE on board of the vehicle, is capable to perform 3GPP sensing detection or not, it can always request 3GPP sensing results of the surrounding environment to the network, which will be used in combination with the sensing data from the on-board non-3GPP sensors for deriving the sensing result locally. Based on the service request, the 3GPP network selects either RAN node(s) and/or UE(s) capable of 3GPP sensing to perform sensing and then provides the requested sensing data to the vehicle, or a UE on board of the vehicle.
7.21.2 Pre-conditions
Operator A provides 3GPP ISAC service.
Company X provides a reliable AD service. Company X can be a car producer or a 3rd party. One of the features of the AD service is to allow the vehicle subscriber to integrate Operator A’s 3GPP sensing results with onboard non-3GPP sensors to enhance environmental perception for autonomous vehicles.
Alice subscribes to an AD service from Company X.
Company X’s subscriber (e.g. Alice’s Vehicle) is allowed to use Operator A’s ISAC service.
As the subscriber of Company X, Alice’s vehicle is subscribed to the ISAC service provided by Operator A as a consumer. Alice’s vehicle can’t perform 3GPP sensing detection.
Alice’s vehicle supports AD based on Network-assisted Sensing, which can be switched on and off.
7.21.3 Service Flows
Figure 7.21.3-1: Autonomous driving based on network-assisted sensing
As shown in Figure 7.21.3-1, the service flow for an use case of autonomous driving based on network-assisted sensing may include the following steps.
Step 1: Alice is busy with her work; however, it is time to pick up her son from school. Alice set the route to the school and switches on “Autonomous Driving based on Network-assisted Sensing” of her vehicle. Her vehicle automatically runs on the road. The road is very busy, and a truck runs in front of Alice’s car which blocks the sights of some non-3GPP sensors on Alice‘s vehicle.
Step 2: Alice’s vehicle sends an ISAC service request to the 6G network requiring periodically receiving the sensing result of its surrounding environment in the predefined route.
Step 3: Alice’s vehicle is authorised by the 6G network to use the ISAC service as the subscriber of Company X.
Step 4: The 6G network selects RAN node(s) and/or UE(s) capable of 3GPP sensing to perform sensing periodically.
Step 5: The 6G network periodically collects the required sensing data from the selected RAN node(s) and/or UE(s).
Step 6: The sensing data is pre-processed by 6G network, and the sensing result is derived.
Step 7: Based on the volume of the requested sensing data, the 6G network determines to establish a connection to Alice’s vehicle and provides Alice’s vehicle with the requested sensing result along the predefined route.
Step 8: Alice’s vehicle receives the requested sensing result from the 6GS network.
Step 9: The 3GPP sensing result received from the 6GS network, in combination with the locally detected non-3GPP sensing data is processed at Alice’s vehicle, and a car accident blocked by the truck is detected on the same lane. Alice’s vehicle changes the lane and avoids the potential traffic jam due to the accident.
7.21.4 Post-conditions
Alice’s vehicle safely arrives at the school on time.
7.21.5 Existing features partly or fully covering the use case functionality
Use Case on Sensing Assisted Automotive Manoeuvring and Navigation described in clause 5.8 of TR 22.837 [9] includes a similar scenario; however, the sensing result is sent to third party application, instead of the UE.
Considering that the vehicle capable of AD usually supports sensing result calculation and with the combination of both 3GPP sensing results and non-3GPP sensing data, the performance can be significantly improved, it is better that the 3GPP sensing results is provided to the UE directly.
There are also several additional benefits of a UE directly obtaining 3GPP sensing results from the 3GPP network compared to acquiring it from third party AS:
Reduced Latency: When the UE obtains sensing data directly from the 3GPP network, it eliminates the additional step of the AS retrieving the data first. This direct communication reduces latency, which is crucial for real time applications such as AD and ADAS.
Improved Reliability: Directly obtaining data from the 3GPP network reduces the dependency on the application server's availability and performance. This can enhance the reliability of the sensing data, ensuring that the UE receives timely and accurate information.
Enhanced Security: Direct communication between the UE and the 3GPP network can provide better security measures, as it minimizes the number of intermediaries involved in the data transmission. This reduces the risk of data breaches or tampering.
Consistency and Accuracy: Directly obtaining sensing data from the 3GPP network ensures that the UE receives the most up-to-date and accurate information. This consistency is vital for applications that require precise and real time data.
7.21.6 Potential New Requirements needed to support the use case
[PR 7.21.6-1] Subject to regulation, operator policy and user consent, the 6G system shall be able to provide a mechanism for a network operator to authorize a UE or an application on the UE to obtain sensing result as a consumer for a specific service.
7.22 Use case on structural health monitoring
7.22.1 Description
With the development of global industry and economy, many bridges, high-rise buildings, dams, mines and other facilities have appeared in various countries. As the service life of these facilities increases, supported by continuous, and large covering sensing, structural health monitoring could become increasingly important for ensuring resilience and public safety and extending the lifespan of critical infrastructures.
Structural health monitoring provides a systematic way to assess the current condition of buildings and other structures and to detect abnormal changes. Traditionally, such monitoring has relied on manual inspections and specialized instruments (e.g. GNSS, radar, optical Doppler), which are often costly, customized, and difficult to be deployed on a large scale. In 6G networks, base stations equipped with both communication and sensing capabilities can perform structural health monitoring of nearby targets with large coverage and high accuracy. This structural health monitoring could be realized by strategically deploying high Radar Cross Section (RCS) sensing targets (e.g. corner reflectors [325]) on critical structural points of the monitored building infrastructure. The base station sends sensing signals to these sensing targets, receives their echoes, and sends the collected 3GPP sensing data to the 6G network. The network processes this sensing data into sensing results related to the measured structural displacement and health status of the building infrastructures. These sensing results can be securely exposed to trusted third-party structural health monitoring applications. Infrastructure owners and building or bridge management departments can subscribe to sensing services offered by operators to obtain continuous indicators of structural condition, trend analyses, and timely alerts for anomaly detection and post-event assessment.
This 6G network-based structural health monitoring offers several advantages: wide-area coverage leveraging existing network infrastructure, continuous and objective monitoring, with deployed high-RCS sensing targets. It could support routine condition assessment for buildings, enabling building owners to implement proactive maintenance strategies, enhance safety, and extend structural service life.
Figure 7.22.1-1: An illustration for Structural health monitoring
7.22.2 Pre-conditions
In City A, in response to the request from the road management department, MNO MM has deployed base stations near bridges to monitor the structure’s health. On critical structures of bridges, MNO MM also deployed several corner reflectors as sensing targets to help measure the displacement of bridges, as depicted in Figure 7.22.1-1. The base station transmits sensing signals and receives the sensing signal reflected by the sensing targets on the bridge.
7.22.3 Service Flows
1. The road management department subscribes to the service of structural health monitoring for bridges in City A.
2. MNO MM has base stations deployed nearby bridges.
3. MNO MM has corner reflectors as sensing targets deployed on critical structures of bridges.
4. The base stations keep transmitting sensing signal to sensing targets on bridges, receiving the reflected echo signal, collecting 3GPP sensing data, and reporting to 6G network.
5. 6G network obtains 3GPP sensing data and processes the 3GPP sensing data into sensing results related to the measured structural displacement of the sensing targets on bridges, and exposes the sensing results to the road management department.
6. Based on the sensing results above, the road management department derives the health status of bridges in real time. For example, the displacement of the bridge could be calculated based on sensing results of the corner reflector on bridge with the formula [326]:
7.22.4 Post-conditions
The road management department officers and engineers are able to check the health status information of bridges, benefiting from structural health monitoring provided by 6G network. Accordingly, the officers and engineers make plans for future maintenance and infrastructure renewal.
7.22.5 Existing features partly or fully covering the use case functionality
In TS22.137 [6], The basic procedure of sensing has been described, including capability reporting, data collection, data transmission, data processing, and capability exposure, listed below:
The 5G system shall be able to provide sensing service to detect, and/or track one or more objects (e.g. UAVs, birds) and the environment around the object(s).
Based on operator’s policies, operator’s control and regulation, the 5G system shall be able to collect 3GPP sensing data from sensing receivers for processing.
Subject to operator’s policy and regulation, the 5G system shall be able to provide secure means for a trusted third-party to receive sensing results with contextual information.
7.22.6 Potential New Requirements needed to support the use case
[PR 7.22.6-1] The 6G system shall be able to support the following KPIs:
Table 7.22.6-1: Performance requirements of sensing results for structural health monitoring
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Structural health monitoring
Outdoor (e.g. detection of corner reflectors on bridges, buildings in urban scenario)
95
0.1,
(NOTE)
0.1
N/A
N/A
N/A
N/A
5000
60
5
5
NOTE: The typical length of corner reflector is 0.5m [327].
7.23 Use Case on UAV Detection, Classification and Counting
7.23.1 Description
In increasingly complex sensing environments, such as urban centr[[SUGGESTION_START]]e[[SUGGESTION_END]]s, airports, and large-scale public events, the accurate detection, classification, and counting of UAVs is critical for airspace safety, security, and regulatory compliance, as depicted in Figure 7.23.1-1. Moreover, in many real-world applications, UAV sensing involves the simultaneous detection and tracking of multiple UAVs rather than a single target. This is especially common in scenarios where UAV usage is dense and diverse, such as logistics, infrastructure inspection, or public event monitoring. Therefore, the ability to accurately count and distinguish multiple UAVs becomes a fundamental requirement.
This capability is particularly relevant considering the growing interest in low-altitude airspace applications, where UAVs are expected to play a central role in logistics, inspection, emergency response, and aerial mobility. In some regions, such as China, the concept of a “low-altitude economy” is gaining policy-level support and industrial momentum, further highlighting the importance of robust UAV sensing and counting technologies to enable safe and efficient airspace operations.
Traditional RF sensing methods often assume a dominant direct or line-of-sight (LoS) path between the UAV and the sensing infrastructure. However, in real-world deployments, especially in urban environments, high-rise buildings introduce significant multipath effects by reflecting sensing signals. These multipath reflections lead to two major challenges:
• False Alarms: Reflected paths may appear as additional targets, causing over-counting if detection is based solely on signal strength thresholds.
• Localization Errors: Secondary reflections may be misinterpreted as direct paths, resulting in large position estimation errors.
Initially, UAVs can be distinguished from environmental clutter and other aerial targets such as birds by analysing their unique micro-Doppler signatures. To further address the challenges posed by multipath environments, this use case proposes leveraging micro-Doppler spectrograms extracted from 6G ISAC signals. Micro-Doppler signatures capture the fine-grained, periodic motion characteristics of UAVs (e.g. rotor blade rotations), which remain consistent across different multipath reflections of the same UAV. These signatures are largely invariant to propagation distance, reflection angle, or the number of bounces.
The distinct micro-Doppler signatures can be used to:
• Detect UAVs among various aerial objects and environmental clutter (e.g. distinguish UAVs from birds).
• Differentiate UAVs not only across different types (e.g. quadcopter, hexacopter) but also among individual UAVs of the same type.
By identifying and clustering paths with consistent micro-Doppler signatures, the system can:
• Detect and classify UAVs among multiple detected objects.
• Associate multiple reflections with a single UAV in multi-target scenarios.
• Reduce false alarms and improve localization accuracy.
• Accurately count the number of UAVs in a given airspace.
This capability is enabled by 6G RAN entities and UEs (e.g. smartphones, IoT devices, RSUs) acting as distributed sensors, combining their observations through data fusion to enhance robustness and coverage. The use case is particularly relevant for low-altitude airspace applications, where UAVs are expected to play a central role in logistics, inspection, emergency response, and aerial mobility. In some regions, such as China, the concept of a “low-altitude economy” is gaining policy-level support and industrial momentum, further highlighting the importance of robust UAV detection, classification and counting technologies. Upon request, the UAV detection, classification and counting sensing results can be provided by the 6G mobile network operator to trusted third-party applications, such as UAV service operators, regulatory agencies, UTM systems, and the UAV itself.
Figure 7.23.1-1: UAV detection, classification and counting by 6G system
7.23.2 Pre-conditions
In this use case, a USS/UTM ( provides UAV services, such as package delivery, aerial photography, flight management services, within an area covered by a 6G network. These services may lead to a high-density UAV scenario in certain areas due to busy UAV traffic.
Network Operator NN provides 6G sensing service for UAV supervision service, including illegal UAV intrusion detection, UAV flight trajectory tracing, and UAV collision prediction etc. NN can make use of sensing entities (e.g. network nodes and UEs) to sense the airspace within their coverage area and report the sensing results (including detected and classified UAV) to the USS/UTM.
Network Operator NN provides the sensing result that may comprise multiple objects of multiple types (e.g. UAVs, birds, other aerial targets, environmental clutter). The system is required to detect and distinguish UAVs among detected objects even in challenge wireless conditions. The results may also include UAV classification and counting of each class to enable more fine-grained sensing.
Company MM uses the USS/UTM to supervise the low-altitude UAVs and manage potential illegal intrusion into the restricted areas. MM has proved its restricted area information to the USS/UTM. The USS/UTM uses 6G sensing service provided by the 6G Network Operator NN for UAV supervision.
The USS/UTM provides specific details to the 6G network operator NN, including the characteristics of the UAV that will be detected. This information includes types (e.g. quadcopter, hexacopter) and sizes of target UAV. This information may also include the characteristics of the environment clutter and other aerial objects.
The 6G Network Operator NN provides the sensing service by collecting, processing and storing sensing data with the network providing sensing results to the USS/UTM.
7.23.3 Service Flows
Based on the request from USS/UTM, the 6G Network Operator NN activates the UAV sensing service in the designated airspace, which includes multiple UAVs, other aerial objects (e.g. birds), and various buildings and structures. These objects and structures contribute significant environmental clutter that can make sensing challenging.
Once the sensing service is activated, RAN entities and UEs within the operator NN network are triggered to perform RF sensing, transmitting and/or receiving sensing signals that are reflected from UAVs, birds, and environmental clutter.
The received sensing signals are processed to extract sensing data depicting micro-Doppler spectrograms. Using the micro-Doppler signatures of UAVs and birds, which are typically unique and remain consistent even across multipath reflections, the network is able to distinguish UAVs from birds and clutter.
With further processing, the network is able to:
• Classify the detected UAVs by type (e.g. quadcopter, hexacopter) and, where possible, distinguish individual UAVs of the same type.
• Count the number of UAVs in the area, including type-level (e.g. number of propellers) counting, and report the results to authorized stakeholders (e.g. airspace managers, event organizers).
The sensing results (e.g. different types of UAVs and the count of UAVs in the area, including type-level counting) and contextual information (e.g. time and locations of UAVs) are exposed to the authorized USS/UTM.
In certain cases, if the sensing assistance information such as the type and size of UAVs are not received from the USS/UTM, the detection of the UAVs is completed by the 6G system and the sensing results could be shared with a 3rd party application where the classification and counting would be performed.
7.23.4 Post-conditions
The UAVs of interest in the designated airspace is accurately detected, classified, counted, and exposed to the trusted third parties.
7.23.5 Existing features partly or fully covering the use cast functionality
TR 22.837 [9] and TS 22.137 [6] present use cases and requirements for 5G system integration of sensing and communication, including detection and tracking of UAVs and other objects:
• The 5G system shall be able to provide sensing service to detect, and/or track one or more objects (e.g., UAVs, birds) and the environment around the object(s).
5G wireless sensing requirements address general object detection and tracking, but do not explicitly support, identification of UAV-specific characteristics and counting. The use case, potential requirements and KPIs presented here are expected to facilitate robust discrimination between UAVs and other aerial objects (e.g. birds), identification of characteristics and counting in many scenarios, especially, under challenging wireless conditions.
7.23.6 Potential New Requirements needed to support the use case
[PR 7.23.6-1] Subject to operator policy, the 6G network shall be able to provide sensing service to detect, classify and count one or more sensing targets (e.g. detect UAVs, distinguish UAVs from birds, identify specific UAV characteristics.).
NOTE: Classification refers to the identification of specific characteristics of the detected target objects and grouping together of the detected target objects with similar characteristics. For example, once a UAV is detected, the sensing service can be used to determine the type of UAV it is (e.g. quadcopter or hexacopter) and then group together UAVs with similar characteristics in each class.
Table 7.23.6-1: Performance requirements for UAV Detection, Classification and Counting
Scenario
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Sensing Target Density
Max sensing service latency
[ms]
Refreshing rate
(Hz)
Missed detection
[%]
False alarm
[%]
Sensing service description in a target sensing service area
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s]
UAV detection, classification and counting
95
1
Note 1
1
Note 1
1
Note 1
1
Note 1
[0.3 – 1]
Note 2
1
Note 1
N/A
Note 1
≤1000
Note 1
≤1
Note 1
2
Note 1
2
Note 1
outdoor
NOTE 1: Most of the requirements presented here are related to the sensing category 3 in [6]
NOTE 2: Detection, classification of UAV features such as propellers may require more stringent velocity and range resolution due to the dimensions of propellers with respect to the UAVs. Some propeller diameters to UAV body ratio could be between ½ to 1/3 [330], therefore, the range resolution values are modified accordingly.
7.24 Use case on gesture recognition in industrial environments
7.24.1 Description
NOTE: This use case on gesture recognition in industrial environments has been originally described in the 5G-ACIA Whitepaper on ISAC use cases and requirements in connected industries and automation [94].
Interaction of robots and humans in factory environments is a dynamically evolving aspect of modern manufacturing. This collaboration involves various levels of cooperation, communication, and coordination between robotic systems and human operators.
Collaborative Robots (Cobots) are designed to directly and collaboratively interact with humans, often in close proximity. Efficient interaction ensures that cobots and humans can work together seamlessly, leveraging each other’s strengths to maximize the overall productivity of manufacturing processes.
Cobots include interactive modes, for example, for reparametrization or maintenance. With cobots, easy, continual, and intuitive interaction is key. (Touch) panels and other handheld devices for interacting with robots are impractical in some environments and scenarios, for example, if a worker is wearing heavy protective gloves or needs his hands to perform a concurrent task. In other scenarios, for example, when performing certain dangerous tasks, nonstop visual monitoring of the cobot by the human is required. Factory environments can also be too noisy to use a voice interface.
In such scenarios, it would be helpful to have an easy and intuitive way of interacting, for example with gestures. This can be described as a touchless interface. Two main functions are required for reading gestures: object detection and pattern recognition. In contrast to continuous gesture recognition (for example, by moving a mouse pointer on a screen), discrete gesture detection is more appropriate for scenarios involving interactions with a cobot. Typically, a predefined set of gestures is employed, with each of them corresponding to a specific action. See Figure 7.24.1-1 for an example. Interactions can be additionally enhanced by visual acknowledgements etc. to assist communication between humans and cobots.
Figure 7.24.1-1: ISAC for gesture recognition in an industrial environment (Source: 5G-ACIA / ZVEI e.V.[94]
Gesture-based interfaces are likely to become more sophisticated and permit the use of a wider range of gestures for more nuanced communication between humans and cobots. Human gestures can be categorized as follows:
• Hand gestures: hand gestures, finger positions and pointing directions
• Body and limb gestures: full body actions, motions or poses, arm positions and movements
• Head gestures: nodding, shaking or direction in which the head is pointing
• Facial gestures: winking or closing one or both eyes
When cobots work alongside humans, it has to be prevented that incorrectly used gesture commands provoke dangerous situations.
The performance requirements for sensing objects and detecting gesture patterns can vary depending on the gesture set and scenario.
Gesture recognition is applicable to several other application areas as well, for example, healthcare use cases.
Potential sustainability impacts
[tbd]
7.24.2 Pre-conditions
A mobile network with ISAC capabilities and wireless coverage are available in the relevant areas of the factory. Standardized interfaces are used for accessing information including detailed estimates of the margin of error of the provided measurement data.
7.24.3 Service Flows
Step 1: A worker wishing to take an autonomous mobile robot (AMR) with integrated UE somewhere else to perform a task walks toward it and stands in front of it.
Step 2: The worker laterally raises his/her arms, which is a predefined gesture for telling the robot to follow.
Step 3: Specialized radio signals from the infrastructure and/or nearby UE are used to sense the environment.
Step 4: The sensor data is processed and the gesture is interpreted.
Step 5: The recognized request is forwarded to the AMR.
Step 6: The AMR follows the worker while the system continually interprets any new gestures.
Figure 7.24.1-1 shows the service flow for stopping the following AMR:
1. Sensing: The raised hand of the worker is detected. The raised hand is the defined gesture for ":stop".
2. Gesture detection and interpretation: The gesture of the raised hand of the worker is detected and is interpreted as ":stop".
3. Triggering: The stop command ( ) is sent to the autonomous mobile robot.
7.24.4 Post-conditions
The worker has used gesture detection to deliberately control a nearby machine. This is done without the need to touch the machine or use a device to issue commands. This increases the worker’s efficiency and productivity.
7.24.5 Existing features partly or fully covering the use case functionality
TS 22.137 [6] Table 6.2-1: Performance requirements for 5G Wireless sensing contains performance requirements for human hand gestures obtained by sensing in category 7. However, these (general) requirements on hand gesture recognition are less stringent than for gesture recognition in industrial environments for several parameters.
7.24.6 Potential New Requirements needed to support the use case
The following requirements are necessary abilities for recognizing relevant body language in industrial environments.
[PR 7.24.6-1] Subject to regulatory requirements and user consent, the 6G network shall be able to use the 6G sensing service to monitor and recognize gestures of a worker such as hand gestures, body and limb positions, head positions, and facial expressions of a worker.
NOTE: The sensing resolution is proportional to the accuracy of recognition. No angular resolution for the sensor is given, since it depends on the distance from the subject. A combination of different sensors could be implemented to meet these requirements.
[PR 7.24.6-2] The 6G System shall support the sensing KPIs for gesture recognition in industrial environments as provided in Table 7.24.6-1.
Table 7.24.6-1: KPIs for gesture recognition in industrial environments [94]
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
(note 1)
False alarm
[%]
(note 1)
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Hand Gestures Recognition
Indoor (Factory Environment)
99
[0.1-0.3]
[0.1]
n/a
n/a
[≤0.02]
n/a
≤20
[≤0.02]
≤ 1%
≤ 10%
Facial Gestures Recognition
Indoor (Factory Environment)
99
[0.1]
[0.1]
n/a
n/a
[≤0.002]
n/a
≤20
[≤0.02]
≤ 1%
≤ 10%
Head Gestures Recognition
Indoor (Factory Environment)
99
[0.1]
[0.1]
n/a
n/a
[≤0.05]
n/a
≤20
[≤0.02]
≤ 1%
≤ 10%
Body and Limb Gestures Recognition
Indoor (Factory Environment)
99
[0.1]
[0.1]
n/a
n/a
[≤0.1]
n/a
≤20
[≤0.02]
≤ 1%
≤ 10%
NOTE 1: Depending on the actual application to be controlled by the gestures, Missed detection and False alarm may have different KPI values from < 1% to 10%.
NOTE 2: References for hand gesture recognition are [331][332], for facial and head gesture recognition [333], and for body and limb gesture recognition [334][335].
7.25 Use case on Smart Shopping Tracker
7.25.1 Description
The indoor location analytics is expected to see exponential growth from USD14.09 billion in 2024 to USD 41.66 billion in 2029 at a CAGR of 24.2%. The growth in the forecast period can be attributed to initiatives for smart cities, inefficiency of the GPS technology in indoor premises, demand for navigation, growing need for accurate and real time location services and increasing adoption of data analytics in the retail sector [336]. Furthermore, the World Economic Forum published a report on the Top 10 emerging technologies with “collaborative sensing” as one of them, outlining the opportunity for ISAC to “combine distributed sensors […] to improve decision making, urban systems and autonomous technologies” [337].
The described use case here demonstrates the business opportunities of ISAC enabled indoor analytics for MNOs and service providers to offer novel services to the retail sector. The proposition is that a shop owner in a larger shopping mall is interested in knowing which products customers are checking out most in their store. The store owner then finds out about a new service a company called SENSOR - not affiliated with the shopping mall or with any particular MNO - offers to retailers, allowing them to learn which parts of their store (and products displayed in that area) customers are most interested in. This is conveyed as a 3D representation of the store's interior and a heatmap-like overlay showing the customer interest. Another important benefit of sensing by 6G is the fact that risk of personal privacy being compromised is much less compared to camera-based sensing approaches.
The scenario is illustrated in Figure 7.25.1-1 and illustrates SENSOR (third-party service provider), the store owner and two MNOs, MNO A and MNO B. Furthermore, the store (shown on the bottom left) rents a broadband connection from MNO A which is operational inside the store. Inside the store are users with 6G UEs from MNO A and MNO B and all devices are 6G and Wi-Fi sensing capable.
Figure 7.25.1-1: Smart Shopping Tracker Scenario Utilising Sensing Results from Multiple MNOs
7.25.2 Pre-conditions
SENSOR has contracts with multiple MNOs and it is assumed that both MNO A and MNO B have 6G coverage of the shopping mall and have sensing capabilities in their 6G networks. Furthermore, the store owner also bought a broadband package from MNO A that comes with a sensing-enabled Wi-Fi AP and offers general internet access to all staff at the store. This Wi-Fi sensing station is under MNO A control.
As illustrated in Figure 7.25.1-1, both MNOs have an AF (Application Function) deployed in their network offering a Wi-Fi sensing enablement via an application on all 6G UEs.
UE1 and UE2 are registered with MNO A and UE3 is registered with MNO B. All three UEs are 6G and Wi-Fi sensing-enabled and have also registered with the AF of their operator.
Users of UE1, UE2, and UE3 have explicitly opted for being part of the sensing operations offered by their respective MNOs. This consent allows them to control when and where they can be part of a sensing operation, possibly in exchange for potential incentives.
The shop owner receives SENSOR services with information about which products were of most interest to their customers. Once the shop owner completed their sign-up process with SENSOR and booked a sensing service, SENSOR issues a sensing service request to MNO A and B providing the necessary information about the target sensing service area (e.g. cartesian coordinates of the shop outline), the desired sensing result KPIs for the sensing results, and the daily opening times of the store when sensing results should be provided.
7.25.3 Service Flows
At the opening time of store's business days, both MNOs start setting up their sensing activities to provide the sensing results SENSOR requested, executing the following service flows
• Using the provided location (i.e. TSSA) of the store, both MNO start continuously checking for available sensing entities (TRPs and UEs) in the TSSA for sensing operations.
• MNO A identifies UE1 and UE2 to be in proximity of the store and MNO B identifies UE3.
• Both MNOs start performing sensing operations with the identified UEs and sensing data is sent to the MNO’s CN for processing into sensing results for potential exposure to SENSOR.
• While continuously assessing the KPIs of the generated sensing results (e.g. target object identification confidence level, resolution accuracy), both MNOs' networks determine that the sensing result KPIs have not been met and consults their AF for assistance information to utilise Wi-Fi sensing of all three UEs located inside the TSSA.
• The AF of each MNO reaches out to the registered UEs that are capable of performing Wi-Fi sensing to provide sensing data using Wi-Fi APs that are discoverable by UEs.
• As UE1 and UE2 are with MNO A and the store’s AP is from the same MNO, the AF of MNO A assists MNO A’s CN to perform Wi-Fi sensing. .
• MNO A uses the Wi-Fi sensing data and fuses it with the 6G sensing data to process them into sensing results and expose them to SENSOR.
7.25.4 Post-conditions
MNO A can successfully provide sensing results that meet the requested KPIs through 6G and Wi-Fi sensing data fusion resulting in SENSOR being able to offer their service to the store's owner (i.e. generate a heatmap of customer interest overlayed onto a 3D representation of store interior).
The store owner can access the third-party service provider’s website through a browser and app at any point in time and check out in which aisle and shelve customers spent most of their time in the store.
7.25.5 Existing features partly or fully covering the use case functionality
TR 22.837 [9] has described use cases to monitor micro doppler effect by ISAC caused by chest rise/fall during sleeping. The sensing results represent the human respiration rate. TR 22.837 [9] has also described use cases for coarse gesture recognition for application navigation and immersive interaction.
In this use case, 3GPP ISAC is expected to leverage Wi-Fi Station [201] for detection and tracking of comprehensive characteristics of individual environmental objects to meet the same sensing KPIs per object type.
7.25.6 Potential new requirements needed to support the use case
Functional Requirements:
[PR7.25.6-1] 6G Network should be able to provide configuration information for the non-3GPP sensing operation under Operator control.
Editor’s Note: The PR above is FFS.
8 Ubiquitous Connectivity
8.1 General
Connectivity is foundational for providing access and delivering services. The ITU-R, in [27] describes that further development of ubiquitous connectivity "… would provide digital inclusion for all by meaningfully connecting the rural and remote communities, further extending into sparsely populated areas, and maintaining the consistency of user experience between different locations including deep indoor coverage." Furthermore, ubiquitous positioning is a key capability to enhance the resilience, accuracy and availability of positioning-enabled services.
These use cases consider expanding network as well as improving user experiences, where possible, for advanced services (e.g. positioning, location). Connectivity could also be enhanced through interworking with other systems. Capabilities of existing satellite networks and normative requirements specified in [14] provide the foundation for these use cases.
NOTE: The use of the clarifying phrase “the 6G system with satellite access” implies the following text applies specifically to the satellite access.
8.2 Use case on ubiquitous and resilient network
8.2.1 Description
The Ubiquitous Network use case is focused on delivering mobile broadband connectivity to every user across the globe, eliminating any "white zones"(i.e. areas in which there is no mobile phone service). This includes ensuring network access in all locations, such as remote areas, regions with challenging geographical conditions (e.g. mountains, forests), airspace for aerial operations (e.g. drone operations), as well as the open ocean.
This ubiquitous network will be realized by using both TN and NTN (including satellites, High-Altitude Platform Stations (HAPS), air-to-ground networks, and UAVs) in a transparent manner for the end user, fulfilling the required QoS criteria, such as bit rates and latency, to support daily needs.
While most users in these areas may not experience services with the highest performance levels (e.g. no URLLC or the most immersive experiences), they will be able to access a wide range of services with dependable connectivity, including high-quality voice or video streaming.
The uubiquitous networks will ensure that every person on earth can access the Internet and its most common applications, while also enabling the provision of new services, such as digital health services. Remote medical consultations will become feasible in areas without adequate medical infrastructure. Educational institutions, such as schools, could also leverage more advanced applications for remote virtual education.
Finally, the supporting of TN with NTN ensures that the network can deliver guaranteed connectivity under all conditions, maintaining a minimum level of service even during a crisis. This network resilience will be critical for emergency services in challenging weather conditions, such as storms (where network elements might be compromised), or during natural disasters like floods, earthquakes, forest fires, tsunamis, human-induced catastrophes (e.g. conflicts), or other events that currently cause network outages.
Table 8.2.1-1: Potential sustainability impacts of the use case
Potential
benefits of the use case (added value)
Potential areas of attention of the use case (risks to be mitigated)
Environmental
Preventing travel-related emissions as a result of remote access to services
Reducing emissions by low-usage TN base stations in remote area.
Enabling collection of environmental data for Earth monitoring
Access to precise status information can enable precision farming practices (agriculture, aquaculture) to reduce the use of fertilizers, pesticides, and fresh water
Increased material and energy use to build this ecosystem (e.g. devices, base stations, satellites)
Increased land use (e.g. networks, data centres) could potentially impact existing habitat, and thus, biodiversity
Potential increase of e-waste on land, oceans and space if not handled properly (e.g. collection, 100% biodegradable)
Social
Providing everyone access to the digital ecosystem
Providing access to digital services to all (e.g. entertainment, education, transactions, voting, etc.) à Bridging the digital divide
Contribute to making networks reliable (perception), everywhere
Enhanced trustworthiness of digital services (availability and accountability)
Delivering increased resilience in networks, crucial in unexpected events (e.g. natural disasters)
Enhanced healthcare and education to reach new areas
Increases food yield from enhanced agricultural management
Creation of new industries, and new job opportunities
Potential digital divide for people with functional variation / ageing population / IT literacy if all services are meant to be handled digitally
Potential risk to privacy if all services are meant to be handled digitally
Potential mental health problem due to possibility of always being connected and being traceable
Economic
Improved economic resilience from wider availability of connectivity for different services
Efficiency improvements from the reuse of resources
Economic benefits (profitability) to society/country from combining ubiquitous network with other use cases (e.g. in public services)
Profitability challenge from the network investment: to make digital services available to everyone, prices must be "affordable" which risks the economic/financial return of this use case.
Resilience challenges from the maintenance of network
8.2.2 Pre-conditions
Alex is living in a mountainous area with challenging access conditions. He has registered UEs (e.g. smartphone, tablet, computer) for communication and the network can provide wide coverage with TN and also NTN.
8.2.3 Service Flows
1. Alex is located in a mountainous area with difficult access conditions.
2. As depicted in Figure 8.2.3-1, the network ensures the continuous connectivity between the terrestrial and non-terrestrial networks.
3. Alex has an e-health appointment with a doctor and connects to the consultation using a compatible device (smartphone, tablet, computer), with sufficient video quality to enable the doctor to diagnose common health problems.
4. Throughput and latency requirements are less stringent than for URLLC communications, enabling a smooth consultation even under variable network conditions.
5. Advanced security mechanisms are in place to protect data integrity, confidentiality, and availability across the network.
Figure 8.2.3-1: Ubiquitous Network: "Connectivity at Remote Locations"
8.2.4 Post-conditions
Thanks to the ubiquitous network, Alex can now benefit from a wide range of services provided by the network. Alex can engage in virtual consultations with doctors. The video quality is sufficient for diagnosing numerous common health issues.
8.2.5 Existing features partly or fully covering the use case functionality
The following functionalities, described in the TS 22.261 [14], can be included in the list of requirements necessary to provide this use case:
- Network Slicing ( lause 6.1.2),
- Multiple Access Technologies ( lause 6.3.2),
- Priority, QoS, and Policy Control cClause 6.7.2).
- Connectivity Models (clause 6.9.2),
- Extreme Long Range Coverage in Low Density Areas ( lause 6.17.2)
8.2.6 Potential New Requirements needed to support the use case
[PR 8.2.6-1]: The 6G system shall be able to support the following KPIs:
Editor's note: KPIs are FFS and need to be further justified.
Table 8.2.6-1: Ubiquitous and Resilient Network KPIs [112]
End-to-end latency [ms]
Availability
[%]
Reliability
[%]
User experienced data rate
[Mb/s]
Connection density
[devices/m2]
Coverage
[%]
Mobility [km/h]
Location accuracy
[m]
Positioning availability
[%]
Positioning latency
[s]
[10-100]
(note)
[10-500]
[98.5]
[99.9 –99.999]
[0.1 –25] Downlink/
[2] Uplink
(note)
[0.1]
[99.9]
up to [120]
Low (≈ [10])
[99]
[1]
NOTE: When using base station on board HAPS for NTN, end-to-end latency should be [1 – 10] ms (operating at 20 km altitude) and user experienced data rate should be up to [500] Mb/s for DL and [50] Mb/s for UL to enable eMBB service.
8.3 Use case on enhanced user experience with sparse LEO satellite deployment
8.3.1 Description
Different from terrestrial cellular and GEO deployment, the LEO satellite has a moving coverage which requires a new deployment thinking. A satellite operator may have a launch plan for a large size LEO constellation. However, the deployment of complete LEO constellation is a long-term task which needs several years or even longer. The deployment is both time consuming and costly. According to the target service area, a satellite operator may choose to sequence the deployment of satellites, which helps to provide the continuous service to the user of target service area as early as possible. The satellite operator can start the commercialization of the constellation once the number of the satellites is sufficient instead of waiting until the whole constellation has been deployed completely. Even with the un-completed deployed constellation, the satellite operator should guarantee the uninterrupted service for the users. To achieve this, each satellite of the constellation should provide larger coverage compared with the case that the LEO satellites are densely deployed.
With the sparse LEO satellite deployment, basic services (e.g. voice calls, short message, message with picture, etc.) should be supported in order to satisfy the user’s requirements. Especially for emergency scenarios, the picture can provide more useful information than the text and voice to help on the emergency rescues. Also the multiple services are assumed to be supported by multiple type of devices, including phones, smart watch and other wearables. Especially the supporting of the wearables will greatly increase the possibility that user can use this service during the emergency situations.
User experience is always an important aspect for the operator network. The use manners have great impact on the user experience. Current satellite communication for smart phones and wearables, the user assistance is required, e.g. the user needs to follow the introduction of the satellite alignment and during the service, the user needs to point the UE to the satellite’s direction. These extra requirements bother the user and may degrade the user experience especially in emergency scenarios, when user is usually very nervous. This use manner should be improved in 6G and enhance the user experience.
The satellite communication is expected to provide emergency service, during which the user location needs to be provided in compliance with the regulatory requirements. Therefore, the location service should also be supported even under the sparse LEO satellite deployment.
8.3.2 Pre-conditions
Satellite Operator A has launch plans for a large-size constellation of LEO satellite. Alice has subscribed to the ‘UE- direct-to-satellite service’ of Satellite Operator A and both her smartphone and smart watch are capable of directly connecting with Satellite Operator A's satellites.
8.3.3 Service Flows
1. Satellite Operator A plans to deploy a large-size LEO constellation step by step.
2. Before the deployment of the large-size LEO constellation is complete, Satellite Operator A decides to provide connectivity service with continuous coverage to users when the sparse constellation with sufficient satellites (e.g. several hundreds) for the target service area has been deployed. Due to the sparse deployments, each satellite needs to support sufficiently long-range coverage.
3. Alice is going on a hiking in a mountain area with her smartphone and smart watch. She noticed that there is no terrestrial cellular network coverage and her smartphone and smart watch have switched to the Satellite Operator A's satellite network. When she is on the way, she puts the smartphone in the pocket. Suddenly, the smart phone is ringing. She takes it from the pocket and it is a call from her mother. She answers the call and talks with her mother for several minutes. During the call, there is no interruption since the satellites provide continuous coverage. Alice can save time by not manually following the satellite alignment instruction provided by the smartphone thanks to the smartphone’s capability to support of autonomously pointing to the satellite.
4. After walking for a long time, Alice takes a break, and she browses the web to search for some information on the hiking route using her smartphone. There is also no interruption during the internet access. After the break, Alice continues hiking. However, due to her carelessness, Alice loses her smartphone at the place where she took a break. It takes some time to get her smartphone back. To avoid her mother’s worries, Alice uses her smart watch to send a message to her mom. When Alice meets an emergency condition, Satellite Operator A’s satellite network can provide location service meeting the regulatory requirements.
5. After years’ deployment, the deployment of the LEO constellation is gradually complete. The newly launched satellite and the satellites already deployed can improve the user experience with easy configuration extension.
8.3.4 Post-conditions
Thanks to the support of the service using LEO satellites access with sparse satellite deployment, Satellite Operator A can start the commercial usage of the constellation even though the constellation is sparse, while the users can have a good experience of the satellite communication in the constellation's early deployment stage, including without requiring the user to manually follow the satellite alignment instruction. The sparse LEO constellation can provide basic services to the user (including voice, short message, and picture message, etc.) and multiple types of the UE can be supported (including smartphone, wearables and so on). With the step-by-step deployment, the user experience can be smoothly improved.
8.3.5 Existing features partly or fully covering the use case functionality
Clause 6.46 of TS 22.261 [14] defines the requirements for 5G systems with satellite access. Clause 7.4 of TS 22.261 [14] defines the KPIs for 5G systems with satellite access. This use case proposes new requirements for satellite access in case of sparse LEO satellite constellation.
8.3.6 Potential New Requirements needed to support the use case
[PR 8.3.6-1]: The 6G system with satellite access shall optimize the coverage per LEO satellite.
[PR 8.3.6-2]: The 6G system with satellite access shall support service continuity when UE is within the optimized coverage of the LEO constellation, e.g. in the sparse LEO scenarios.
[PR 8.3.6-3]: The 6G system with satellite access shall be able to support flexible configurations (e.g. parameters for satellite access) for both sparse satellite constellation and dense satellite constellation.
[PR 8.3.6-4]: The 6G system with satellite access shall be able to provide the location service meeting the regulatory requirements for both sparse satellite constellation and the dense satellite constellation.
8.4 Use case on service continuity for wearable mobile devices
8.4.1 Description
Reelika has just started a famous ultra-trail "Grand Raid des Pyrénées" in the Pyrénées mountains between France and Spain. She is equipped with wearable mobile devices including a wrist watch, a forehead mounted camera and a smart phone in order to share in real time her position, her health conditions and possibly voice message or short videos with her friends Guillaume and Didier in Paris but also with her coach Loïc waiting for her at a check point in the middle of the race in a remote area without mobile access coverage. Reelika expects that her personal data (e.g. position, health conditions, etc.) are protected.
As she starts, her mobile device is first served by the cellular network but then when climbing the first mountain, she reaches the edge of network coverage. Throughout the race, her wearable devices are transferred from cellular to satellite access whenever the coverage of the terrestrial network disappears.
Her friends do not perceive the transition between both access technologies (i.e. no packet loss for non real time service and no interruption for real time service). However, they may perceive that the quality of the image/voice is adapted to varying available bandwidth and latency.
The race organisers as well as all her friends including Loïc, regularly receive the position and health conditions of Reelika. At some point Loïc detects that Reelika is getting very tired and cannot progress. He decides to send her a short message to cheer her up. Reelika’s wearable mobile device receives the text message and converts it to voice. Reelika listens to the message and thanks him with a video message.
During the night, Reelika calls and reports to the race organisers that she is with a runner who injured himself while accidentally falling in a pit. The race organisers detect that Reelika may have crossed the Spanish border and launches a request for reliable positioning service in order to determine whether the French or the Spanish public safety organisation should intervene. Reelika's reported position is confirmed to be in Spain and hence the race organizers initiate an emergency call to the Spanish public safety organisation that is in contact with the Spanish first responder who fortunately is not far from the accident. He is able to call Reelika for additional location guidance.
Reelika is able to continue her race leaving the injured runner in good hands.
As Loïc is monitoring the progress of Reelika, it starts raining and Loïc decides to enter the large shelter where the runners will find some assistance. The rain is so dense that the race organisers decide in coordination with the public safety officials to alert the runners and all the followers along the track about possible slippage of terrain. An alert message is broadcast to the area with the intent to reach the maximum number of persons, including the ones in indoor conditions. With this alert message, public safety officials request all runners to report their position every 10 minutes during the next two hours. Loïc (indoor), Reelika and all race participants/followers in the area receive this alert message.
At a certain point in the race, Reelika sets off on a bad track, due to the de-tagging by local activists. The monitoring system of the race organizer quickly detects that she is going off the planned track. She is immediately alerted with an alert message that triggers an alarm sound in her wrist watch. She consults her wearable device to learn where she lost the planned track and how she can rejoin it.
The race organizers inform the race participants behind Reelika about the virtual track to rejoin the planned track as well as in field member of the race organization to correct the track. This prevents late runners from losing time with this track issue and no one need to be sent after lost runners.
Fortunately, Reelika can successfully complete her race without further technical incident and all her friends join in a video conference to celebrate with her.
8.4.2 Pre-conditions
Reelika's wearable mobile devices are served by access to a cellular network.
8.4.3 Service Flows
1. From Reelika (cellular or satellite access) to the race organizers (cellular access), to Guillaume/Didier (cellular access) and to Loïc (satellite access): messaging (including reported position), non-real time and real time services.
2. From Reelika (cellular or satellite access) to Loïc (satellite access): messaging services.
3. From Loïc (satellite access) to Reelika (cellular or satellite access) messaging services.
4. From Reelika (satellite access) to race organizers (cellular access): call (including reported position).
5. From race organiser (cellular access) to Reelika (satellite access): reliable positioning service (network based).
6. Between race organizer (cellular access) and Spanish public safety headquarters (cellular access) and subsequently between the Spanish public safety headquarter and a first responder (satellite access): calls (during which reported positions of both Reelika and 1st responder can be exchanged and compared).
7. From race organizer (cellular access) to Reelika (satellite access) and Loïc (satellite access, indoor): public warning.
8. From in field first responder (satellite access) and to public safety headquarter (cellular access): reported position.
9. From race organisers to Reelika (satellite access) and following runners (satellite access): alert about off track and guidance to re-join the planned track.
10. Between Reelika (cellular access), Guillaume/Didier in France (cellular access) and Loïc (satellite access): real time services (video conference).
8.4.4 Post-conditions
Reelika's wearable mobile devices are served by the cellular access.
8.4.5 Existing features partly or fully covering the use case functionality
In TS 22.261 [14] one can read:
Clause 6.46.3 Service continuity
For a 5G system with satellite access, the following requirements apply:
- A 5G system with satellite access shall support service continuity between 5G terrestrial access network and 5G satellite access networks owned by the same operator or owned by different operators having an agreement.
- Subject to regulatory requirements and operator's policy, a 5G system with satellite access shall support service continuity (with minimum service interruption) for a UE engaged in an active communication, when the UE changes from a direct network connection via 5G terrestrial access to an indirect network connection via a relay UE (using satellite access) and vice-versa.
Clause 6.41.2.9 Regulatory Services
A hosting network using the 5G system shall be able to support regulatory services (e.g. PWS, LI, and emergency calls), based on regional/national regulatory requirements.
In the above, three aspects have not been clearly specified:
Service continuity with minimum interruption time between satellite and terrestrial (mobile) access
The density of UE supported by satellite access for SMS
The support of PWS via satellite for UE also in light indoor conditions
8.4.6 Potential New Requirements needed to support the use case
[PR 8.4.6-1]: The 6G system shall be able to ensure service continuity with minimum interruption for UEs during the transition between terrestrial and satellite access and vice versa.
[PR 8.4.6-2]: The 6G system with satellite access, shall support SMS delivery to a high density of UEs i.e. up to [1000] UE per km2.
[PR 8.4.6-3]: Subject to regulatory requirements, the 6G system with satellite access, shall be able to support PWS [62] for broadcasting warning notifications to UEs in adverse propagation conditions e.g. light indoor conditions, dense forest.
8.5 Use case on resilient positioning in satellite networks
8.5.1 Description
Positioning, Navigation and Timing (PNT) is fundamental for a wide range of sectors, such as transportation, critical infrastructure (e.g. 6G system) and regulatory services (e.g. emergency services, LI). Nowadays, GNSS is the backbone technology to fulfil these PNT needs. However, the dependency of these sectors on GNSS certainly results in major threats and risks, in case of unavailability or disruption of GNSS, with severe safety, legal and economic impacts. The resilient positioning use case is based on the demand to withstand and recover rapidly from PNT disruptions anywhere.
In addition, the 6G system may assume the need to obtain the UE location with a valid GNSS solution for the operation of 6G NTN solutions. In such situations, the degradation or unavailability of GNSS positioning, such as due to jamming, spoofing or obstructions, may lead to a degradation or loss of the 6G NTN communication capability, as it may currently occur in 5G NTN operations. For 6G NTN, there may be alternative communication approaches, but if these approaches are limited or not successful, a 6G integrated communication and positioning system can provide a GNSS-independent positioning solution for 6G NTN communications.
The 6G NTN positioning service is expected to exploit multiple satellites in view to provide accurate positioning. These satellites in view can belong to single or multiple orbits (e.g. LEO and GEO constellations) from one or more operators.
The satellite network operator of the 6G system is then able to provide the resilient positioning service, by ensuring the availability of 3GPP positioning technologies for the service area, and independently of the availability of non-3GPP technologies (e.g. GNSS). This resilient positioning service can then be exploited by any kind of 6G user (e.g. aircrafts and drones [25]) that may experience GNSS degraded service.
8.5.2 Pre-conditions
The UE has satellite network coverage (e.g. LEO constellation) in the 6G system.
The UE can be unregistered or registered (e.g. in idle or connected mode) to the satellite network.
The UE can be under the coverage of multiple or multi-orbit satellite networks (e.g. LEO and GEO constellations) deployed by one or more operators and have subscription(s) for accessing these satellite networks.
The environment of use is outdoor static or moving with coverage provided by the satellite network(s).
The UE is equipped with a native 6G communication and positioning function. The positioning function relies on 3GPP technologies delivered by the satellite network, to avoid any dependence on the availability of non-3GPP technologies.
8.5.3 Service Flows
1. The 3GPP positioning service provided by the satellite network can be initiated by a third party or by the end-user.
2. This 3GPP positioning service can determine the UE location with a certain resilient accuracy level.
3. When triggering the 3GPP positioning service, if the UE fails to obtain the location data within a time limit or the location data is not accurate enough, the UE can select different satellite networks or improve the accuracy of location data combining multiple satellite networks.
4. The UE location is used to provide the resilient positioning service.
5. The UE can report its UE location and positioning accuracy level.
8.5.4 Post-conditions
The user or third-party entity (e.g. a PSAP) successfully uses the resilient positioning service.
8.5.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] clause 6.27.1 includes the following description:
5G positioning services aims to support verticals and applications with positioning accuracies better than 10 meters, thus more accurate than the ones of TS 22.071 [24] for LCS. High accuracy positioning is characterized by ambitious system requirements for positioning accuracy in many verticals and applications, including regulatory needs.
TS 22.261 [14] clause 6.27.2 includes the following positioning requirements:
The 5G system shall provide different 5G positioning services, supported by different single and hybrid positioning methods to supply absolute and relative positioning.
NOTE 2: hybrid positioning methods include both the combination of 3GPP positioning technologies and the combination of 3GPP positioning technologies with non-3GPP positioning technologies such as, GNSS (e.g. Beidou, Galileo, GPS, Glonass), Network-based Assisted GNSS and High-Accuracy GNSS, Terrestrial Beacon Systems, dead-reckoning sensors (e.g. IMU, barometer), WLAN/Bluetooth-based positioning.
The 5G system shall support mechanisms to determine the UE's position-related data for period when the UE is outside the coverage of 3GPP RAT-dependent positioning technologies but within the 5G positioning service area (e.g. within the coverage of satellite access).
However, these requirements do not ensure resilience of the 3GPP positioning technologies in satellite networks, independently of the availability of non-3GPP technologies (e.g. GNSS).
TS 22.261 [14] clause 7.3.2.1 includes the following performance requirements:
The 5G system shall support the combination of 3GPP and non-3GPP positioning technologies to achieve performances of the 5G positioning services better than those achieved using only 3GPP positioning technologies.
NOTE 1: For instance, the combination of 3GPP positioning technologies with non-3GPP positioning technologies such as GNSS (e.g. Beidou, Galileo, GLONASS, and GPS), Terrestrial Beacon Systems (TBS), sensors (e.g. barometer, IMU), WLAN/Bluetooth-based positioning, can support the improvement of accuracy, positioning service availability, reliability and/or confidence level, the reduction of positioning service latency, the increase of the update rate of the position-related data, increase the coverage (service area).
NOTE 2: The combination can vary over time to optimise the performances, and can be the combination of multiple positioning technologies at the same epoch and/or the combination of multiple positioning technologies at different epochs.
TS 22.261 [14] clause 7.3.2.2 includes the following requirements for horizontal and vertical positioning service levels:
The 5G system shall be able to provide positioning services with the performances requirements reported in Table 7.3.2.2-1.
However, these performance requirements are not fully independent of non-3GPP positioning technologies, since a combination of 3GPP and non-3GPP positioning technologies can be used to fulfil these performance requirements.
TS 22.261 [14] clause 6.46.2 includes the following requirements related to the determination of the UE location:
A 5G system with satellite access shall be able to determine a UE's location in order to provide service (e.g. route traffic, support emergency calls) in accordance with the governing national or regional regulatory requirements applicable to that UE.
NOTE: This is also applicable for UE using only satellite access. The determination of a UE’s location can be based on 3GPP and/or non-3GPP positioning technologies subject to operator’s policies.
However, no performance requirements are defined on determining the UE location subject to regulatory requirements and operators' policies.
8.5.6 Potential New Requirements needed to support the use case
[PR 8.5.6-1] The 6G system with satellite access shall be able to provide the UE with positioning service with 3GPP technologies, independently of non-3GPP positioning technologies (e.g. GNSS) with the following KPIs:
Table 8.5.6-1: Performance requirements for satellite-based positioning services
Scenario
(note)
Accuracy
(95 % confidence level)
Positioning service availability
Positioning service latency
Environment of use
UE speed
UE type
Horizontal Accuracy
Vertical Accuracy
Airplane en-route
[50] m
[50] m
[99 %]
[1] s
Outdoor
Up to [1500] km/h
Airplane mounted
Airplane landing
[10] m
[10] m
[99 %]
[1] s
Outdoor
Up to [350] km/h
Airplane mounted
UAV positioning
[1] m
[1] m
[99 %]
[1] s
Outdoor
Up to [160] km/h
UAV mounted
NOTE: Multiple satellites can be used to support 3GPP positioning technologies.
[PR 8.5.6-2] Subject to regulatory requirements, and operator policy, and to user’s consent, the 6G system with satellite access shall be able to determine the UE location.
8.6 Use case on disaster relief
8.6.1 Description
The national meteorological centre has detected an upcoming storm with heavy rains which are likely going to cause a major flood in the Valencia region. The alert is propagated to the public safety organisations as well as to the population.
All first responders are equipped with a set of wearable devices (i.e. handheld, bodycam, vital signs monitoring sensors, …) and some with drones with regular and thermal (infrared) cameras.
The storm and floods created power supply outages in the region that resulted in the loss of terrestrial cellular network connectivity. Thanks to the available satellite access, the national public safety organisation is guiding first responders that are already on site (most are volunteers among the population). Additional first responder teams are deployed, each with their all-terrain vehicles or amphibious vehicles in the harbour. The remaining bandwidth of the satellite access can be used by the population to exchange messages.
Quickly, it is decided to ask assistance from public safety and disaster relief organisations from neighbouring countries.
Local access networks are mounted on each land vehicle with enabled satellite connectivity. Each local access network can be used for communication between the team members, with their headquarters (area, regional, national) as well as with other responder teams. Even civilians could exploit this connectivity if sufficient remaining bandwidth is available. When the wind bursts subside, drones are used by team members to assess the disaster, report to headquarter(s) and rescue the population.
As the flood took place in the coastal area, casualties are being spread to the sea and therefore, public safety organisations are deploying boats, each equipped with an on-board local access network and satellite connectivity.
Responders or drones may have to move beyond the coverage of a local access network. In such cases continuity of service is ensured through smooth transition to the satellite access. Several local access networks can be directly connected via satellite to ease the coordination between the teams. User equipment belonging to national first responders can seamlessly communicate with user equipment appertaining to responders of the neighbouring countries.
During the recovery phase, an HIBS (base station on board a HAPS) can be launched to increase the available capacity over the area before the terrestrial base stations are repaired.
8.6.2 Pre-conditions
The base stations of the terrestrial network have been flooded which created a major failure. The terrestrial network is down and cellular service is not available in the area.
8.6.3 Service Flows
Given that the terrestrial network is down, all UEs will look for alternative available networks in the area, that is the satellite access and later the HAPS based access network.
Satellite or HAPS based access network can be used to support:
- Public warning service to the entire population (including 1st responder's and volunteers) in the impacted area (also in adverse propagation conditions such as light indoor),
- Non real time and real time services to pedestrians or drone mounted UEs,
- Backhaul connectivity to vehicle/boat mounted local access point,
- Connectivity (without usage of satellite feeder link) between two local access points.
The satellite or HAPS based access bandwidth can be pre-empted for public safety organisations but the remaining bandwidth if available may be used by the population only for messaging.
Vehicle/boat mounted local access points can be used
- To serve pedestrian or drone mounted UEs,
- To support UE to UE connectivity.
8.6.4 Post-conditions
The cellular network is restored and operational again.
8.6.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] already includes the following relevant requirements:
Clause 6.46.7 Satellite and Relay UEs:
- A 5G system with satellite access shall be able to support relay UEs with satellite access.
Clause 6.46.9 UE-Satellite-UE communication:
- Subject to regulatory requirements and operator's policy, a 5G system with satellite access shall support UE-Satellite-UE communication regardless of whether the feeder link is available or not.
- Subject to regulatory requirements and operator's policy, a 5G system with satellite access shall be able to provide QoS control of a UE-Satellite-UE communication.
- Subject to regulatory requirements and operator's policy, a 5G system with satellite access shall be able to support different types of UE-Satellite-UE communication (e.g. voice, messaging, broadband, unicast, multicast, broadcast).
Clause 7.4.2 Requirements
A 5G system providing service with satellite access shall be able to support GEO based satellite access with up to 285 ms end-to-end latency.
NOTE 1: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system providing service with satellite access shall be able to support MEO based satellite access with up to 95 ms end-to-end latency.
NOTE 2: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system providing service with satellite access shall be able to support LEO based satellite access with up to 35 ms end-to-end latency.
NOTE 3: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system shall support negotiation on quality of service taking into account latency penalty to optimise the QoE for UE.
The 5G system with satellite access shall support high uplink data rates for 5G satellite UEs.
The 5G system with satellite access shall support high downlink data rates for 5G satellite UEs.
The 5G system with satellite access shall support communication service availabilities of at least 99,99 %.
Table 8.6.5-1: Performance requirements for satellite access (Table 7.4.2-1 from [14])
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
(note 1)
Area traffic capacity
(UL)
(note 1)
Overall user density
Activity factor
UE speed
UE type
Pedestrian
(note 2)
[1] Mbit/s
[100] kbit/s
1,5 Mbit/s/km2
150 kbit/s/km2
[100]/km2
[1,5] %
Pedestrian
Handheld
Public safety
[3,5] Mbit/s
[3,5] Mbit/s
TBD
TBD
TBD
N/A
100 km/h
Handheld
Vehicular connectivity
(note 3)
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
50 %
Up to 250 km/h
Vehicle mounted
Airplanes connectivity
(note 4)
360 Mbit/s/ plane
180 Mbit/s/ plane
TBD
TBD
TBD
N/A
Up to 1000 km/h
Airplane mounted
Stationary
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
N/A
Stationary
Building mounted
Video surveillance
(note 4a)
[0,5] Mbit/s
[3] Mbit/s
TBD
TBD
TBD
N/A
Up to 120 km/h or
stationary
(note 4b)
Vehicle mounted or fixed installation
Narrowband IoT connectivity
[2] kbit/s
[10] kbit/s
8 kbit/s/km2
40 kbit/s/km2
[400]/km2
[1] %
[Up to 100 km/h]
IoT
NOTE 1: Area capacity is averaged over a satellite beam.
NOTE 2: Data rates based on Extreme long-range coverage target values in clause 6.17.2. User density based on rural area in Table 7.1-1.
NOTE 3: Based on Table 7.1-1.
NOTE 4: Based on an assumption of 120 users per plane 15/7.5 Mbit/s data rate and 20 % activity factor per user
NOTE 4a: Refer to video surveillance data transmitted (in UL) from a UE on the ground (e.g. picture or video from a camera) using satellite NG-RAN to connect to 5GC, and video surveillance-related configuration or control data sent (in DL) to the UE/device. 0.5 Mbit/s for DL experienced data rate is based on MAVLINK protocol that is widely used for UAV control. 3 Mbit/s for UL experienced data rate is based on the assumed sum from 2.5 Mbit/s for video streaming and 0.5 Mbit/s for data transmission.
NOTE 4b: Up to 120 km/h applies to vehicle mounted while stationary applies to fixed installation.
NOTE 5: All the values in this table are targeted values and not strict requirements.
NOTE 6: Performance requirements for all the values in this table should be analysed independently for each scenario.
=> What is not yet covered in 5G using satellite access are:
- Other Size, Weight and Power (SWAP) constrained moving platforms (e.g. flying drone, small vessels mounted) compared to vehicle.
- Relay UE-Satellite-Relay UE communication.
8.6.6 Potential New Requirements needed to support the use case
[PR 8.6.6-1]: The 6G system with satellite access, shall be able to provide eMBB service to UE mounted on SWAP constrained moving platforms (e.g. flying drone, small vessels mounted).
[PR 8.6.6-2]: The 6G system with satellite access shall be able to provide eMBB service with the following KPIs:
Table 8.6.6-1: KPIs of the 6G satellite access network to support the use case disaster relief [112]
End-to-end Round Trip Delay [ms]
Service link (Radio link Availability)
[%]
Reliability
[%]
Peak data rate
[Mb/s]
Connection density
[devices/km2]
Mobility [km/h]
Location accuracy
[m]
Positioning availability
[%]
Positioning latency
[s]
UE Type
Up to [600]
Up to 99.9
[99.9 –99.999]
Up to [20] Downlink / Up to [2] Uplink
Up to [100] for SMS only
3
Low (≈ [100])
Up to [99]
[1]
Handheld
[PR 8.6.6-3]: Subject to regulatory requirements, the 6G system with satellite access, shall support text messaging services to/from UEs in adverse propagation conditions e.g. indoor or outdoor conditions.
8.7 Use case on low-energy positioning in satellite networks
8.7.1 Description
This use case corresponds to a capability for 6G with satellite access to provide low-energy positioning for low-power and low-complexity devices (e.g. IoT and low-power devices) when served by a satellite network. Integrating GNSS into IoT devices operating over satellite networks significantly impacts battery consumption, especially in battery-powered UEs requiring frequent positioning updates. The extent of this impact varies based on GNSS start modes (hot, warm, or cold), power consumption of GNSS modules, reporting intervals, and device mobility. According to the analysis carried out in [26], GNSS position fixes can reduce battery life from approximately 5% up to 45% in cases of frequent position fixes. Mitigating the power consumption due to GNSS would therefore be beneficial. To minimize energy consumption, NTN signals can be used for position estimation, reducing dependency on GNSS for position information updates.
Integrating NTN-based positioning could be particularly advantageous for applications such as asset tracking in logistics, where devices like containers, wagons and pallets typically operate over extended periods with limited power resources, as outlined in TS 22.261 [14]. While specific performance targets for battery life, message size, and reporting frequency are defined (See Table G.2-1 of “Annex G” in TS 22.261 [14]), positioning accuracy is not currently addressed. The horizontal positioning accuracy target and positioning service availability is expected to be aligned with [338].
8.7.2 Pre-conditions
A battery-powered IoT or low-power device has been deployed in an environment where it obtains its initial position and synchronization from non-3GPP methods (e.g. GNSS) and / or terrestrial networks. A combination of non-3GPP technologies (e.g. GNSS) and 3GPP technologies, terrestrial and non-terrestrial, can be configured if compatible with energy consumption requirements.
8.7.3 Service Flows
1. At some point, the IoT or low-power device may lose its original positioning sources due to one of the following reasons:
- GNSS-originated position: GNSS signal may be lost due to interference, or obstruction, or intentionally disabled to augment its autonomy.
- Terrestrial network-originated position: the IoT or low-power device may have moved beyond terrestrial network coverage, such as in the case of asset tracking sensor installed on a container to be transported by sea, rail, or road.
In this context, it requires the IoT or low-power device to refresh a more accurate position. The IoT or low-power device, served by the satellite access, is expected to know an approximate position from its original positioning.
Therefore, the IoT or low-power UE initiates the 3GPP positioning method over satellite to estimate its current location.
The 6G system broadcasts to the UE via its satellite access at least the following necessary information, including but not limited to:
- Network assistance data: Satellite ephemeris and additional assistance data to improve accuracy (e.g. ionospheric models to correct the atmospheric delay errors, etc.)
- Reference signal for time of arrival measurements.
8.7.4 Post-conditions
The IoT or low-power device successfully estimates/updates its position based on information provided by the 6G system via the satellite access.
8.7.5 Existing features partly or fully covering the use case functionality
Ranging signals and positioning methods for IoT and low-power applications are defined in TS 36.211 [45] and TS 38.211 [46] and TS 36.305 [47] and TS 38.305 [48], respectively, but only for terrestrial networks. They need to be adapted to satellite access context.
The functional and performance requirements for asset tracking use cases reported in the below “Table G.2-1: Battery life expectancy and message size to support example use cases for asset tracking” of “Annex G (informative): Asset Tracking use cases” in TS 22.261 [14] do not include positioning requirements.
Table 8.7.5-1: Battery life expectancy and message size to support example use cases for asset tracking (Table G.2-1 in [14])
Scenario
Battery Life
Expectancy
(note 1)
Typical Message size
Maximum
Message size
Typical Frequency
(number of messages per day)
Typical
Battery
Capacity
Device Density
1
Containers
(note 2)
12 years
200 bytes
2500 bytes
24
21,6 Wh
1,4 devices / m3
2
Wagons
20 years
200 bytes
2500 bytes
24
36 Wh
0,3 devices / m2
3
Pallets
7 years
300 bytes
300 bytes
24
12 Wh
4 devices/ m2
NOTE 1: Battery life expectancy is to be assumed in all coverage conditions and is based on typical message size value and typical frequency
NOTE 2: A large containership with a mix of 20 ft and 40 ft containers is assumed.
NOTE 3: All the values in this table are targeted values and not strict requirements.
8.7.6 Potential New Requirements needed to support the use case
[PR 8.7.6-1]: The 6G system with satellite access shall be able to provide a mechanism for energy constrained devices to determine its position using 3GPP positioning technology.
[PR 8.7.6-2]: The 6G system with satellite access shall be able to provide positioning service with the following KPIs:
Table 8.7.6-1: Performance requirements for satellite-based positioning services for energy constrained devices from [338]
Scenario
Battery Life
Expectancy
(note)
Horizontal Accuracy
(95 % confidence level)
Positioning service availability
Environment of use
UE speed
UE type
Containers
12 years
[100] m
[95 %]
Outdoor
Up to [37] km/h
Container mounted
Wagons
20 years
[100] m
[95 %]
Outdoor
Up to [350] km/h
Train mounted
Pallets
7 years
[100] m
[95 %]
Outdoor
Up to [100] km/h
Vehicle mounted
NOTE: Battery life expectancy is to be assumed in all coverage conditions and is based on typical message size value and typical frequency, e.g. the number of messages per day by a typical user
8.8 Use case on global mobile video
8.8.1 Description
Many places and people are currently underserved when it comes to Mobile Broadband (MBB) services. From the user's and society's point of view a lot is gained already with a basic internet connection, since many internet services can be delivered with a fairly moderate bitrate, and the most important needs would be met already with a low activity factor. Therefore, the problem is mainly related to providing remote service coverage for basic MBB. Still, such basic services may be the basis of sensitive systems (e.g. related to health or surveillance) and therefore uninterrupted and resilient operation is important.
The use case global mobile video is about provisioning access to basic broadband services, exemplified by the capability to make a video call, at remote places on earth where people live or work, using handheld-type of 3GPP devices. A full global area coverage (e.g. 99.9 %) needs to be provided through satellite access. However, terrestrial cellular networks need to handle more populated areas, for instance through very large cells, such that the total traffic to be handled by satellite networks is not too high.
Based on population density, this can be divided into remote and deep rural scenarios.
1) Remote scenario
Covering virtually all people on earth, and virtually all areas including the oceans. In most cases very sparsely populated areas (approximately < 1 person/km2). The expectation is that this scenario is covered mainly by NTN access. A lower rate, activity factor, and area traffic can be supported in these areas. The global mobile video service might be delivered with reduced quality compared to the Deep Rural scenario.
2) Deep Rural scenario
A deep rural scenario represents sparsely populated areas (approximately 1-10 persons/km2). The expectation is that this scenario is mainly covered by cellular access, from large macro cells to very large "boomer" cells, and in addition have satellite coverage to fill gaps between the terrestrial cells. A higher rate, activity factor, and area traffic can be supported in these areas.
Sustainability impact analysis:
Energy resources: Increased energy consumption due to the buildout of networks.
Material resources: The material need increases when building out networks with new 6G sites and satellites. Material resource depletion increases as materials sent into space is considered not recyclable.
Emissions: Internet access provides the possibility for various activities (e.g. bank, hospital, work) leading to less travel needs and thereby reduced emissions. Emissions related to producing and operating new equipment can be somewhat mitigated.
Biodiversity and land use: New sites, especially high tower 6G sites, increase the land use for Information and Communication Technologies (ICT).
Education: Internet access enables access to remote educational material.
Health: Global internet could enable using remote healthcare/first aid leading to better healthcare access in rural areas.
Inclusion and Equality: Providing the possibility of internet access facilitating banking, healthcare and education services is positive from an inclusion point of view. However, it is important not to leave anyone behind and thereby risk widening the gap.
Trustworthiness: Increased resilience for ICT services could be provided by satellites included in the use case as back up in unforeseeable situations.
Work and income: This use case could enable the possibility of running businesses from everywhere.
Infrastructure: Increasing the access to internet is the main goal of the use case.
8.8.2 Pre-conditions
UE is capable of connecting to satellite access. For high accessibility this should be achieved with mainstream UEs.
8.8.3 Service Flows
1. User initiates or receives video call to a handheld device to/from another user.
2. If cellular access is available the traffic is supported by the cellular network, otherwise satellite access is used.
3. The call is sustained uninterrupted, also under mobility, until either user terminates the call.
8.8.4 Post-conditions
The user is able to use uninterrupted internet services, such as video communication, regardless of position and for an extended period.
8.8.5 Existing features partly or fully covering the use case functionality
- 5G Advanced specifies a Non-Terrestrial Network component.
- 5G Terrestrial Network.
8.8.6 Potential New Requirements needed to support the use case
[PR 8.8.6-1] The 6G system with satellite access shall support service continuity within NTN, and between TN and NTN.
[PR 8.8.6-2] Following KPI's should be achieved for the global mobile video service.
Table 8.8.6-1: KPIs for global mobile video
Scenario
User experienced rate
Population density
Area traffic
1
Remote area
(NOTE 1)
DL: [5] Mbps
UL: [1] Mbps
[< 1] person/km2
DL: [up to 50] kbps/km2
(NOTE 2)
2
Deep Rural area
(NOTE 1)
DL: [10] Mbps
UL: [2] Mbps
[1-10] person/km2
DL: [1] Mbps/km2
(The area traffic is here split between two operators,
with [2] Mbps/km2 in total)
(NOTE 3)
NOTE 1: Reduced video quality is acceptable in the remote areas and deep rural area, see chapter 3.9.1.3 and 3.9.1.5 in [31].
NOTE 2: The DL area traffic corresponds to an activity factor of 2 % with an average user density of 0.5 person/km2.
NOTE 3: The DL area traffic corresponds to an activity factor of 4 % with an average user density of 5 persons/km2 equally shared by two operators.
8.9 Use case on low-altitude logistics supported by NTN
8.9.1 Description
For remote regions with inconvenient transportation, complex terrain and rapidly changing weather, traditional logistics delivery methods struggle to meet the needs of local residents. By utilizing UAVs and other low-altitude aircraft, the low-altitude logistics can overcome limitations posed by terrain and other factors to swiftly deliver the medicine, food, and emergency supplies to the residents living in such remote regions, which exhibits its tremendous potential and advantages.
The development and operation of the low-altitude economy heavily rely on robust communication networks to ensure the safety and reliability of UAV systems. However, depending solely on traditional terrestrial networks (such as 4G/5G cellular networks) presents several critical challenges, for example:
The density of ground base station deployments is limited, particularly in remote areas or challenging terrains such as mountains or forests. This can lead to insufficient or unstable signal coverage, limiting the expansion of the low-altitude economy into these regions.
UAVs often operate in environments with high mobility and dynamic changes. Ground networks, which are primarily designed to serve relatively stationary or slowly moving objects, will struggle to maintain stable connections and experience higher data transmission delays.
The coverage height of ground base stations is limited. This can lead to the flight range of medium and large-size UAV being restricted, especially in long-distance, long-haul and heavy-load flight mission. Refer the latest CHINA airspace classification [99], the low altitude area is below 3000m, where the low-altitude UAV could fly.
To address these challenges, the low-altitude economy relies on NTNs, such as LEO satellite networks, HAPS, and other space-based or non-conventional communication infrastructures. NTN can offer broader and reliable coverage, accommodate high-dynamics flight scenarios, and provide enhanced security and regulatory capabilities.
Additionally, in some challenging environment, such as steep mountains, deep valleys, and dense forests, it brings significant difficulties for UAV flight. The rapidly changing weather conditions in remote mountainous regions, such as sudden strong winds, rain, or fog, further complicate UAVs’ operations. An example is showed in Figure 8.9.1-1, some UAVs flight over the area 1 are affected by the heavy rain, while the UAVs flights over the area 2 are affected by the dense fog. Usually, UAVs can be equipped with camera and sensors to monitor the visible environment and process the collected sensor data locally or transmit them back to the AS on the ground for collaborative processing with the data from other UAVs, and/or assisted information about the flight paths from 3rd party (e.g. Meteorological Bureau, which keeps monitoring the weather and forecasting for the whole region based on distributed meteorological monitoring station on the ground and onboard satellites). For the varying environment, the UAV need to adjust the flight paths and associated operations regarding the weather prediction, which raises demands on the round-trip delay of sensing result. Under such scenarios, the 6G networks with satellite access cannot only provide satellite-based communication but also provide satellite onboard computing resources to facilitate non-3GPP sensing data processing (e.g. weather condition, obstacle detection) for UAVs to assist optimizing flight paths and ensure task operation.
Figure 8.9.1-1: Illustration of logistics delivery in remote mountain areas
8.9.2 Pre-conditions
To improve efficient delivery, Logistics Company A bought a large number of UAVs for the delivery, and it has a dispatch centre responsible for scheduling and optimizing the flight route of UAVs. All of the UAVs have already achieved flight permission from local aviation authority.
The UAVs are configured with sensors (e.g. camera, radar) and a UE.
Logistics Company A has received many delivery orders toward the remote region A from different customers, and one order is from the customer Alice to deliver the medicine to Jack’s home.
MNO B has deployed a 6G network with not only terrestrial access but also satellite access to provide the reliable communication service in this remote region A.
Besides the communication service, the 6G network with satellite access is equipped with the service hosting environment, which can process the sensing data (e.g. high-resolution image data), as well as performing the intelligent analysis of sensing data directly on the satellite.
Logistics Company A has subscribed to the communication service, and computing service from MNO B for its UAVs.
8.9.3 Service Flows
Logistics Company A inputs all delivery tasks of this remote region into the dispatch centre, different delivery tasks have different targeted locations in this region. Then the dispatch centre selects some UAVs and distributes a suitable initial route and delivery task for each UAV taking into account environmental conditions. Alice’s order will be delivered by the UAV ‘1-1’.
The UAVs take their corresponding expresses and begin the delivery according to the planned initial route.
The 6G network provides communication service for the UAVs through terrestrial access, satellites and/or high-altitude platform during UAVs flight to the remote region.
During the flight, UAVs that are equipped with sensors continuously collect the environmental condition information. Multiple UAVs can share flight status, environmental condition information, and mission progress information through 6G network with satellite access.
During the flight, the collected environmental condition information can be transmitted from UAVs, which request computing service to a proper selected Serving Hosting Environment (e.g. edge servers on board satellite) within the 6G network with satellite access. The Service Hosting Environment in the 6G network with satellite access processes the collected environmental data from one or more UAVs, with the associated weather information of this region from 3rd party application and detects that there will be a thunderstorm cloud on the predefined flight path of UAV ’1-1’, which requests UAV ‘1-1’ to adjust the height flight to avoid the cloud.
The 6G network with satellite access sends an alert (as the result of computing) to the UAV ‘1-1’, the UAV ‘1-1’ adapts its flight action to avoid the threat.
Finally, all the UAVs safely arrive at the targeted location as scheduled, and Jack gets the delivered medicine from UAV ‘1-1’.
8.9.4 Post-conditions
The UAVs can communicate with dispatch centre, dynamically update their flight routes and successfully finish their own delivery task in such harsh mountainous region through the support of 6G network with satellite access.
Logistics Company A can finish all delivery orders from all customers securely and efficiently.
8.9.5 Existing features partly or fully covering the use case functionality
SA1 has identified a series of performance requirements related to the UAVs in clause 7 of [35]. For example, in Table 7.1-1, the maximum altitude is 300 m. The flight average speed is 60 km/h. However, the existing performance requirement cannot support the communication performance requirement for the UAVs in low altitude area (below 3000 m) with satellite access and/or terrestrial access.
In [14], Table 7.4.2-1, the 5G system with satellite access performance requirements, the data rate of DL is [0.5] Mbit/s, and data rate of UL is [3] Mbit/s for the scenario “Video surveillance” with the UE type of “Vehicle mounted or fixed installation” while the data rate of DL 360 Mbit/s/plane and date rate of UL 180 bit/s/plane for scenario “Airplane mounted” have been specified.
In [14], clause 6.46.6, it has been specified:
A 5G system with satellite access shall be able to support both UEs supporting only satellite access and UEs supporting simultaneous connectivity to 5G satellite access network and 5G terrestrial access network.
But it has not supported same data shared among these two different kinds of UEs.
In [14], clause 6.5.2 on Service Hosting Environment:
The 5G network shall enable a Service Hosting Environment provided by operator.
Based on operator policy, the 5G network shall be able to support routing of data traffic between a UE attached to the network and an application in a Service Hosting Environment for specific services, modifying the path as needed when the UE moves during an active communication.
The 5G network shall be able to interact with applications in a Service Hosting Environment for efficient network resource utilization and offloading data traffic to the most suitable Service Hosting Environment, e.g. close to the UE's point of attachment to the access network or based on usage information.
NOTE: To accomplish offloading data traffic, usage information might be exposed to the Service Hosting Environment.
The 5G system shall support mechanisms to enable a UE to access the closest Service Hosting Environment for a specific hosted application or service.
In [14], clause 6.46.12.2 on Multi-orbit has been specified:
Based on regulatory requirements, operators’ policy and agreement with 3rd party, the 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support a mechanism to provide suitable Service Hosting Environment across satellites with orbit types having different characteristics.
NOTE 3: One example is an operator can choose the most suitable service hosting environment on-board of satellites, based on the topology of satellites as they are moving on the orbits.
But the existing requirements don't cover the computing service via Service Hosting Environment under the control of core network and consider the impact of the characteristics of the satellites (e.g. latency, the movement of LEO/Medium Earth Orbit (MEO)).
8.9.6 Potential New Requirements needed to support the use case
[PR 8.9.6-1] Subject to operator’s policy and agreement with 3rd party, the 6G system with satellite access shall be able to provide a computing service via a suitable Service Hosting Environment on board satellite to a UE (e.g. UAV) using only satellite access e.g. considering the latency and satellite capabilities.
[PR 8.9.6-2] The 6G system shall be able to support communication service for UE (e.g. UAV) with variant altitudes (e.g. from 0 to 3 km [99]) with satellite access and/or terrestrial access.
[PR 8.9.6-3] Subject to operator’s policy and user’s consent, the 6G system with satellite access shall be able to support data sharing among multiple UEs (e.g. UAV).
NOTE: The shared data is the processed results based on non-3GPP sensing data from the UEs (e.g. UAV) provided by Service Hosting Environment on aboard satellite.
[PR 8.9.6-4] Subject to operator’s policy, the 6G network with satellite access shall be able to support modifying the path for routing data traffic between a UE and Service Hosting Environment on board satellites to minimize service interruption considering the movement of UE and/or satellite.
8.10 Use case on hybrid TN and NTN positioning
8.10.1 Description
High-accuracy positioning with high availability is a key feature to enable increased efficiency and automation of operations, such as in mapping and transportation. This is typically achieved thanks to the use of GNSS with precise corrections, which can be provided by 3GPP networks through assistance data. Although GNSS and commercial corrections systems offer reliable services, their positioning solutions can still be affected by different sources of error, such as multipath or interferences. Thus, relative positioning solutions, such as sensors or vision-based navigation, are typically coupled with precise GNSS to enhance the positioning robustness. However, there are very limited mechanisms to verify the absolute positioning accuracy and availability of these precise GNSS solutions, especially when relative positioning solutions are not available or operational.
Dedicated 3GPP TNs can be deployed to enable high-accuracy positioning with high availability in enhanced service areas, while ensuring low latency. Nonetheless, the increased cost expenses of such dedicated deployments are not expected in commercial networks. Furthermore, the use of commercial TN deployments may not be sufficient to provide complementary 3GPP positioning for ground users, due to the expected blockage of the LoS from distant base stations, i.e. only one or two base stations may be available in LoS conditions. In case of aerial users, the commercial TN deployment may be sufficient to provide 3GPP horizontal positioning, however the similar altitude of the terrestrial base stations results in a poor accuracy in the height estimation. For both ground and aerial users, the deployment of 3GPP NTN can then increase the number of available LoS links and the vertical geometrical diversity, with respect to only using 3GPP TN positioning, as shown in Figure 8.10.1-1.
Figure 8.10.1-1: Hybrid TN and NTN positioning with 3GPP technologies
Hybrid TN and NTN positioning includes the combination of TN and NTN to support 3GPP positioning technologies. Thus, hybrid TN and NTN positioning with 3GPP technologies can enable high-accuracy positioning with high availability and fast convergence time, which could be used as a verification mechanism of non-3GPP positioning technologies, such as GNSS with precise corrections. This high-accuracy positioning service can then fulfil stringent performance requirements from aviation and UAV applications, such as those in [25].
One example application is the positioning verification of airport surface operations, where the aircraft on ground is required to meet safety navigation requirements in the taxiway. The aircraft follows the planned path based on the on-board navigation systems and visual operations, while the navigation requirements depend on the taxiway width of the airport, including the rapid exit phase with a ground aircraft speed up to 100 km/h. Low-visibility conditions can severely impair these aircraft taxi operations.
Another example application is the positioning verification of UAVs for facility monitoring. The UAVs fly over the installations for inspection (e.g. to capture photos and videos) or for surveillance, where the UAVs can achieve speeds up to 160 km/h.
In both cases, the verification mechanism is expected to operate with update rates between 2 Hz and 10 Hz and an availability above 99%, similarly to GNSS state-of-the-art solutions.
8.10.2 Pre-conditions
The UE has terrestrial and non-terrestrial network coverage in the 6G system.
The UE is registered to the terrestrial or non-terrestrial network.
The UE can be under the coverage of multiple or multi-orbit satellite networks (e.g. LEO and GEO constellations) deployed by one or more operators and has subscription(s) for accessing these satellite networks.
The environment of use is outdoor static or moving with coverage provided by the terrestrial and non-terrestrial networks.
The UE is equipped with a native 6G communication and positioning function. The positioning function relies on 3GPP technologies delivered by the satellite network, to be able to verify non-3GPP positioning results.
8.10.3 Service Flows
The 3GPP positioning service provided by the terrestrial and non-terrestrial networks can be initiated by a third party or by the end-user.
This 3GPP positioning service can determine the UE location with a certain accuracy level.
When triggering the 3GPP positioning service, if the UE fails to obtain the location data within a time limit or the location data is not accurate enough, the UE can select different terrestrial or non-terrestrial networks or improve the accuracy of location data by combining multiple networks.
The UE location is used to verify the non-3GPP positioning results.
The UE can report its UE location and positioning accuracy level.
8.10.4 Post-conditions
The user successfully obtains the 3GPP positioning service and can verify the non-3GPP positioning results.
8.10.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] clause 6.27 includes positioning and performance requirements for the 3GPP and non-3GPP positioning technologies, including the following requirement related to 3GPP terrestrial coverage:
The 5G system shall support mechanisms to determine the UE's position-related data for period when the UE is outside the coverage of 3GPP RAT-dependent positioning technologies but within the 5G positioning service area (e.g. within the coverage of satellite access).
However, this requirement does not ensure the combination of 3GPP terrestrial and non-terrestrial positioning technologies, which could be used to achieve high-accuracy positioning with high availability.
TS 22.261 [14] clause 7.3.2.2 also includes requirements for horizontal and vertical positioning service levels:
The 5G system shall be able to provide positioning services with the performances requirements reported in Table 7.3.2.2-1.
8.10.6 Potential New Requirements needed to support the use case
[PR 8.10.6-1] The 6G system with terrestrial and satellite access shall be able to provide positioning service for UE type e.g. Airplane Mounted and UAV Mounted with high availability, by using 3GPP positioning technologies and fulfilling the following KPIs:
Table 8.10.6-1: Performance requirements for positioning services based on hybrid TN and NTN positioning technologies
Scenario
(note 2)
Accuracy
(95 % confidence level)
Positioning service availability
Positioning service latency
Environment of use
UE speed
UE type
Horizontal Accuracy
Vertical Accuracy
Airplane taxiway (note 1)
[1] m
N/A
[99 %]
[0.1 - 0.5] s
Outdoor
Up to [100] km/h
Airplane mounted
UAV facility monitoring
[3] m
[3] m
[99 %]
[0.1 - 0.5] s
Outdoor
Up to [160] km/h
UAV mounted
NOTE 1: Airplane taxiway refers to an airplane on ground during taxi operations.
NOTE 2: Requirements for Airplane taxiway and UAV facility monitoring are in [25].
8.11 Use case on hybrid NTN and GNSS positioning
8.11.1 Description
The ubiquitous availability of position knowledge is paramount to a plethora of use cases and services. GNSS fulfils most of these positioning needs. GNSS systems are typically designed with a global depth of coverage of at least 4 satellites in open-sky conditions to enable standalone use of each constellation. Interoperability and standardisation of GNSS systems on signal structure, power levels, navigation message design and broadcasting relative time-offset has enabled modern smartphones to rely on Galileo, GPS, Beidou and Glonass increasing the number of visible satellites.
Yet, GNSS users can face availability challenges when buildings or natural objects obstruct the LoS view of satellites (such as in urban canyons with partly obstructed sky as in “Annex A.3.2” of [346]). The increased occurrence of a jamming and spoofing event constitutes an additional challenge for continuity and availability of positioning for users. For cold-start standalone GNSS, users may take tens of seconds or more to obtain a first position fix.
Hybrid operation of NTN and GNSS positioning may enable better accuracies especially in challenging environments thanks to an increased total number of satellites in view. Hybrid operation could further offer improved continuity of satellite-based positioning through frequency diversity of NTN bands and GNSS L-band signals, especially in jamming and spoofing events impeding L-band. Lastly, NTN satellites have the potential of reducing time to first fix, especially in cold-start scenarios by providing a fast data channel for satellite clock and ephemeris data. The target performance of hybrid NTN and GNSS positioning is expected to be aligned with positioning service level 1 and 2 of Table 7.3.2.2-1 in TS 22.261 [14], considering outdoor urban environments and requirements from airborne [25] and maritime [347] scenarios, respectively.
8.11.2 Pre-conditions
The UE has satellite network coverage in the 6G system.
The UE has access to GNSS signals (non-3GPP technology).
The UE can be unregistered or registered (e.g. in idle or connected mode) to the satellite network.
The UE can be under the coverage of multiple or multi-orbit satellite networks (e.g. LEO and GEO constellations) deployed by one or more operators and has subscription(s) for accessing these satellite networks.
The environment of use is outdoor static or moving with coverage provided by satellite network(s) and GNSS. The outdoor environment may have partly obstructed satellite visibility.
The UE is equipped with a native 6G communication and positioning function. The positioning function relies on 3GPP technologies delivered by the satellite network.
8.11.3 Service Flows
1. The 3GPP positioning service provided by the satellite network can be initiated by a third party or by the end-user.
2. This 3GPP positioning service with support from GNSS can determine the UE location with a certain accuracy level.
3. When triggering the 3GPP positioning service, if the UE fails to obtain the location data within a time limit or the location data is not accurate enough, the UE can select different satellite networks or improve the accuracy of location data by combining multiple networks.
4. The UE location is provided by the hybrid NTN and GNSS positioning service.
5. The UE can report its UE location and positioning accuracy level.
8.11.4 Post-conditions
The user successfully obtains its position based on 3GPP NTN positioning service and GNSS.
8.11.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] clause 6.27 includes positioning requirements for the 3GPP and non-3GPP positioning technologies. In addition, TS 22.261 [14] clause 7.3.2.2 includes performance requirements for horizontal and vertical positioning service levels for the 3GPP system:
The 5G system shall be able to provide positioning services with the performances requirements reported in Table 7.3.2.2-1.
NOTE: The requirements do not preclude any type of UE, including specific UE such as for example V2X, MTC.
Table 8.11.5-1: Performance requirements for Horizontal and Vertical positioning service levels (Table 7.3.2.2-1 from [14])
Positioning service level
Absolute(A) or Relative(R) positioning
Accuracy
(95 % confidence level)
Positioning service availability
Positioning service latency
Coverage, environment of use and UE velocity
Horizontal Accuracy
Vertical Accuracy
(note 1)
5G positioning service area
5G enhanced positioning service area
(note 2)
Outdoor and tunnels
Indoor
1
A
10 m
3 m
95 %
1 s
Indoor - up to 30 km/h
Outdoor
(rural and urban) up to 250 km/h
NA
Indoor - up to 30 km/h
2
A
3 m
3 m
99 %
1 s
Outdoor
(rural and urban) up to 500 km/h for trains and up to 250 km/h for other vehicles
Outdoor
(dense urban) up to 60 km/h
Along roads up to 250 km/h and along railways up to 500 km/h
Indoor - up to 30 km/h
3
A
1 m
2 m
99 %
1 s
Outdoor
(rural and urban) up to 500 km/h for trains and up to 250 km/h for other vehicles
Outdoor
(dense urban) up to 60 km/h
Along roads up to 250 km/h and along railways up to 500 km/h
Indoor - up to 30 km/h
4
A
1 m
2 m
99,9 %
15 ms
NA
NA
Indoor - up to 30 km/h
5
A
0,3 m
2 m
99 %
1 s
Outdoor
(rural) up to 250 km/h
Outdoor
(dense urban) up to 60 km/h
Along roads and along railways up to 250 km/h
Indoor - up to 30 km/h
6
A
0,3 m
2 m
99,9 %
10 ms
NA
Outdoor
(dense urban) up to 60 km/h
Indoor - up to 30 km/h
7
R
0,2 m
0,2 m
99 %
1 s
Indoor and outdoor (rural, urban, dense urban) up to 30 km/h
Relative positioning is between two UEs within 10 m of each other or between one UE and 5G positioning nodes within 10 m of each other (note 3)
NOTE 1: The objective for the vertical positioning requirement is to determine the floor for indoor use cases and to distinguish between superposed tracks for road and rail use cases (e.g. bridges).
NOTE 2: Indoor includes location inside buildings such as offices, hospital, industrial buildings.
NOTE 3: 5G positioning nodes are infrastructure equipment deployed in the service area to enhance positioning capabilities (e.g. beacons deployed on the perimeter of a rendezvous area or on the side of a warehouse).
TS 22.261 [14] clause 7.3.2.1 does require in general the combination of 3GPP and non-3GPP positioning to achieve superior performance than the 3GPP system standalone:
The 5G system shall support the combination of 3GPP and non-3GPP positioning technologies to achieve performances of the 5G positioning services better than those achieved using only 3GPP positioning technologies.
However, no performance targets are provided with satellite access. Furthermore, no specific mention is made of hybrid NTN and GNSS positioning where signals used for ranging emanate from satellites in both systems.
TS 22.261 [14] clause 6.46.10 requires the 5G system with satellite access to be able to support 3GPP positioning methods for UEs using only satellite access. The UE referred to may or may not have GNSS capabilities.
However, no specific requirements are set either on the functionality of combining 3GPP positioning technologies and GNSS nor on the performances of such combination.
TS 22.261 [14] clause 6.27.2 notes that hybrid positioning includes a combination of 3GPP and non-3GPP positioning methods, for example Network-based Assisted GNSS. However, no mention is made of provision of Network-based Assisted GNSS by means of NTN.
While not further specified in [14], Network-based Assisted GNSS is understood as the 5G system providing data that assists the UE in obtaining a GNSS position. This data can include for example GNSS navigation message data required for the UE to compute its position as is supported in LTE positioning protocol (LPP) for terrestrial networks.
TS 38.171 [100] specifies minimum expected GNSS performance. However, this document does not provide any specific requirements on positioning performance of a 5G system with satellite access. For outdoor environment, the performance of combined NTN and GNSS is expected to be superior to the minimum GNSS performance as specified in TS 38.171 [100], clauses 5 and 6.
8.11.6 Potential New Requirements needed to support the use case
[PR 8.11.6-1] The 6G system with satellite access shall be able to provide a positioning service supported by the combination of NTN and GNSS positioning technologies, including Network-based Assisted GNSS.
[PR 8.11.6-2] The 6G system with satellite access shall be able to provide positioning service with the following KPIs:
Table 8.11.6-1: Performance requirements for positioning services with satellite access
Scenario
Accuracy
(95 % confidence level)
Positioning service availability
Positioning service latency
Environment of use
UE speed
UE type
Horizontal Accuracy
Vertical Accuracy
UE with partly obstructed sky
[10] m
[3] m
[95 %]
[1] s
Outdoor rural and urban
Up to [250] km/h
Handheld, vehicle mounted, IoT
Airborne and maritime
[3] m
[3] m
[99 %]
[1] s
Outdoor rural
Up to [500] km/h
Airborne or maritime mounted
8.12 Use case on ubiquitous emergency rescue via UAVs
8.12.1 Description
When emergency situations (such as natural disasters or accidents) occur, timely and effective rescue is crucial for saving lives and minimizing damage. Traditional rescue operations are affected by various factors, including the severity of a disaster, the availability of transport resources, and the damage to communication infrastructure. By utilizing UAVs and other low-altitude aircraft supported by NTN, the ubiquitous emergency rescue can overcome limitations posed by adverse factors to minimize casualties and property losses.
The UAVs equipped with intelligent rescue planning system, high-resolution cameras, thermal imaging, and environmental sensors, could gather affected areas environment data in real time, then deliver to the Emergency Response Agency (ERA). The ERA will design a rescue plan based on the information and dispatch UAVs embedded with 6G base station to provide temporary communication for rescue team on site. Introducing low-altitude aircraft (e.g. UAV), into emergency rescue can significantly enhance situational awareness, rescue resource management, ultimately leading to more effective and timely responses in crisis situations.
NOTE: The term 6G base station does not imply any architectural assumption, e.g. whether 6G CN/base station is a new or evolved CN/base station (compared to 5G).
Refer the latest CHINA airspace classification [99], the low altitude area is below 3000m, where the low-altitude aircraft could fly. The communication coverage for altitude less than 3000m is needed accordingly. However, in some area[[SUGGESTION_START]]s ([[SUGGESTION_END]]e.g. forests, mountains, etc.[[SUGGESTION_START]])[[SUGGESTION_END]] the terrestrial network is not available[[SUGGESTION_START]],[[SUGGESTION_END]] which great[[SUGGESTION_START]]ly[[SUGGESTION_END]] impacts the rescue operation. The NTNs, supported by LEO satellite networks, HAPS, multi-orbit satellites and other space-based infrastructure, could compensate the coverage blind spots and insufficient coverage of terrestrial network to improve communication service reliability.
Beside NTN based communication service, 6G NTN system will provide computing capabilities in the operator managed Service Hosting Environment, which will reduce the throughput of the feeder link and help intelligent rescue planning system to process data more intelligent and faster.
8.12.2 Pre-conditions
ERA has already applied flight permission for its UAVs from local aviation authority. In order to carry out rescue operations effectively, the rescue teams are equipped with two kinds of UAVs, i.e. Type-1 UAVs (one kind of rescue UAV, equipped with UE, high-resolution cameras, advanced sensors) and Type-2 UAVs (another kind of rescue UAV, embedded with 6G base station).
NOTE: The term 6G base station does not imply any architectural assumption, e.g. whether 6G CN/base station is a new or evolved CN/base station (compared to 5G).
Operator B provides 6G TN and NTN service.
The ERA has a contract with Operator B to achieve 6G TN and NTN services.
The rescue team is equipped with AR devices.
8.12.3 Service Flows
Figure 8.12.3-1: Illustration of emergency rescue via UAVs supported by the NTN
NOTE 1: The term 6G base station below does not imply any architectural assumption, e.g. whether 6G CN/base station is a new or evolved CN/base station (compared to 5G).
1. When an earthquake occurs, the terrestrial network breaks down. ERA dispatches Type-1 UAV A1, A2 and Type-2 UAV X1 to the disaster area.
2. With the help of 6G NTN network for C2 communication, UAV A1, A2 and X1 successfully arrive in the disaster area.
3. UAV X1 working as the temporary 6G base station, with the assistance of satellite, provides 6G coverage for the ground rescue team and low altitude UAV A1, A2, in the disaster area.
4. UAV X1 could detect ground rescue team moving and UAV A1, A2 flying, and adjust its location to continue providing communication service. The 6G network could get the satellite information e.g. satellite load, satellite availability, etc.
5. When the satellite backhaul link (e.g. GEO) quality for the temporary 6G base station on boarding UAV X1 degrades, the 6G system transfers UAV X1's communication connection to the nearest LEO satellite, based on the information e.g. satellite ephemeris information, satellite load, satellite availability, etc., ensuring uninterrupted rescue task operation.
6. The UAV A1 and A2 collect disaster area environment data (e.g. thermal imaging/video to locate survivors and environmental monitoring for hazardous conditions, etc.) via their cameras, and deliver these data to ERA through 6G NTN as the orange line showed in Figure 8.12.3-1. The data delivery need real time and higher data rate in uplink (e.g. 50 Mbit/s) even in complicated geographical environments and long transmission distance.
NOTE 2: The calculation of the uplink data rate needs to comprehensively consider the various data types, including video streams, images, sensor data (such as position, gestures, and depth) of the environmental, etc. Based on TR 26.925 [24], the typical streaming video for real time 4K Ultra HD is from 5Mbps to 25 Mbps with 4K resolution (3840 × 2160). The amount of sensors (including position, posture, gesture, High-resolution depth sensor, etc.) data depends on the data frequency and resolution which in general are 25Mbps. In summary, the Uplink data rate is about 50 Mbps. If it is 8K or 12K, the data rate will be increased more.
7. ERA receives disaster area environment data from UAV A1 and A2, analyse and identifies survivor’s location, then direct the rescue team, UAV A1 and A2 to go to the location.
8. The rescue team on-site with AR glasses receives real time data from UAV A1 and A2 through the communication link provided by UAV X1. These AR glasses can use image recognition technology to identify survivors' faces, heat sources, and dangerous areas, and overlay information in the field of view, providing visual decision support and helping them better understand the situation on site.
9. The rescue team receives images from UAV A1 via the 6G base station on boarding UAV X1, as the red line showed in Figure 8.12.3-1, which requires high reliability and low latency transmission in this link. Through the images, rescue team finds survivors in the location area and carry out the rescue as soon as possible.
10. Finally, all rescue tasks are successfully completed.
8.12.4 Post-conditions
All the UAVs can efficiently work for rescue through 6G NTN network. All survivors are timely rescued and treated.
8.12.5 Existing features partly or fully covering the use case functionality
In TS 22.261 [14], following NTN requirements have been specified, but have not covered base statoin on boarding UAV.
The 5G system shall be able to support mobile base station relays using 3GPP satellite NG-RAN (NR satellite access).
The 5G system shall be able to support mobile base station relays accessing to 5GC via NR satellite access and NR terrestrial access simultaneously.
The 5G system shall be able to support service continuity for mobile base station relays using at least one 3GPP satellite NG-RAN.
NOTE 7: This requirement applies to scenarios where there is a transition between two 3GPP NG-RAN, operated by the same MNO, involving at least one 3GPP satellite NG-RAN.
For a 5G system with satellite access, the following requirements apply:
- A 5G system with satellite access shall support the use of satellite links between the radio access network and core network, by enhancing the 3GPP system to handle the latencies introduced by satellite backhaul.
- A 5G system with satellite access shall be able to support meshed connectivity between satellites interconnected with ISLs.
The 5G network can also support multiple wireless backhaul connections (e.g. satellites and/or terrestrial), and efficiently route and/or bundle traffic among them.
In TS 22.125 [35], following “BS on boarding UAV” requirements are specified, but the NTN has not been considered.
[R-6.4-001] The 5G system shall be able to support UxNBs to provide enhanced and more flexible radio coverage.
[R-6.4-002] The 3GPP system shall be able to provide suitable means to control the operation of the UxNBs (e.g. to start operation, stop operation, replace UxNB etc.).
[R-6.4-003] The 3GPP system shall be able to provide means to minimize power consumption of the UxNBs (e.g. optimizing operation parameter, optimized traffic delivery).
[R-6.4-004] The 3GPP system shall be able to minimize interference e.g. caused by UxNBs changing their positions.
In TS 23.501[140], the reporting of satellite backhaul to the SMF based on the "satellite backhaul category" is specified, but dynamic switching between satellites in different orbits has not been considered.
In TS 28.541 [340], the attribute properties of qFMonitoredSatelliteBackhaulCategories and SatelliteBackhaulInfo.satelliteBackhaulCategory are specified, but dynamic switching between satellites in different orbits for QoS monitoring and category have not been considered.
In TS 29.571 [339], the description of each satellite backhaul category is specified, and only a single backhaul category can be indicated. Moreover, dynamic switching between satellites in different orbits has not been considered.
In TS 22.261 [14], clause 6.46.12.3 Requirements related to mobile base station relays, following requirements are defined.
Subject to regulatory requirements and operator’s policies, the 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to support mobile base station relays to access satellites with orbit types having different characteristics considering e.g., availability of the coverage, latency, data rate, required QoS.
Subject to regulatory requirements and operator’s policies, the 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support connectivity between a mobile base station relay and the 5G Core through simultaneously using terrestrial and one multi orbit satellite access path taking into account the respective capabilities (e.g., latency, data rate) and availability of the different satellite access (e.g., over GEO, MEO, LEO) to map the traffic with the aggregated QoS required at the mobile base station relay.
But it doesn’t consider the selection the satellite backhaul.
8.12.6 Potential New Requirements needed to support the use case
NOTE: The following terms 6G base station/CN do not imply any architectural assumption, e.g. whether 6G CN/base station is a new or evolved CN/base station (compared to 5G).
[PR 8.12.6-1] The 6G system with satellite backhaul links between a 6G base station on board of UAV and the 6G CN, shall support the selection and switching between satellite links, each having different characteristics, based on e.g. traffic load, quality of the link, satellite availability.
8.13 Use case on HAPS-based rapid deployable network for public safety and disaster response
8.13.1 Description
This use case focuses on leveraging HAPS, specifically HIBS (base station on board a HAPS), to rapidly establish or restore communication services in areas affected by natural disasters (earthquakes, floods, hurricanes, wildfires) or other emergencies where terrestrial infrastructure is damaged, destroyed, or overloaded. HAPS provides a service link to standard UEs over wide-area (e.g. 50-100 km radius per platform) and can prioritize emergency traffic for first responders. Its stratospheric position makes it immune to ground-level events. Multiple HAPS can be deployed to cover larger areas or provide redundancy.
8.13.2 Pre-conditions
• A disaster or emergency event has occurred, disrupting terrestrial communications.
• HAPS platform(s), which can be Heavier-than-Air (HTA) types like fixed-wing aircraft or Lighter-than-Air (LTA) types like airships, are available for deployment or are already airborne near the affected region.
• Authorized spectrum is available for emergency use.
• Emergency response agencies and targeted user groups are equipped with compatible devices (standard smartphones or dedicated terminals).
• A mechanism exists to establish feeder link for the HAPS (e.g. via satellite link (HTS), a connection to an intact ground gateway outside the disaster zone (HTG), or through another nearby HAPS (HTH)).
• Coordination with relevant authorities (e.g. aviation, emergency management) is established.
8.13.3 Service Flows
Upon notification/authorization, HAPS platform(s) are deployed/re-tasked to the target area.
HAPS activates communication payload, establishing service links over the designated area.
Establish the feeder link (HTS, HTG and/or HTH). The service link between HAPS and UEs is then activated over the designated area.
Authorized UEs (first responders, public safety devices, potentially public UEs) register and connect to the network via HAPS.
Voice, data, and potentially location services are provided. HAPS should prioritize traffic for emergency services. HAPS can also provide communication between UEs under the coverage of base stations onboard HAPS platforms, without the user traffic going through the ground network (e,g. push-to-talk for first responders.)
Network monitoring and management are performed remotely.
Figure 8.13.3-1 shows a structure of HAPS-based rapid deployable network based on the above flows.
Figure 8.13.3-1: HAPS-based rapid deployable network
8.13.4 Post-conditions
• Essential communication services are established/restored in the affected area via HAPS.
• First responders can communicate effectively.
• Affected populations can access emergency information or contact help.
• Network status is monitored, and operational logs are maintained.
8.13.5 Existing features partly or fully covering the use case functionality
• Public Safety features (e.g. priority access [63], MCPTT [53], MCData [56], MCVideo [55]).
• Location Services (LCS) [61].
• Basic connectivity, QoS framework.
• Mobility Management (Handover, Cell Reselection), especially in case the TN network is just partially or locally damaged, some level of mobility between TN and temporary HAPS coverage is expected.
• Multi-connectivity/redundancy features with regard to multi-link backhaul connectivity.
8.13.6 Potential New Requirements needed to support the use case
[PR 8.13.6-1]: The 6G system shall support communication between UEs under the coverage of base stations onboard HAPS platforms, without the user traffic going through the ground network.
8.14 Use case on resilient time distribution in satellite networks
8.14.1 Description
In cellular systems, accurate time synchronization is essential for various network operations, including the coordination between base stations and efficient spectrum usage. Typically, GNSS like GPS are employed to provide this time synchronization. However, during certain circumstances—such as GNSS signal jamming, natural disasters, or equipment failures—access to GNSS-based time synchronization may be compromised.
In such scenarios, 3GPP satellite networks can serve as a resilient alternative for time distribution. UEs or terrestrial base stations, can obtain highly accurate time signals directly from the 3GPP satellite networks. This capability ensures the continuity of network operations, even when GNSS signals are unavailable or unreliable. Satellite networks can also support time synchronization across vast geographic regions, making them particularly useful in remote areas or during large-scale disruptions.
For example, in a region where GNSS signals are temporarily unavailable, a local cellular network can use 3GPP satellite networks to maintain accurate timing. This synchronization enables seamless communication between UEs and ensures the proper functioning of time-sensitive applications.
8.14.2 Pre-conditions
• The 3GPP satellite network must be operational and capable of providing accurate time synchronization signals.
• UEs and base stations, must be equipped with capabilities to receive time information from 3GPP satellite networks.
8.14.3 Service flows
1. Under normal circumstances, the cellular system uses GNSS to synchronize time for base stations and UEs.
2. When GNSS signals are disrupted (e.g. due to jamming or environmental factors), the system automatically detects the issue and switches to the 3GPP satellite network for time synchronization.
3. The satellite network provides accurate time signals to UEs and possibly also terrestrial base stations, ensuring continuity of operations.
4. The synchronized time allows for uninterrupted communication and proper functioning of network applications.
5. Once GNSS signals are restored, the system may smoothly transition back to GNSS-based synchronization.
8.14.4 Post-conditions
• The cellular system continues to function with accurate time synchronization, either via GNSS or 3GPP satellite networks.
• Time-sensitive operations and applications operate without disruptions, maintaining high reliability.
8.14.5 Existing features partly or fully covering the use case functionality
Clause 6.36.4 in TS 22.261 [14] includes requirements related to 5G timing resiliency.
6.36 5G Timing Resiliency
6.36.1 Overview
5G systems rely on reference precision timing signals for network synchronization in order to operate. These synchronization references are generated by Primary reference Time Clocks that typically get the timing reference from GNSS receivers and in order to meet the relevant synchronization requirements also during failure conditions, the synchronization network designs typically include means to address potential degradation of the GNSS signal performance. Some deployment of 5G involve applications that themselves can be sensitive to any degradation of the timing signal. In such cases it is beneficial for the 5G system to be enhanced to act as a backup for loss of their GNSS references. In some implementations, timing resiliency enhancements to the 5G system can work in collaboration with different types of time sources (e.g., atomic clock, time service delivered over the fibre) to provide a robust time synchronization. 5G as a consumer of time synchronization benefits from timing resiliency which enables the support of many critical services within the 5G network even during the event of a loss or degradation of the primary GNSS reference timing. Additionally, for time critical services (e.g. financial sector or smart grid), the 5G system can operate in collaboration with or as backup to other timing solutions. A base of clock synchronization requirements when 5G is providing a time signal, if it is deployed in conjunction with an IEEE TSN network or if it is providing support for IEEE 1588 related protocols, is included in [21] clause 5.6. The enhancements in this clause build on this to add timing resiliency to the 5G system enabling its use as a replacement or backup for other timing sources.
6.36.2 General
The 5G system shall support enhanced timing resiliency in collaboration with different types of time sources (e.g., GNSS, TBS/MBS [33] [34], Sync over Fiber [34]) to provide a robust time synchronization.
The 5G system shall be able to maintain accurate time synchronization as appropriate for the supported applications in the event of degradation or loss of the primary timing reference (e.g., GNSS).
6.36.3 Monitoring and Reporting
The 5G system shall be able to support mechanisms to monitor for timing source failure (e.g., GNSS).
The 5G system shall be able to detect when reference timing signals (e.g., from GNSS or other timing source) are no longer viable for network time synchronization.
The 5G system shall support a mechanism to determine if there is degradation of the 5G time synchronization.
The 5G system shall be able to support mechanisms to indicate to devices (e.g., UEs, applications) that there is an alternate time source available for use (e.g., 5G system internal holdover capability, atomic clock, Sync over Fiber, TBS, GNSS), taking into account the holdover capability of the devices.
The 5G system shall be able to detect when a timing source fails or is restored for network time synchronization.
The 5G system shall support mechanisms to monitor different time sources and adopt the most appropriate.
The 5G system shall support a mechanism to report timing errors such as divergence from UTC and time sync degradation to UEs and 3rd party applications.
6.36.4 Service Exposure
The 5G system shall support a mechanism for a 3rd party application to request resilient timing with specific KPIs (e.g., accuracy, interval, coverage area).
However, there is no requirement indicating that the 3GPP satellite network can be used as a synchronization source independently of GNSS.
8.14.6 Potential New Requirements needed to support the use case
[PR 8.14.6-1] The 6G system with satellite access shall be able to provide time synchronization to UEs and applications using 3GPP technologies, independently of non-3GPP technologies (e.g. GNSS).
8.15 Use case on On board Computing in 6G NTN domain
8.15.1 Description
5G NTN initially accounted for use of transparent satellites in Release-17, TR 23.737 [341] and Release-18, TR 23.700-28 [342]. Subsequent 5G NTN enhancements in Release-19, [343] and Release-20 [344] considered the use of satellites with on-board processing, i.e. deployment of (part of) gNB on board satellites as well as some core network functions (at least UPF). The study on satellite backhauling [345] conducted within Release-18 introduced for the first time deployment of edge computing (EC) capabilities on board satellite. However, this EC feature was considered solely for the cases of GEO satellites.
Foreseen developments of satellite systems, their mass deployment in lower (LEO and Very Low Earth Orbit (VLEO)) and MEO as well as unification of NTN and TN domains within 6G introduce the need for a generic support of computation on board satellites. As discussed in HEXA-X-II deliverable [31] 6G will benefit from dynamic allocation of computational workloads over various parts of the network. Computation-communication continuum in 6G will allow for increased network capacity, enhanced QoS and will ensure that AI-driven applications receive sufficient computational power for real time decision-making.
NTN domain of 6G will consist of multiple spaceborne layers i.e. VLEO, LEO, MEO and GEO. 6G will further enhance performance of the NTN domain and reduce its dependency of TN domain. For this reason, it is essential to support computation capabilities on board satellites within the service hosting environment [14]. This capability will allow for, but is not limited to:
- training and running AI models in space, which can be exploited for autonomous navigation, data analysis, and anomaly detection,
- processing and analysis of massive datasets generated by space-based sensors sharing the same platform as the telecom mission e.g. telescopes, radars or other spaceborne units.
- Enhanced Earth and weather observation applications that would locally process collected data using satellite on-board computation capabilities, transferring solely essential (meta-) data.
Satellite computing capabilities of a single satellite might be limited due to design constraints, i.e. by size, power, cooling and weight limitations. This limitation may be (partially) alleviated through the combining of computational capabilities over multiple interconnected satellites. In such case, computational context (i.e. pre-processed data) from one satellite can be transferred to another satellite for further processing. This principle is useful in case of NGSO (e.g. LEO, MEO) satellites that experience mobility of network nodes.
8.15.2 Pre-conditions
• A 6G network operator ‘6-NO’ provides connectivity services through its NTN domain.
• 6-NO satellite fleet is capable of collecting weather data through sensors on board satellites.
• 6-NO satellite fleet is capable of connecting to satellites dedicated for Earth observation and weather monitoring.
• 6-NO provides computing as a service CaaS capability through its edge computing on board its satellite fleet. CaaS capability enables processing and analysis of large amount of raw data collected through sensing capability of the own satellite fleet or from another satellite fleet.
8.15.3 Service Flows
1. Meteorological agency Mete-O provides high precision weather forecasts to customers, e.g. maritime agencies, coast guards and private individuals. Mete-O provides reliable and prompt weather forecasts. Mete-O provides warnings in case of sudden weather changes in specific geographical areas that are of interest for the customers.
2. Mete-O has purchased subscription with 6-NO to acquire specific meteorological meta-data extracted from the large amount of raw data collected from corresponding sensors.
3. 6-NO collects raw sensed data on-board satellites, process them locally using computational capabilities on board satellites, extracts the requested meta-data and transfers it to Mete-O.
4. Mete-O further processes the meta-data in its weather models and provides high precision weather forecast to the end users.
8.15.4 Post-conditions
The end users exploit the high accuracy weather forecast to preserve safe and reliable operation of its business or task.
8.15.5 Existing features partly or fully covering the use case functionality
TS 23.548 [137] covered the topic of Edge Computing but did not address deployment of computing capabilities on board satellites.
8.15.6 Potential New Requirements needed to support the use case
[PR 8.15.6-1] The 6G network using satellite access shall be able to provide the service hosting environment onboard satellite minimizing the necessary bandwidth of the inter satellite links and feeder links.
[PR 8.15.6-2] The 6G system using satellite access based on regenerative satellites shall be able to support the transfer of computing information (e.g. pre-processed data within the service hosting environment) between satellites over a given area.
8.16 Use case on positioning integrity in TN and NTN
8.16.1 Description
Safety- and liability-critical applications, such as in aviation, maritime and automotive sectors, demand high levels of integrity and reliability of the positioning solution, especially in autonomous operations. Positioning integrity is a measure of the trust in the accuracy of the position-related data and the ability to provide timely warnings based on assistance data provided by the positioning system. In TS 22.261 [14], there are already 5G service requirements on the need to determine the reliability, and the uncertainty or confidence level, of the position-related data. Given these requirements, the solutions for GNSS positioning integrity were studied in Release 17 within TR 38.857 [348], and the solutions for the integrity of 3GPP positioning technologies in Release 18 within TR 38.859 [349]. As a result, assistance data relevant for positioning integrity (i.e. integrity support data such as integrity services parameters and integrity alerts) was introduced in the functional specification of UE positioning, while navigation performance requirements are defined by external bodies, such as by ICAO in civil aviation [25].
The 6G positioning integrity benefits from TN and NTN in terms of increased availability of the integrity support data through access diversity, as well as the introduction of integrity support data for 3GPP NTN positioning technologies. Therefore, the capability of providing integrity support data with both TN and NTN can enable safety- and liability-critical applications even beyond terrestrial network coverage.
8.16.2 Pre-conditions
The UE has terrestrial and non-terrestrial network coverage in the 6G system.
The UE is registered to the terrestrial or non-terrestrial network.
The UE can be under the coverage of multiple or multi-orbit satellite networks (e.g. LEO and GEO constellations) deployed by one or more operators and have subscription(s) for accessing these satellite networks.
The environment of use is outdoor static or moving with coverage provided by the terrestrial and non-terrestrial networks.
The UE is equipped with a native 6G communication and positioning function. The positioning function relies on 3GPP technologies delivered by the network(s), to support positioning integrity.
8.16.3 Service Flows
1. The 3GPP positioning service with integrity provided by the terrestrial and non-terrestrial networks can be initiated by a third party or by the end-user.
2. This 3GPP positioning service with integrity can be used to determine the UE location and to bound related errors within a given integrity level.
3. When triggering the 3GPP positioning service with integrity, if the UE fails to obtain the positioning integrity data within a time limit, the UE can select different terrestrial or satellite networks to retrieve the positioning integrity data.
8.16.4 Post-conditions
The user successfully obtains the 3GPP positioning service with integrity.
8.16.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] clause 6.27 includes the need to determine the reliability, and the uncertainty or confidence level, of the position-related data:
The 5G system shall be able to determine the reliability, and the uncertainty or confidence level, of the position-related data.
TS 22.261 [14] clause 6.37 includes an integrity requirement related to the ranging information in ranging-based services:
The 5G system shall be able to ensure the integrity and confidentiality of ranging information used by ranging-enabled UEs.
However, these requirements are only applicable for 3GPP terrestrial and non-3GPP positioning technologies, but they are not considered yet for 3GPP non-terrestrial positioning technologies. Therefore, a requirement is needed to cover the positioning integrity gap with both terrestrial and non-terrestrial access.
8.16.6 Potential New Requirements needed to support the use case
[PR 8.16.6-1] The 6G system with terrestrial and satellite access shall be able to provide integrity for a positioning service.
8.17 Use case on 6G satellite backhaul
8.17.1 Description
Satellite backhauling first emerged in the early 2000s as a solution for connecting remote cellular base stations to the backbone network when terrestrial connections were unavailable or unreliable. As cellular networks have evolved from 2G to 5G, the satellite backhauling technology has advanced significantly – starting with GEO satellites for 2G voice/SMS to High Throughput satellites (HTS) for 3G/4G data and more recently, to LEO satellites for low-latency data service.[350] Today, the satellite based backhauling plays a critical role in the mobile market, as it can not only extend coverage to underserved regions with limited terrestrial infrastructure (e.g. Africa, South America), but also enhance network resilience in urban areas by alleviating congestion and solving disaster-induced failures.
Figure 8.17.1-1: Legacy Satellite Backhauling based on non-3GPP system
As outlined by ITU-R [27], IMT-2030 is expected to connect humans, machines and various other things together with further development of ubiquitous connectivity and enhanced resilience in design, deployment and operation. With the trend toward integrated satellite and terrestrial network in 6G, satellite backhaul will increasingly become a necessary backhaul option, offering unprecedented flexibility for 6G networks and improving the cost efficiency of the satellite-based 6G infrastructure. However, existing satellite backhaul is treated as an independent system from 3GPP system. The isolation between each system has caused a series of issues.
- Extra CAPEX and redundancy: As [351] mentioned, the ownership of dedicated satellite equipment (VSAT modem and hub) used for satellite backhaul is obligatory to be MNOs (e.g. in Turkey). If the mobile traffic is carried over satellite equipment owned by 3rd party satellite providers, the providers need to have customized mobile communication licenses by local government agencies. Such regulations require extra investment from MNOs or satellite operators to enable satellite backhauling. Moreover, the satellite backhaul network (using DVB) and 3GPP satellite network are architecturally decoupled, resulting in duplicated onboard components (e.g. transponders) on the satellites capable of satellite access and backhauling, or additional single-purpose satellites, or redundant ground infrastructures.
- Inefficient resource management: for the satellites supporting both access and backhauling, the independent operations of two payloads may lead to resource inefficiency issues such as the spectrum interference when using overlapping Ka/Ku bands, static bandwidth partition without dynamic allocation, etc. The lack of collaboration in resource allocation may reduce the chance to ensure the user experience of 3GPP services carried on the satellites especially when the backhauling congestion could be solved by using the bandwidth provisioned for 3GPP satellite access, or further, sharing the satellite link used for wireless access. Compared with traditional terrestrial transmission, the transmission cost per bit via satellite links is higher. Thus, the sharing of the satellite links either among different kinds of services (e.g. access, backhauling) or multiple operators can be a promising business model to maximize resource utilization and reduce OPEX.
- Inflexible network topology and backhauling: 6G base stations mounted on various locations (e.g. ground, vehicles, vessels, drones, HAPS and satellites) exhibit distinct mobility characteristics, which raise demands for switching the service link of the satellite backhauling to adapt to their specific mobility patterns as illustrated in Figure 8.17.1-1. Besides, the movement of NGSO satellites used for backhauling intensifies the demand for dynamic link switching. However, one satellite may have only one feeder link to a traditional ground station (e.g. using DVB) located in a specific area, limiting the possibility to choose a suitable ground station or feeder link for optimal backhaul link especially adapting to service link switching. Moreover, in scenarios such as disaster areas or remote areas like oceans and deserts, there is a strong demand to deploy one ground station for temporary usage, in order to quickly set up a localized network enabling communications for UAVs and emergency devices. However, the legacy ground stations that rely on the provisioning cannot easily meet such plug-and -play demand.
- Lack of coordination for the reliability and resilience of E2E connectivity: traditional terrestrial backhauling is generally assumed to be reliable and stable in the design of 3GPP systems. However, satellites mobility and inherent instability of feeder links may compromise the reliability and continuity of the backhaul link, which need to be taken into account to ensure reliable E2E connectivity. Store and forward operation defined in 5G could be treated as a workaround to mitigate the impact of instable satellite links on the service delivery but its applicability is limited to the latency-insensitive service. Currently, the satellite backhauling is managed independently, and there are no standardized mechanisms to coordinate the satellite backhauling with 3GPP network. The lack of coordination hinders the rapid adaption of backhaul links such as replacement, re-configuration and further impedes the optimization of E2E QoS, or the fulfilment of divergent backhaul quality requirements (e.g. via one or multiple links, by dynamic configuration).
6G can be expected to solve the above pain points by leveraging 3GPP technologies to support satellite backhaul as a part of 6G system as Figure 8.17.1-2. And this may also stimulate a new business model for the MNOs supporting satellite access, to offer 6G satellite backhaul service to other MNOs based on the same satellite infrastructures.
This use case illustrates an example that the 6G system serves various types of 6G users using satellite access through 6G satellite backhaul.
Figure 8.17.1-2: 6G Satellite Backhauling based on 3GPP satellite network
8.17.2 Pre-conditions
As Figure 8.17.2-1, Network Operator OpSAT provides 6G backhauling service via its deployed satellite based 6G network (6G SAT NW), which may include multiple orbit types of satellites (e.g. GEO, MEO, LEO) and distributed ground stations (e.g. Ground Station#1) for the usage.
Network Operator OpMNO#1 and OpMNO#2 have agreements with OpSAT for backhauling services
- They can provide satellite access to their individual subscribers via dedicated satellite-based RANs or shared satellite-based RANs with OpSAT to ensure ubiquitous connectivity.
- They have individual ground core network (6GC#1 for OpMNO#1 and 6GC#2 for OpMNO#2).
OpMNO#2 has owned a vehicle mounted ground station with the subscription of OpSAT for emergency usage.
Bob (with UE#1) is a travel blogger and subscribes to OpMNO#1for the global reliable communication services.
A UAV (with UE#2) is used for remote search and rescue and subscribes to OpMNO#2 for 3GPP services (e.g. communication service, sensing service and computing service).
Figure 8.17.2-1: Serve 6G users with 6G Satellite Backhauling
8.17.3 Service Flows
Based on the agreement with OpMNO#1 and OpMNO#2, OpSAT enables the backhauling services for 6GC#1 and 6GC#2 through the provisioned ground stations within their respective serving areas, establishing dedicated backhaul links connecting each operator’s base station and ground core networks.
Sub-Scenario A: Efficient service delivery via dynamic backhauling links
(1). Bob traveled to Tanzania to photograph the Great Migration of animals. Upon arriving at East Africa, UE#1 camped on base station on 6G SAT(GEO) and connected to 6GC#1 through the satellite backhaul link#1, enabled by 6G SAT(GEO) and Ground Station#1.
(2). To report the safety, Bob initiated a voice call with his family using UE#1. The voice traffic was identified and routed to 6GC#1 via the satellite backhaul link#1 regarding the operator’s policy and the coordination of 6GC#1 and 6G SAT NW.
(3). When Bob switched to a video call, the change was identified and the video traffic was routed to 6GC#1 through the satellite backhaul link#2, enabled by 6G SAT(GEO), 6G SAT(LEO) and Ground Station#1 regarding the operator’s policy and the coordination of 6GC#1 and 6G SAT NW.
(4). During the video call, UE#1 can maintain the user experience with minimum interruption although the satellite backhaul link#2 keeps changing due to the movement of LEO satellite.
(5). After one day of photography, Bob uploaded all the photos and videos to the remote cloud using UE#1. The data traffic was identified and successfully routed to 6GC#1 through the satellite backhaul link#2 regarding the operator’s policy and the coordination of 6GC#1 and 6G SAT NW.
Sub-Scenario B: Resilient 3GPP services via temporary backhaul links
(1). UAVs were launched to a mountain area to search for the lost travelers. Upon reaching the target area. UE#2 connected to 6GC#2 through the satellite backhaul link#3, enabled by 6G SAT(GEO), 6G SAT(LEO) and Ground Station#1 regarding the policy and the coordination of 6GC#2 and 6G SAT NW.
(2). UAV and the remote control platform exchanged the information of the tasks, the control and command messages and the real time imaging data for localizing the travelers within the expected time via the satellite backhaul link#3.
(3). When Ground Station#1 met the failure issue caused by the local flood, Ground station#2 on an emergency vehicle was powered on and quickly registered to 6G SAT NW for backhauling service. The satellite backhaul link#4 was rapidly setup enabled by 6G SAT(GEO), 6G SAT(LEO) and Ground Station#2, and continually served UE#2 for the rescue tasks.
8.17.4 Post-conditions
Thanks to the flexible backhauling service, OpMNO#1 can provide Bob with reliable communication services globally in a cost-efficient manner. OpMNO#2 can support UAVs to complete the search-and-rescue tasks through flexible and resilient backhauling links.
8.17.5 Existing features partly or fully covering the use case functionality
1. Wireless Self-backhaul
TS 22.261 [14] clause 6.12 has specified a series of requirements about wireless self-backhaul for 5G systems as below.
The 5G network shall enable operators to support wireless self-backhaul using NR and E-UTRA.
The 5G network shall support flexible and efficient wireless self-backhaul for both indoor and outdoor scenarios.
The 5G network shall support flexible partitioning of radio resources between access and backhaul functions.
The 5G network shall support autonomous configuration of access and wireless self-backhaul functions.
The 5G network shall support multi-hop wireless self-backhauling.
NOTE 1: This is to enable flexible extension of range and coverage area.
The 5G network shall support topologically redundant connectivity on the wireless self-backhaul.
NOTE 2: This is to enhance reliability and capacity and reduce end-to-end latency.
This feature mainly focuses on the terrestrial network and intends to solve the challenge caused by the increased density of terrestrial access nodes by the functionalities like dynamic spectrum sharing, relays/IAB, SON and so on. However, satellite backhauling has distinct challenges, not addressed by the above solutions.
2. Satellite Backhaul
TS 22.261 [14] also specifies the requirements about routing, QoS handling and charging for 5G system using satellite backhauling as below.
The 5G network can also support multiple wireless backhaul connections (e.g. satellites and/or terrestrial), and efficiently route and/or bundle traffic among them.
The 5G system shall support a mechanism to determine suitable QoS parameters for traffic over a satellite backhaul, based e.g. on the latency and bandwidth of the specific backhaul.
NOTE 3: The case where a backhaul connection has dynamically changed latency and/or bandwidth needs to be considered.
The 5G system shall be able to support mechanisms to differentiate charging information for traffic carried over satellite backhaul.
However, the 5G system can just adapt to the impact of used satellite backhaul passively but cannot coordinate the backhaul system to be adjusted to improve the end-to-end connectivity. Moreover, one bottleneck of flexibility is ground station, which has not been taken into account in 5G.
8.17.6 Potential New Requirements needed to support the use case
[PR 8.17.6-1] Subject to operator’s policy, the 6G network shall support mechanisms to connect the base stations (onboard satellite) and the ground core networks using 3GPP satellite technology.
Editor’s note: the wording of 3GPP satellite technology is for FFS.
8.18 Massive user access over limited satellite links in disasters
8.18.1 Description
In extreme disasters such as earthquakes and floods, or in extreme environments like remote mountainous regions and offshore platforms, public network infrastructure may be completely disrupted. In these situations, users’ devices need to rely on satellites for communication backhaul.
However, satellite link bandwidth is limited, and network unavailability may occur when user demand exceeds capacity. While affected civilians have lower access priority than emergency responders, they still require basic services such as emergency calling and safety notifications to their families. This use case focuses on enabling access for more affected users under constrained bandwidth conditions.
When a disaster occurs, rescuers rapidly deploy temporary core network nodes in affected areas via emergency airdrops, drones, emergency vehicles, and other means. These nodes provide local access, authentication, and service processing, enabling users in the disaster area to connect without relying on the damaged public network.
However, as user authentication, subscription data are generally stored in the unaffected network, the temporary core network still require satellite interaction to obtain them when needed. In addition, the temporary core network relies on satellite interactions to support service exchanges, such as establishing calls between affected users and those in other areas.
To support large-scale user access under limited satellite bandwidth, the system should be capable of improving the utilization of satellite link resources. For example:
• The system reduces signalling overhead by minimizing message sizes, suppressing non-critical procedures like early media, and leveraging locally preconfigured user profiles to avoid unnecessary interactions.
• The system retrieves user data from the unaffected network in batches, and constructs a locally indexed mapping table based on user identities. By performing table lookups, the core parameters required for user registration and session establishment can be quickly retrieved, minimizing satellite interaction frequency and enabling efficient procedures without repeated external queries.
• The system enables adaptive codec selection by dynamically aligning satellite bandwidth availability with service strategies. Under constrained conditions, it automatically switches to low-bitrate modes, optimizing bandwidth usage while preserving essential communication quality.
• The system adaptively aligns service demand with satellite bandwidth availability in real time. By dynamically adjusting service priorities and resource allocation weights, it ensures that essential communication needs are met for as many users as possible under constrained conditions.
8.18.2 Pre-conditions
When an extreme natural disaster occurs in a certain area and the public network infrastructure is completely disrupted, rescuers quickly deploy temporary core network nodes on-site to communicate via satellite backhaul.
8.18.3 Service Flows
1. Disaster-affected users’ devices automatically search for available networks and can access the temporary core network without requiring SIM replacement or number change.
2. The system dynamically adapts to real time user demand and satellite resource availability, optimizing signalling and resource strategies to support large-scale access while minimizing bandwidth usage.
3. Despite limited satellite bandwidth, the system ensures continuous access to essential communication services for a large number of affected users.
8.18.4 Post-conditions
Upon restoration of the main infrastructure, users are seamlessly transitioned back to the original network with uninterrupted service.
8.18.5 Existing features partly or fully covering the use case functionality
TS 23.502 [30] defines the standard procedures for 5G user registration and session establishment, serving as the basis for UE identity authentication and PDU session setup in both terrestrial and satellite-enhanced networks.
TS 22.228 [138] defines general service requirements for the IMS, including support for voice services, IMS registration, session control, emergency service access
TS 22.261 [14] already includes the following relevant requirements for the satellite access:
Clause 6.46.7 Satellite and Relay UEs:
- A 5G system with satellite access shall support mobility management of relay UEs and the remote UEs connected to the relay UE between a 5G satellite access network and a 5G terrestrial network, and between 5G satellite access networks.
Clause 6.46.9 UE-Satellite-UE communication:
- Subject to regulatory requirements and operator's policy, a 5G system with satellite access shall support UE-Satellite-UE communication regardless of whether the feeder link is available or not.
- Subject to regulatory requirements and operator's policy, a 5G system with satellite access shall be able to provide QoS control of a UE-Satellite-UE communication.
- Subject to regulatory requirements and operator's policy, a 5G system with satellite access shall be able to support different types of UE-Satellite-UE communication (e.g. voice, messaging, broadband, unicast, multicast, broadcast).
Clause 7.4.2 sets latency and throughput targets for GEO, MEO, and LEO satellite access. Additionally, it requires high uplink/downlink rates and 99.99% service availability.
8.18.6 Potential New Requirements needed to support the use case
[PR 8.18.6-1] The 6G system using satellite access shall support mechanisms to improve communication QoS and capacity under constrained satellite link conditions, e.g. using signalling optimization, codec rate adaptation.
9 Immersive Communication
9.1 General
Immersive Communication has been described as follows by ITU-R: "This usage scenario extends the enhanced Mobile Broadband (eMBB) of IMT-2020 and covers use cases which provide a rich and interactive video (immersive) experience to users, including the interactions with machine interfaces." [27].
Normative requirements for mobile metaverse services, which include immersive communication, have been specified in [14] and [28]. Use cases considered in this clause of the present document will consider the gap with what they proposed and existing stage 1 specifications.
9.2 Use case on immersive gaming
9.2.1 Description
With the growing adoption of extended reality in the 5G era, it is expected that 5G-Advanced and 6G will scale immersive experiences for the enterprise and consumer mass market. In 6G, users of immersive technologies are expected to work, play, and interact seamlessly in both the physical and virtual worlds. These immersive experiences would be enabled by leveraging advanced XR) and multimedia features such as user-interaction via Avatar-Holographic conferencing, spatial collaboration on Hi-fidelity 3D objects, high resolution immersive 2D-3D Cloud Gaming, high resolution 360o 2D-3D content streaming and Multi-Modal AI-intermediated User Experiences.
User-interaction via Avatar or Holographic conferencing:
User-interaction in immersive experiences can be via Avatar or Holographic conferencing. This feature allows participants to engage in virtual meetings where they appear as lifelike avatars or holograms, creating a sense of physical presence despite being miles apart. By leveraging advanced motion capture and real time rendering, users can express emotions, gestures, and body language, making interactions more natural and engaging. This form of conferencing not only enhances communication and collaboration but also breaks down geographical barriers, enabling a more inclusive and dynamic way of connecting with others.
Spatial collaboration on Hi-fidelity 3D objects:
Spatial collaboration on high-fidelity 3D objects enables multiple participants regardless of their location to work together in immersive environments. The feature enables multiple users to interact with detailed 3D models in a shared virtual space, allowing for real time modifications and discussions. By providing a highly realistic and interactive representation of objects, users can explore every angle, scale, and detail as if they were physically present. This level of collaboration enriches immersive experiences, and is particularly beneficial in fields such as design, engineering, and architecture, where precision and detail are crucial. It enhances creativity, improves communication, and accelerates decision-making processes, making it an invaluable tool for modern collaborative efforts.
High resolution immersive 2D or 3D Cloud Gaming:
High-resolution immersive 2D or 3D cloud gaming with rich backgrounds is revolutionizing the gaming industry by delivering stunning visual experiences directly from the cloud. This technology allows gamers to access and play graphically intensive games on various devices (e.g. handheld gaming device, high-resolution television or monitor, head mounted displays) without the need for high-end hardware. By leveraging powerful cloud servers, games can render intricate details and complex environments in real time, providing seamless transitions between 2D and 3D perspectives. The rich backgrounds enhance the immersive experience, making players feel as if they are truly part of the game world. This advancement not only democratizes access to high-quality gaming but also paves the way for more interactive and visually captivating game designs, pushing the boundaries of what is possible in digital entertainment.
High resolution 360o 2D or 3D content streaming:
High-resolution 360° 2D or 3D content streaming is transforming how digital media is experienced, by offering an unparalleled level of immersion and detail. This feature allows users to stream content that can be viewed from any angle, providing a fully immersive experience whether in 2D or 3D. By leveraging advanced compression algorithms, powerful cloud infrastructure, high-resolution 360° streaming ensures smooth playback and high-quality visuals, even on devices with varying capabilities. This innovation is particularly impactful in fields such as virtual tourism, education, and entertainment, where it enables users to explore environments and scenarios as if they were physically present.
Multi-Modal AI-intermediated User Experiences:
Multi-modal AI-intermediated user experiences are redefining how interactions with technology is performed, by integrating various forms of input and output to create seamless, intuitive interactions. This approach leverages AI to interpret and respond to multiple modes of user interaction, such as voice, text, gestures, and even facial expressions and video streams representing user context. By combining these inputs, AI can provide more accurate and context-aware responses, enhancing the overall user experience across all forms of 2D and 3D content consumption discussed above. Users can control actions through a combination of voice commands and gestures, with the AI understanding and executing tasks based on the context, presenting the results via hi-fidelity 2D or 3D assets.
These extended reality and multimedia features and 6G capabilities such as enhanced communication, sensing, compute and AI would enable significant improvement in immersive experiences across many fields including education, healthcare, entertainment, and enterprise collaboration, bringing us closely to vision of seamless merging together of the physical and virtual worlds.
Enhanced Immersive gaming enables single or multiple participants in various location(s) to interact in real time with digital representation of object(s) and their surrounding environments, which are generated with hi-fidelity 3D assets and digital representation of human using avatars and/or holograms. These digital representations of object(s), environment and humans are created leveraging inputs such as visual, audio or tactile features and/or location information generated from sensors (such as cameras, RF sensors and haptic sensors) embedded in the vicinity/environment or on the participants. In addition, the interactions between entities in the immersive gaming experiences can be made more accurate, intuitive and seamless using cloud gaming and multi-modal AI-intermediated techniques.
Sustainability impact analysis:
Economic Growth:
The demand for immersive experiences drives technological innovation in many areas including communication, XR, compute and AI, with applications beyond gaming. Overall, immersive gaming contributes to industry growth, creating jobs and fostering economic activity while providing a rich and multifaceted experience.
Inclusion:
Immersive gaming offers numerous benefits, especially, social benefits such as fostering teamwork, emotional connection and a sense of community and camaraderie, irrespective of the physical distance of separation. The use case discussed below aims by no means to exclude people but instead, the goal is to include as many participants in this gaming experience as possible irrespective of their separation distance. In this use case, all the participants (i.e. players, cheerleaders and spectators) are localized, further consideration is required on whether this technology is needed. Also, significant consideration needs to be made to ensure that the technologies involved simulate seamless immersive gaming as much as possible to ensure that the experience of the participants is not negatively impacted or burdensome.
9.2.2 Pre-conditions
The University of Nee's basketball team is playing the last game of the tournament against a visiting team from the University of Zee. This is a home game for the University of Nee so its players would be participating in person at the Nee city basketball gymnasium. However, the team from the University of Zee are flying in from another country, but due to bad weather, two players from the University of Zee, the team's MVP, David and another player Steve flights were delayed so they would have to join the game virtually from location A.
As a result, the basketball players involved in this game are in two different locations, Nee city basketball gymnasium and location A. The immersive services at these locations are provided by Network Operators NeCom and ZeCom, respectively. Each location is outfitted with equipment (e.g. camera, sensors and edge computing resources) to support the immersive experience. Each player wears immersive gears including HMD/AR glasses and motion capturing devices such as haptics gloves, smart watches and compute puck/phones.
There are also dance routines performed by twelve cheerleaders physically present at the Nee city basketball gymnasium and three virtual cheerleaders at location B with the immersive experience in that location provided by Network Operator TeCom. Each cheerleader would also wear HMD/AR glasses and haptics gloves.
Also viewing the game are spectators (approximately one hundred people), some of whom are in-person at the Nee city basketball gymnasium and many more watching remotely. Leveraging high resolution 360o 2D/3D content streaming techniques, the in-person spectators view the game using HMD/AR glasses while the remote spectators can watch the game using the HMD, tablet, or even their phones.
9.2.3 Service Flows
1. When the game starts, all the immersive participants at the Nee City basketball gymnasium can view their surroundings, including in-person players, the cheerleaders and spectators through their immersive devices. In addition, virtual players and cheerleaders represented as avatars/holograms with accompanying movements, gestures and even facial expressions are rendered into their appropriate display devices. From the perspective of the virtual players and cheerleaders, they see themselves as participating in activities (i.e. playing basketball or dancing) in the Nee City basketball gymnasium and with background features showing highly realistic 3D digital representation of the objects (such as the court and baskets), players, cheerleaders and spectators that are present at the location.
2. For a truly immersive experience involving between local and virtual players, the ball is virtually created and interacted with by both in-person and remote players using gloves outfitted with haptic sensors. The movements and impacts on the ball from the players are communicated to all viewers in "real time". During halftime, dance routines are performed by cheerleaders, twelve of whom are local, while the remaining three are participating from location B. The movements, locations and gestures of the cheerleaders are combined virtually and displayed to the all the viewers.
3. In addition, the spectators can customize their viewing perspectives, such as the desirable viewing angle, location or resolution. Spectators interact with their immersive applications using voice commands and gestures, with the AI understanding and executing tasks based on the context, presenting the results via hi-fidelity 2D or 3D assets.
4. The immersive experience of players, cheerleaders and spectators are enabled by cellular traffic flows that can be classified into the following categories:
a) Compute flows: Traffic flows that are related to immersion and AI related compute that might be split between the compute processing available across cellular connections.
b) Conversational and game state flows: Traffic flows that are related to audio, video, avatar/hologram-based interaction between users. Also, traffic flows that are related to information representing the global immersion state across all users and objects.
c) Streaming flows: Traffic flows that are related to slower time scale streaming of information to certain users, e.g. spectators.
9.2.4 Post-conditions
With the advancements in immersive gaming technologies and 6G capabilities, local and remote players, cheerleaders and spectators can seamlessly participate and watch an immersive basketball game.
9.2.5 Existing features partly or fully covering the use case functionality
Existing immersive features from the 3GPP SA1 Mobile Metaverse Services Study and Work document in TS 22.856 [49] and TS 22.156 [28] can be used to enable some of the features in this use case.
But for XR display devices, pixel density below 60 PPD (Pixels Per Degree) triggers noticeable "Screen Door Effect", users can perceive individual display pixels, creating a mesh-like visual obstruction akin to viewing through a screen door [157]. This optical distortion phenomenon severely degrades immersive gaming experiences. For 2D/3D video, to provide users with an ultimate experience, a horizontal field of view (FOV) of 210 degrees and a vertical FOV of 100 degrees are required [158].
With the rapid development of video coding, both the video compression ratio and pixel quantization methods will become more efficient. For example, H.265 supports a video compression ratio of 400:1. Compared with the individual quantization of different color components in the RGB format, the YUV 4:1:1 chroma subsampling format can be adopted for pixel quantization, where only 12 bits are required to quantize one pixel [160].
In addition, many current display devices support a refresh rate of 90 frames per second (fps) with 8K resolution. Therefore, a refresh rate of 90 fps also needs to be considered as a typical value.
9.2.6 Potential New Requirements needed to support the use case
Table 9.2.6-1: Key Performance Indicator (KPI) for Immersive Gaming
Characteristic parameter (KPI)
Influence quantity
Max allowed end-to-end latency
Service bit rate: user-experienced data rate
(note 6)
Reliability
Area Traffic capacity
Message size (byte)
Transfer Interval
Positioning accuracy
UE Speed
Service Area
Compute flows: [5 ms - 20 ms]
Conversational and game state flows:
[50 - 100 ms]
Streaming flows:
[200 - 300 ms]
Player/Cheerleader
DL: (Note 1)
[640 Mbps] (8K, 120fps, compression ratio of 300 and 8 bits per color)
[240 Mbps] (8K, 120fps, compression ratio of 400 and 12 bits per pixel)
[200 Mbps] (8K, 90fps, compression ratio of 400 and 12 bits per pixel)
UL: [100 Mbps]
(note 2)
Spectator:
DL: (note 3)
[320 Mbps] (2D 8K, 120fps, compression ratio of 300 and 8 bits per color)
[120 Mbps] (2D 8K, 120fps, compression ratio of 400 and 12 bits per pixel)
[100 Mbps] (2D 8K, 90fps, compression ratio of 400 and 12 bits per pixel)
UL: [10 Mbps]
(note 4)
[99.9 – 99.99 %]
N/A
N/A
N/A
[≤10 cm]
Stationary, pedestrian
[38 m x 15 m]
(note 5)
NOTE 1: It is important to note that the data rates may change under different assumptions. Data Rate = Video resolution * (Bits per color * 3) * Refresh rate * # of eye buffer / (compression ratio), with the following assumptions: 2 eye buffers; 8K video resolution; refresh rate of 120 fps or 90 fps. The frame rate of 120 fps is assumed as it has been shown that such high frame rate help reduce the probability of simulator sickness [15]. 90 fps is the typical frame rate in current display device. compression ratio of 300 and 8 bits per color; or compression ratio of 400 and 12 bits per pixel [160]. A resolution of 8K (8192 x 4320) per eye can help to remove graphics pixelation and provide good XR user experience [159]. High refresh rates (e.g. 120 fps) are very correlated and inter prediction between frames increase compression. Some codecs (e.g. MV-HEVC) may further drop the bitrate requirement.
NOTE 2: Significantly higher compared to 5G to enable sensor sharing for split computation.
NOTE 3: DL data rate for spectator is assumed to be 2D 8K video
NOTE 4: UL tracking for spectator not as intensive as the player/cheerleader
NOTE 5: Average basketball court is 28 m x 15 m (adding 5 m on both sides for the spectator seating) to make a 38 m x 15 m basketball gymnasium.
NOTE 6: The provided values are targeted values and not strict requirements.
9.3 Use case on multi-media services with deterministic experience via collaborative processing among UE-network-cloud
9.3.1 Description
Usually, the limited image rendering capability of mobile devices poses challenges for services that require high-quality image processing capability like high order super-resolution and de-noising algorithm, as well as high-quality rendering capability like ray tracing effect for gaming. As shown in Figure 9.3.1-1, the GPU computing power for image rendering increasing in last ten years and the predication up to 2030 for mobile phone platform as well as the computing power required for advanced services has been summarized as below based on [16], [17], and [18]:
Figure 9.3.1-1: increasing computing power: offered by mobile GPU vs. required by services
It can be observed that with the failure of Moore's law, the growth of mobile platform computing power gradually slows down. Offloading rendering tasks to cloud (including operator managed data network, edge or central cloud), where sufficient computing power is hosted, is a promising trend to support advanced multi-media services in the future while lowering the weight and the power consumption of the XR glasses and mobile phones. UE-Network-Cloud synergized multi-media operation is a collaboration mode which allows mobile devices to dynamically offload part or all of rendering task to the cloud to improve the user experience. As shown in Figure 9.3.1-2 the offloading decision can be based on communication link status and computing resource availability in the cloud (operator managed data network, edge or central cloud) in order to maintain deterministic rendering task processing latency. There's an expectation for 6G systems to consider such dynamic rendering offload framework to adapt to the synergized multi-media service processing trend.
Figure 9.3.1-2: UE-Network-Cloud synergized multi-media operation framework
The rendering task split function is a function provided by the operating system (OS) of the terminals to map the rendering task request from the application (APP) to local or remote rendering resources. When a rendering task is requested to be executed, the task split function can select the local and/or the remote rendering resources based on the relevant factors, such as required processing quality, radio link status, estimated latency, available computing resource in operator managed data network, edge or central cloud, to ensure the deterministic user experiences for the requested multi-media service. The typical services which can benefit from UE-Network-Cloud synergized multi-media operation include photo enhancement and ray tracing for gaming and so on.
The UE-Network-Cloud synergized operation raises several new issues, as follows:
- Uplink data rate: The rendering task offloading operation usually requires a high uplink data rate to achieve higher service availability, considering the metadata to be uploaded for the rendering task is usually large with tight latency, e.g. 5 Mbits/50 ms, 150 Mbits/1.5 s (see detail in service flow part). However, the current network typically cannot guarantee sufficient uplink data rate especially at cell edge.
- awareness of real time communication link capability and cloud computing resource availability: The rendering task split function might not know the exact communication link capability such as data rate, latency, and UE power consumption for data transmission; the computing resource availability in operator managed data network, edge or central cloud is also unknown, which make it hard for the UE to make the splitting/offloading decision. Moreover, the latency for offloading rendering task to cloud is quite difficult to be always guaranteed in anytime/anywhere due to the varied radio channel condition and network load/coverage, so the UE needs to be aware of the real time network and computing resource status to switch between local and remote rendering.
- Computing task level Packet Delay Budget (PDB) requirement guarantee: The uplink metadata burst for a rendering task offloading arrives in a random way, and the latency requirement is intended for the whole data burst (i.e. the metadata of the task) rather than an individual packet. As shown in Figure 9.3.1-3, the current Guaranteed Bit Rate (GBR) QoS flow framework guarantees the data rate in a fixed averaging window and fulfils latency in a per packet basis which is suitable for periodic traffic. For the burst data carrying the metadata for rendering tasks, the latency and data rate should be guaranteed toward the whole data set rather than a specific packet. Therefore, the fixed averaging window as for periodic traffic is not suitable for bursty traffic. There is no suitable QoS type to guarantee the deterministic transmission latency for unpredictable uplink bursty data traffic.
Figure 9.3.1-3: Burst latency requirement
Moreover, in comparison to the accidental single-packet loss whose effect can be mitigated by the packet-loss-resilient solution, the successive packet loss or burst packet loss will incur more frequent video and audio buffering and play stalling. To solve such issue, AI/ML based approach to concealing the lost packets draws a growing attention, which is capable of recovering the missing audio packets as long as 100 ms, based on the assumption of 20 ms voice samples encapsulated into one audio packet [101].
9.3.2 Pre-conditions
1. Rendering resources are deployed in the cloud (operator managed data network, edge or central cloud).
2. The UE OS supports to map the rendering task from the APP to the local (in the mobile phone) and/or the remote (in the cloud) rendering resources.
9.3.3 Service Flows
Synergized photo enhancement case:
1. User A is at a tourist attraction, taking photos with his mobile phone. Once he clicks the photo button, the phone senor produces raw data of the photo, including up to 6 frames (4K per frame) of raw files spanning 300 ms for HDR reconstruction. Raw files retain the original uncompressed image data from the camera sensor, which is usually larger than compressed image formats such as JPEG and PNG and has unique advantages for image processing in practice.
NOTE 1: 2 to 10 frames are needed for HDR reconstruction based on [19]; 6 frames are assumed in this use case.
2. Upon reception of the rendering task request from the camera APP for photo enhancement, the rendering task split function inside the UE OS determines whether or not to upload the raw data to the cloud for more powerful post-processing to enhance the photo quality, for which some factors would be considered such as potential delay and UE power consumption for data transmission.
The rendering task split function obtains necessary information from the network, including real time communication link capability and cloud computing resource availability, and coordinates with the core network about whether the rendering task can be fulfilled (e.g. having bandwidth and latency assurance, locating appropriate computing resources for processing the photos, etc.).
NOTE 2: It is assumed that the core network is able to obtain the computing resource availability in operator managed data network, edge or central cloud.
3. If the rendering task split function decides to upload the photo to the cloud, the raw data for the photo is compressed to around 150 Mbits (based on the assumption of 6 frames × 4K raw data and compressed ratio assumption in [20], [21]) and uploaded to the cloud within around 1.5 seconds and downloaded the processed photo from the cloud. The UE can reduce the number of frames for uploading based on communication link capability. Therefore, the required uplink data rate in radio layer would be 150 Mbits/1.5 seconds = 100 Mbps.
NOTE 3: E2E latency of 3 seconds is assumed based on users' patience statistics as shown in [22], where 1.5 seconds is allocated for uploading to the cloud while 1.5 seconds for processing in the cloud and downloading to the UE.
4. If the rendering task split function decides not to upload the photo to the cloud, the local post-processing will be performed.
5. When User A views the photos, the downloaded photo or the local enhanced photo can be shown.
Synergized gaming enhancement case:
1. On the train back home to celebrate Chinese New Year, User A starts a delay-sensitive first-person shooter game displayed on XR glasses with friends online, where the gaming APP at the connected mobile phone produces the scene metadata, including 3D objects, lighting data, and materials, user actions, and then encodes the scene before transmits it to the XR glasses. The typical data size after compression can be 5-20 Mbits (assuming middle to high complexity 3D model including 0.35 to 2 million vertexes (200 bits per vertex) 3D models and compression ratio assumption for 3D model in [23]).
To win the battle, User A and friends must frequently exchange the status of their game roles by voice call supported by the gaming APP, all the time during the game even when network connection is poor (e.g. when the train going through the tunnel).
2. The gaming APP at the mobile phone submits the metadata to rendering task split function inside the OS for rendering, the rendering task split function can seek to utilize the more powerful computing in the cloud for higher quality such as ray tracing effect. The rendering task split function determines how to split the metadata to the cloud by considering some factors such as potential delay and mobile phone power consumption for data transmission. For example, the mobile phone selects to offload the slow changed scene background rendering to the cloud while performing rendering for fast moving objects using local rendering resources.
The rendering task split function further coordinates with the core network to check whether the rendering requirements can be fulfilled and sends an offloading request to the core network if all conditions are met.
3. The mobile phone uploads the metadata allocated for the cloud rendering within 50 ms [24] and downloads the rendering result.
4. The mobile phone combines the local rendered result and cloud rendered result to User A.
5. Once the radio condition worsens or the rendering resource is not available, the full local rendering can be activated to maintain the user experience.
9.3.4 Post-conditions
User A continues enjoying high quality photo taking and high-quality gaming experience.
9.3.5 Existing features partly or fully covering the use case functionality
The split rendering architectures defined in TR 26.928 [50] and TS 26.565 [51], and the corresponding QoS requirements in TS 22.261 [14], allow a UE to offload rendering requirements to the cloud for XR rendering. These architectures assume that the metadata including 3D models of the gaming and APP logic is pre-installed in the cloud, therefore only user pose information is needed to be transferred in the uplink and there is no dynamic offload decision on whether to render a specific scene in the cloud or with UE local resources (i.e. fully rely on the rendering result in the cloud). Therefore, the uplink requirement and dynamic offload decision requirement are not covered by the existing split rendering use cases and part of the downlink requirements on rendering result delivery are covered by the existing split rendering use cases.
It is worth noticing that Edge Enabler Client (EEC) is defined in TS 23.558 [52], which provides supporting functions needed for Application Clients. There is some similarity between EEC and the "rendering task split function" described in this use case. In this use case, instead of communicating with the Edge Enabler Server (EES) in the DN that only manages the edge applications, the rendering task split function coordinates with the core network for splitting/offloading rendering tasks.
In TS 22.261 [14] clause 7.6.1, there is requirement for 5G system to support interactive task completion during voice conversation: the 5G system shall support low-delay speech coding for interactive conversational services (100 ms, one-way mouth-to-ear).
9.3.6 Potential New Requirements needed to support the use case
[PR 9.3.6-1] Subject to user consent, the 6G system shall support burst type QoS with the KPI requirements summarized below:
Table 9.3.6-1: KPIs for multi-media services with deterministic experience via collaborative processing among UE-network-cloud
Use cases
Burst size
(note 1)
Max Allowed latency for a burst
(note 2)
Service bit rate: user-experienced data rate
UE speed
Service Area
Synergized photo enhancement
UL: [150 Mbits]
UL: [1500 ms]
UL: [100 Mbit/s]
Stationary or Pedestrian
Countrywide
Synergized gaming enhancement
UL: [5-20 Mbits]
UL: [50 ms]
UL: [100 – 400 Mbit/s]
Stationary or Pedestrian
Countrywide
NOTE 1: Assuming 6 x 4K raw pictures and compression ratio in [21] for photo enhancement. Assuming 3D models including 0.35 to 2 million vertexes and compression ratio assumption for 3D model in [23] for gaming enhancement.
NOTE 2: 1500 ms is derived from the E2E latency of 3 s (based on users' patience statistics as shown in [22]) and 1.5 s for processing in the cloud and downloading. 50 ms uplink latency is derived from [24].
[PR 9.3.6-2] Subject to user consent, the core network of the 6G system shall provide means to coordinate with a UE to make decisions on splitting/offloading rendering tasks dynamically based on the real time network status and the computing resource availability in the Service Hosting Environment, edge or central cloud.
NOTE 1: In the case of tethered XR headset connected to a UE, the latency, computing resource consumption and power consumption (incurred by application processing and communication processing) of both the UE and the XR Glasses need to be also considered.
NOTE 2: It is assumed that the core network is able to obtain the information of computing resource availability in the Service Hosting Environment, edge or central cloud, which enables the core network to locate appropriate computing resources for the rendering tasks.
[PR 9.3.6-3] Subject to operator policy, the 6G system shall provide efficient means to inform an authorised third party (residing in the network or in a terminal) the guaranteed throughput (current and/or predicted, within the boundary of the 6G system) of the associated ongoing session.
[PR 9.3.6-4]: The 6G system shall be able to provide deterministic user experience for multi-party call with the KPI requirements summarized below:
Table 9.3.6-2: KPIs for deterministic user experience for multi-party call
Use case
Max. mouth-to-ear delay
Audio-video synchronisation thresholds
Max. duration of consecutive packet losses
Throughput
(UL and DL)
Availability
UE speed
multi-party call
[100ms]
(note 1)
[- in the range of [125 ms to 5 ms] for audio delayed
- in the range of [45 ms to 5 ms] for audio advanced]
(note 2)
[100 ms]
(note 3)
[>=30Mbps]
(note 4)
[99%]
(note 5)
up to [500km/h]
(note 6)
NOTE 1: one-way delay [102].
NOTE 2: as defined in TS 22.261 [14] clause 7.6.1.
NOTE 3: refers to the capable of recovering the missing audio packets as long as 100 ms, based on the assumption of 20 ms voice samples encapsulated into one audio packet [101].
NOTE 4: it is derived based on 4K 60 fps video encoded with HEVC [103].
NOTE 5: it means the probability to provide the above KPIs during the time that a user intends to use the above services.
NOTE 6: it is to consider the high-speed train scenario as in TS 22.261 [14] clause 7.1, which is intended as a targeted value and not a strict requirement.
9.4 Use case on XR rendering offload support
9.4.1 Description
There is a growing demand for people to use diverse types of devices other than smartphones, which connect to a mobile network system [27]. Then, in 6G, a number of devices are expected to be connected to the 6G system. With this trend, wearable devices are expected to be more popular, but even with such devices like XR devices, users would like to experience immersive applications which require much computing capability for processing application data. However, due to the limited computing capability, user experience could be affected. Therefore, there will be strong needs for such devices to be able to offload application data processing to the edge/cloud server.
In current computing technology, application data processing can be offloaded to edge/cloud server(s) which are completely separate from the mobile network system. However, from the user experience point of view, the 6G system shall support network and/or device control based on computing offload use of user devices.
This use case aims to provide a service scenario when a user wants a computing offload service supported by the network and potential requirements for the 6G system.
9.4.2 Pre-conditions
John's AR glasses can be connected to a mobile network operator's network.
John subscribes to a cloud rendering service to offload the processing functions of the terminal.
NOTE: The cloud rendering service is provided by a function outside the 3GPP system.
9.4.3 Service Flows
1. John is now walking outside. He wants to communicate with one of his friends by using holographic interaction application which is adequately coordinated for his AR glasses.
2. However, the glasses are lightweight, and it will take a lot of time to render the hologram image, so he decided to use the cloud rendering service he contracted to improve the experience.
3. The mobile network obtains information from the cloud rendering service and the interaction application that John uses, such as the communication related requirements (e.g. required latency) and the computing related requirements (e.g. processing capacity, energy consumption).
4. Based on this information, the mobile network controls QoS and provides guidance to the cloud rendering service and application on optimal offloading (e.g. placement of compute processing nodes).
9.4.4 Post-conditions
The mobile network was able to understand the computing requirements of John's applications and the offload requirements of the cloud rendering service, and set up QoS for John's device, allowing him to use the XR offload service comfortably.
9.4.5 Existing features partly or fully covering the use cases functionality
Solution for QoS modification based on communication requirements from application is specified in clause 4.15.6.6 of TS 23.502 [30].
9.4.6 Potential New Requirements needed to support the use case
[PR 9.4.6-1] Subject to privacy considerations and regulatory requirements, the 6G network shall be able to enable compute offloading to a Service Hosting Environment for third party applications.
9.5 Use case on seamless immersive reality in education
9.5.1 Description
Immersive reality combines immersive telepresence and immersive collaboration. Immersive telepresence allows remote participants to appear as physically present in the very same environment as co-located participants. The remote participants perceive the environment and other participants as if they were there in person, thanks to stimulation of their visual, aural, and haptic senses. Immersive collaboration provides a new way of interaction with other people and objects.
Immersive reality technology opens up new opportunities in the education sector, where students are able to collaborate, discuss, and learn in an immersive setting with remote and co-located participants. Participants interact with each other in a more natural way in an immersive reality environment, and participants are also able to interact more naturally with virtual or digital representations of real objects.
The immersive classroom may be local (where all students are physically co-located and learn with virtual objects), hybrid (with both physically co-located as well as remote participants), or fully immersive (where both students and instructors are virtually present).
Figure 9.5.11: Hybrid Immersive Classroom Scenario
For example, consider a hybrid immersive chemistry classroom, exploring and assembling 3D models of complex molecules, shown in Figure 9.5.11. The "orange-handed" student and the teacher represent participants remote to the physical classroom while the "red-handed" and the "blue-handed" students represent participants physically present in the classroom. The equipment local to the physical classroom, e.g. cameras, and wearables worn by the students, senses the real environment, communicates the actions and the outcome of the collaborative molecule assembling, and communicates to establish immersive co-presence of the remote and physically present participants.
Further justification and background for the KPIs for this use case is found in the following table [31]:
Table 9.5.11: Justification and clarification of KPIs
KPI
Target Range
Justification
User-experienced data rate
[Mb/s]
< 250
For DL, but also for UL
Area traffic capacity [Mb/s/m2]
< 250
< 20
Indoor: per floor in a multi-story building
Wide area: for immersive experience "on the go"
Mobility
seamless HO
Pedestrian for participants in the classroom
End-to-end latency
[ms]
< 10
< 50
< 150
Split rendering
Voice
Collaboration
Reliability [%]
99.9 – 99.999
Depending on data stream
Positioning accuracy
[cm]
≤ 10 horizontal
≤ 10 vertical
Positioning of AR glasses and sensors. Network-assisted positioning can be used for e.g. sensors, but high positioning accuracy for AR typically requires device-based sensors and sensor fusion.
Sustainability impact analysis:
Material resources: Increased electronic waste from the disposal of devices and network equipment. Increased material consumption from producing the hardware components and expanding network infrastructure including raw material extraction, manufacturing processes, and transportation
Emissions: Reduction of emissions by a reduction in physical travel for work or education.
Inclusion and Equality: Enhances educational opportunities of the population regardless of their location, and improves the efficacy of the education process, both for the students and for the teachers. However, there is a potential digital inequality depending on connectivity access, information technologies (IT) literacy and economic status.
Trustworthiness: Preserved/uncompromised privacy is key for the enablement of this service. However, there are still potential risks for privacy intrusion associated with localisation and positioning data.
9.5.2 Pre-conditions
The students in the physical classroom are each using an AR device and have joined the classroom application.
Both AR devices are very simple and light so that the students can use them for long periods of time, which means that the AR devices have limited battery and limited computing capabilities.
The physical classroom has a local server with available computing resources.
9.5.3 Service Flows
1. The teacher, who is located remotely, starts the classroom lesson and pulls up a 3D model of a complex molecule. The AR devices of the students in the physical classroom uses the local server to perform the necessary computation tasks to enable their AR devices to display the same 3D model of the complex molecule (e.g. shadows and lighting aspects of the molecule).
2. The teacher asks the students in the classroom and the remote student to work together to disassemble the molecule and use the components to construct a new one.
3. A combination of the information from the wearables of the students and the cameras present in the physical classroom sense the environment, e.g. the students' hand gestures and movements.
4. The AR devices of the students in the physical classroom uses the local server to perform the necessary computation tasks (e.g. via an AI/ML model) to anticipate their hand gesture and movements and translate them into specific actions on the 3D molecule model.
9.5.4 Post-conditions
None.
9.5.5 Existing features partly or fully covering the use case functionality
- Sensing: immersive experience requires the human sensory system to receive realistic stimuli from a mixed or virtual reality. Some scenarios may use ISAC or may apply sensor fusion of network and sensor data of connected sensors. Sensing has been covered in Stage-1 in TS 22.137 [6].
- Positioning: this use case requires accurate positioning for a seamless immersive experience. Positioning requirements are defined in TS 22.261 [14]. However, the KPIs from this use case need to be enhanced with greater accuracy compared with the 5G KPIs.
- Media synchronisation: low E2E latency, in combination with the synchronisation of different media for each participant, as well as synchronisation of inter-participant media are needed to ensure a coherent and realistic user experience. This has been covered by TS 22.156 [28] (e.g. [R-5.1.1-002] and [R-5.1.1-003]).
- Digital immersive mapping as a service: to assist seamless immersive reality, digital immersive mapping is provided as a service by the network. This has been covered by TS 22.156 [28] (i.e. clause 5.2.1. Localized mobile metaverse service).
- Service continuity: service at a minimum level to provide a sufficient and for the end user comprehensible and satisfactory QoE across diverse locations ranging from local wireless networks to the wide area network. This has been covered by TS 22.156 [28].
- Distributed Federated Learning: distributed FL involving multiple UEs is discussed in TS 22.261 [14]. What is not discussed in federated inference, where an AM/ML model includes information from multiple cameras/wearables/sensors for inference.
9.5.6 Potential New Requirements needed to support the use case
[PR 9.5.6-1] Subject to operator policy, the 6G network (e.g. core network) shall enable the support of computing tasks in the Service Hosting Environment to render 6DoF video and spatial audio.
[PR 9.5.6-2] Subject to operator policy, the 6G network (e.g. core network) shall enable the offloading of computing tasks to the Service Hosting Environment to render 6DoF video and spatial audio to a 3rd party.
[PR 9.5.6-3] Subject to operator policy, the 6G network (e.g. core network) shall support hosting of e.g. an AI/ML model in the Service Hosting Environment based on latency, transport load or data privacy requirements.
[PR 9.5.6-4] Subject to operator policy and privacy considerations, the 6G system shall support means to coordinate data flows from UEs (e.g. cameras/wearables/sensors), associated with a specific place (e.g. a classroom, meeting room, office), to enable e.g. an AI/ML model to improve the user's perception of and interaction with an immersive scene, e.g. to more accurately predict scene changes or object movements within that specific place.
Editor's Note: terminology and definition of 'place' is FFS
Editor's Note: clarifications on privacy considerations are FFS
Table 9.5.6-1: KPIs for Seamless Immersive Reality in Education
Scenario
User-experienced data rate
[Mb/s]
Area traffic capacity [Mb/s/m2]
End-to-end latency
[ms]
Positioning accuracy
[cm]
Mobility
Indoor
Outdoor (Wide Area)
Split rendering
Voice
Collaboration
Horizontal
Vertical
Seamless Immersive Reality in Education
[< 250]
[< 250]
[< 20]
[< 10]
[< 50]
[< 150]
[≤ 10]
[≤ 10]
Pedestrian
9.6 Use case on collaborative service in multi-site involved immersive communication
9.6.1 Description
Compared to AR/VR services under existing 5G networks, 6G immersive XR services aim to provide an ultimate user experience through capabilities such as ultra-high resolution, high frame rate, wide color gamut, high dynamic range, wide field of view, and advanced encoding/compression techniques. It supports more natural interaction methods (e.g. voice interaction, gesture interaction, head interaction, eye tracking) to achieve complex perception of users and environments. The 6G system and service platform need to collaborate to complete functions such as rendering, synchronization, encoding, distribution, storage and management of immersive XR services, and support real time transmission and processing of panoramic video data, field of view video data, etc.
6G immersive XR services present new challenges for platforms, networks and terminals. Based on 3GPP R17 XR project research, current XR service data rates are 30/45 Mbps downlink and 10 Mbps uplink for AR scenarios; future requirements with increased video resolution and frame rate can reach Gbps levels. While data volumes increase, immersive XR services have data characteristics of variable packet size, non-integer period, jitter and multiple streams. Concurrent traditional data flows (signaling flows, audio streams, video streams) also exist in the network. In addition to stronger computing capabilities, ensuring real time transmission of relevant data within the 6G system between various processing nodes is also a key aspect. The timeliness of data transmission will impact the success rate and quality of service, so 6G networks must ensure efficient transmission of large volumes of data to maintain SLAs.
This case demonstrates the 6G network’s capability to provide computing service for enabling a multi-site involved immersive communication scenario.
9.6.2 Pre-conditions
Alice, Bob and Lisa, Tom are customers of Network Operator A, a 6G service provider.
On New Year's Eve, a public event will jointly held across two cities - City A and City B. Participants had the option to attend in-person at the event venues for massive gatherings or experience an immersive viewing through different presentation methods (e.g. outdoor large screens, mobile phones/pads, VR/AR glasses) to feel as if present.
Alice attended on-site at the City A venue. Bob attended on-site at the City B venue while Tom watched the event in the bus from City B to City A. Lisa experienced the event remotely from her home in City C through a wireless VR device.
To meet the needs of multi-dimensional perspectives and long-distance follow-up shooting, the event organizer chooses wireless cameras to capture video streams. For saving the time of encoding at camera side and keeping as much as details of video stream, low-compression and even uncompressing ([161], [64], [67]) are selected for cameras on-site. There are also multiple sensors on site collecting signals. Network Operator A’s network provides wireless transmission for video streaming and sensors data.
Besides providing communication service to enable this use case, Network Operator A's 6G network also provides computing service to the organizer’s platform for stream processing to fulfil demands on immersive presentation. Rendering, encoder/decoder, and other capabilities are also pre-deployed in the network on the event venues in City A and City B.
9.6.3 Service Flows
Sub-scenario 1 (Immersive Program Viewing):
For example, two singers perform the same song on stages located in City A and City B respectively. Through the immersive viewing service, Alice, Bob and Lisa, Tom - attending from different physical locations using different means - perceive the experience as if the two singers were performing on a single stage together.
The following Figure 9.6.3-1 illustrates stream flows between City A and City B venues during the live interaction show.
NOTE: Not all entities and control messages are shown in above figure for the sake of simplicity.
Figure 9.6.3-1: Stream flows between two venues during the live interaction show
1, 1`: Local performances are presented to on-site audiences in City A and City B event venues. Meanwhile, multi-dimensional data (such as audio, video, stage view, audience seating view, 3D models, motion, etc.) from both venues is being acquired and sent to on-site core networks.
2, 2`: The core network selects corresponding processing nodes (e.g. core network nodes with computing resources, edge server) to process data (such as data cleaning, compression, encoding, decoding or rendering) and routing data traffic between processing nodes based on the work load of the nodes and computing service requirement received from organizer’s platform.
3, 4: The rendered data from City B is being transmitted to City A through the 6G network and is holographically presented to the on-site audience.
3, 4`: In the opposite direction, the rendered data from City A is being transmitted to City B through the 6G network and is holographically presented to the on-site audience.
The 6G network also needs to collaborate with remote audience service VR application servers to render and fuse the data processed from Cities A and B into a global stage scene data and then distribute it to wireless VR devices located in different locations for remote audiences.
All these data are used to reconstruct through immersive services and presented via Lisa’s wireless VR device.
During Tom’s bus moves from City B to City A, Tom can keep receiving the render data from 6G network with the requested quality of immersive presentation on VR devices and enjoy the live show as he is in the event venue.
Sub-scenario 2 (regular Communication Services Remain Unaffected):
Throughout the duration of the event, audiences' regular communication services such as making phone calls, sending text messages, and IMS multimedia services are unaffected.
Alice shares her experience at the event venue with friends through a video call, and the video interaction remains smooth without issues like audio-video desynchronization or stuttering.
Bob successfully receives congratulatory New Year greeting messages sent by his friends from all the country, without delays.
A phone call from Lisa's friend reaches her successfully, and the voice calling service quality does not degrade.
During this event, there are substantial concurrent up-streams and down-streams of multiple data types (user interaction sensing data, AI algorithm data, AR/VR interaction data) which poses challenges for 6G networks. 6G networks must not only function as a data conduit but also enable required real time data processing operations to ensure user experience.
9.6.4 Post-conditions
Alice, Bob, Tom and Lisa have equivalent viewing and interactive experience during the event.
Alice, Bob, Tom and Lisa's regular communication services are unaffected during their participation in the event.
9.6.5 Existing features partly or fully covering the use case functionality
Currently, communication requirements as described in TS 22.261 [14] clauses 7.1, 7.6.1 and 7.10 are provided. And it is assumed that there are always sufficient computing resources at the selected one node for running rendering server. 5G network lack mechanisms for intermediary data for collaborative tasks in large-scale rendering.
An "Audio and Video Production" use case which uses HD and Ultra HD resolutions deployed over 5G system is introduced in TS 22.104 [64]. Some normative requirements related to support of media service are in TS 22.263 [67].
9.6.6 Potential New Requirements needed to support the use case
[PR 9.6.6-1] Subject to operator’s policy and regulation, the 6G network shall be able to provide 6G Computing Service controlled by the core network of 6G system to an authorized third-party for supporting rendering.
[PR 9.6.6-2] Subject to operator’s policy, the 6G network shall be able to maintain user experience (e.g. for immersive communication) with minimum interruption when the selected computing resources changes within the Service Hosting Environment used for the 6G Computing Service for XR rendering e.g. during UE mobility.
[PR 9.6.6-3]: 6G system shall be able to support immersive media content production with the KPI requirements summarized below in Table 9.6.6-1.
Table 9.6.6-1: Performance requirements for immersive media content production via wireless link
Use Cases
Characteristic parameter (KPI)
Influence quantity
Max allowed end-to-end latency
Service bit rate: user-experienced data rate
(UL)
Service bit rate: user-experienced data rate
(DL)
Reliability
# of UEs
UE Speed
Service Area
Immersive media content production via the wireless link
[100ms]
(note 3)
(Note 1)
[2.65 Gbps] (4K, 60fps, 10 bits per color, compression ratio of 4)
[1.1 Gbps] (4K, 60fps, 10 bits per color, compression ratio of 10)
[7.96 Gbps] (8K, 60fps, 10 bits per color, compression ratio of 6)
[4.8 Gbps] (8K, 60fps, 10 bits per color,
compression ratio of 10)
N/A
[99,99 %]
4
(Note 2)
Stationary, Pedestrian
30 m x 30 m
NOTE 1: This bit rate is assuming to use the JPEG-XS [352] pre-compression encoding method to ensure both high-quality images and high encoding efficiency, while also realizing lower encoding delays [161]. Which is intended as a targeted value per UE and not a strict requirement.
NOTE 2: 4 cameras are used in simple stage [353], more cameras may be needed for condition of larger number of performers, with complex stage and lighting conditions. It depends on the deployment.
NOTE 3: One-way delay that is from camera to holographic player.
9.7 Use case on multiple application media synchronization
9.7.1 Description
In critical immersive communications such as 3D remotely controlled repairs or surgery, users may be equipped with multiple devices for multiple media components (e.g. haptic device for pressure, VR glasses for video, wireless headphones for audio) and each device receives traffic for the corresponding media component from the networks. Figure 9.7.1-1 depicts an example.
Unless the user receives synchronized information from all application media flows involved in the remotely controlled activity – in this example, presented to the user through haptic gloves and headset – the user can easily make errors, e.g. destroying the equipment that is being remotely repaired, or harming the patient by remote surgical procedures.
Remote Repair Room
Remote Technician
App for remote control/haptics
UE
RAN
Core Network
App for visual
App for sensor data
App for audio
Remote Repair Room
Remote Technician
App for remote control/haptics
UE
RAN
Core Network
App for visual
App for sensor data
App for audio
Figure 9.7.1-1: An example of remotely controlled repair
Then, the tighter synchronization between different media components is required. For example, a group of data carrying multiple media components is defined as a chunk of haptic data and chunk of video data and the group of data needs to be delivered collectively within a required time window.
Also, the group of data including more than one media component can be delivered via more than one traffic flows and/or UEs when each device is receiving corresponding media component via specific flow.
In a case when the network condition is poor or varying, it would be hard to support this level of synchronization without the network to understand the inter-dependent packets across multiple flows.
The corresponding 5G requirements from TS 22.261 [14] below is defined for the multi-modality but it does not cover the above mentioned scenario.
-The 5G system shall enable an authorized 3rd party to provide policy(ies) for flows associated with an application. The policy may contain e.g. the set of UEs and data flows, the expected QoS handling and associated triggering events, other coordination information.
-The 5G system shall support a means to apply 3rd party provided policy(ies) for flows associated with an application. The policy may contain e.g. the set of UEs and data flows, the expected QoS handling and associated triggering events, other coordination information.
In this use case there are different distinct application flows that must be synchronized, whereas in existing requirements, the media for a single application is synchronized.
9.7.2 Potential New Requirements needed to support the use case
[PR 9.7.2-1] Subject to operator policy, the 6G system including IMS shall support the synchronization of independent traffic flows of one or more applications, to be delivered to more than one device (i.e. UE or tethered devices).
NOTE: The applications whose traffic flows are synchronized are associated. It is assumed that the association is known to the 6G system.
Table 9.7.2-1: KPIs for media synchronization for multiple applications
Use case
Audio-Haptic synchronization thresholds
Video- Haptic synchronization thresholds
Audio-video synchronisation thresholds
Remotely controlled repair
- In the range of [50 ms to 0 ms] for audio delayed (NOTE 1)
- In the range of [25 ms to 0 ms] for audio advanced (NOTE 1)
- In the range of [15 ms to 0 ms] for video delayed (NOTE 1)
- In the range of [50 ms to 0 ms] for video advanced (NOTE 1)
- In the range of [125 ms to 5 ms] for audio delayed (NOTE 2)
- In the range of [45 ms to 5 ms] for audio advanced] (NOTE 2)
NOTE 1: as defined in TS 22.261 [14] clause 6.43.1.
NOTE 2: as defined in TS 22.261 [14] clause 7.6.1.
9.8 Use case on holographic telepresence in healthcare
9.8.1 Description
Holographic telepresence will enable users located in multiple remote locations to interact with each other in real time as if they were together in a single shared space, for example, one projected as holographic presence appearing in the office to present the document in front of other colleagues while actually being in the car. The 6G system is expected to facilitate such seamless, real time immersive interaction, allowing participants to engage in activities ranging from virtual meetings to collaborative work and remote medical consultations. Utilising immersive media such as hologram can enhance communication efficiency and user experience, bridging geographical distances and creating a sense of co-presence among users.
Although audio and video calls can realise interactions between the participants in different locations, those interactions are still very different from those which can be achieved when all participants are in the same physical location. The reasons for this difference can be the limitation of capturing devices such as cameras and microphones to capture the atmosphere of one location as well as the limitations of displays to render that atmosphere in another location. With the real time telepresence based on holography and avatar technology, the participants in multiple locations are possible to enjoy truly immersive experience as if all of them would interact in the same physical location. However, to realize holographic communication, there are multiple challenges that 6G network need to address.
Bandwidth, latency and Computing of Holographic communication
AR/VR has been identified as one of the important technologies to realise immersive communications in 5G. The immersive experience can be further enhanced with more immersive media including hologram, avatar and multi-sensorial media. Hologram displays require a large volume of data about the user’s motion and sensory inputs captured by cameras, sensors and actuators as well as volumetric data from multiple viewpoints to accommodate 6DOF movements of the observer [104]. Thus, the synchronized multi-modal data streams for holograms may require extremely high bandwidth, potentially network performance in the range of 100 Gbps or beyond [105], [106]. To reduce the challenge to the transmission bandwidth, various compression and decompression technologies are developed such as point-cloud techniques and even powered by AI. Also, holographic avatars or computer-generated holograms that replace photo-realistic hologram are considered and may reduce the demand on transmission rate to 1Gbps or lower. However, the additional processing latency may consume the delay budget reserved for transmission while the increased computation workload must be optimized or offloaded to support portable display devices that the users can access ubiquitously.
Security of holographic communication
Even with such ultra-high data rate communications, confidentiality must be guaranteed as it is today. Widely used encryption algorithms can only support data rates up to 38.70 Gbps on mobile environments [107], [108]. Existing algorithms cannot cope with the required data rates for communications utilizing immersive media so that shall be enhanced in 6G systems. The 6G system shall support encryption and integrity protection algorithms that can handle data rates in the 100 Gbps range. As the computational performance of the chipset on UE improves over the years, and the exploration of distributed computing technologies, the possibilities of the existing algorithms achieving the required performance also improves. Considering the amount of data which needs to be encrypted and integrity protected, having a faster algorithm will improve overall efficiency.
On the other hand, the captured and transmitted data for the holograms contains a significant amount of personal biometric information such as facial features, voiceprints, which need proper privacy protection to ensure the compliance to regulatory requirements.
Sustainability impact analysis:
Economic Growth: Holographic communication cannot be achieved without technological advancements in many areas including capturing devices, displays and communications. Once holographic communication is achieved, we can expect a new type of collaborations in many industries, which can lead to further economic growth.
Emissions: Reduction of emissions will be expected by reducing physical travel.
Inclusion: Holographic communication can realise closer interaction among parties more frequently than ever without traveling. It also brings an opportunity for those who are unable to travel to explorer a remote location as if they are actually in that location.
9.8.2 Pre-conditions
A patient Alice is in the house, while the practitioner Bob is at Clinic A and the practitioner Tom is at the Hospital B.
The house, clinic and hospital are equipped with dedicated sensory systems (including multiple cameras, microphones and other sensors), holographic display device and a mobile communication device, while the practitioners additionally have XR devices to capture the pose and gesture, and haptic information.
The operator managed edge/cloud-based application servers can execute avatar generation, hologram computation and rendering.
The mobile network and communication devices supports 6G.
9.8.3 Service Flows
Scenario#1 One-to-One diagnostic
Alice and Bob start the holographic communication application on their mobile communication devices.
They enter a session ID to establish a connection between two locations.
They connect their displays to mobile devices to start receiving/sending the holographic data.
The sensory system in Alice’s house captures her image and status and then send them to the cloud-based AS via the mobile device for processing and rendering.
The AS generates Alice’s hologram data based on the received multiple data streams with individual modal media such as audio, video, haptic information, etc. and sends it to the mobile device in the clinic.
Upon receiving the hologram data, the display can project Alice’s 3D image in the clinic.
Bob with XR device can examine Alice’s status as if Alice is in the clinic.
Similarly, Bob’s hologram data including associated gesture and pose is sent to the communication device in Alice’s home via the AS and displayed in naked eyes.
Alice can receive feedback from Bob as if Bob is in the house.
All data transmitted over wireless links is encrypted as well as integrity protected to ensure the security of the communication.
Scenario#2 Joint diagnostic(avatar-based)
Alice, Bob and Tom set up a holographic conference via the mobile communication device. For the purpose of privacy, Tom chooses to use holographic avatar which is generated in the network.
When Tom makes an examination and talks, the multi-modal data streams captured in the hospital and his holographic avatar data are transmitted to the selected edge servers of Alice and Bob for rendering, which may include the modal data of Tom’s pose, gesture, motion, or facial expression, the conversational voice, and the interactive information. As the examination goes on, the ty of modal data may change accordingly.
Alice’s and Bob’s mobile devices send captured hologram related data to the edge servers chosen for other participants respectively.
Tom’s animated holographic avatar is simultaneously generated in Alice’s and Bob’s edge server and then is converged with hologram of Bob or Alice individually to render the whole scenario.
The rendered virtual conference is transmitted to Alice’s and Bob’s mobile device for display.
9.8.4 Post-conditions
Alice and Bob can engage in real time, bidirectional holographic communication as if they are in the same room, despite the geographical distance. This will be beneficial especially when they need close interaction during the consultation, for example rehabilitation.
Bob and Tom can make the join diagnostics for Alice’s health as if they are in the same room. And Tom’s avatar can reflect his gesture, motion and facial expression correctly and synchronized.
9.8.5 Existing features partly or fully covering the use case functionality
The service requirements for avatar-based real time communication have been specified in clause 5.2.2 of TS 22.156 [28]. The high data rate requirements for 5G use cases are specified in clause 7.1 of TS 22.261 [14]. However, these are not requirements for 6G systems and are insufficient for realising 6G immersive media communication that utilises realistic 3D image.
9.8.6 Potential new requirements needed to support the use case
[PR 9.8.6-1] The 6G system shall support holographic communication with the following KPIs.
Table 9.8.6-1: Performance Requirements for Holographic Telepresence
Use Case
Characteristic parameter (KPI)
Influence quantity
Max allowed end-to-end latency
Service bit rate: user-experienced data rate
Reliability
# of UEs
UE Speed
Service Area
Holographic telepresence in Healthcare
Presence:
[<100ms]
Movement:
[<20ms]
(note 1）
4K video resolution:
[~ 400Mbps]
(30fps, 8 parallax, 15 bit/pixel, H.265/H.266 with compression ratio 100)
8K video resolution:
[~ 900Mbps]
(30fps,8 parallax, RGB, compression ratio 200)
Hologram/Point cloud
[500Mbps-1Gbps]
(30fps,8 parallax, RGB, AI-based)
(note 2)
[99.9%-99.999%]
1
Stationary, Pedestrian
[5 m x 5 m]
(note 3)
NOTE 1: For real time presence of hologram, refer to [169]; for more interaction, the motion-to-photon delay will consider the effect of cyber sickness [170]
NOTE 2: For volumetric-based holography, the bandwidth is impacted by the effective pixel count, which is related to the resolution, colour quality and bit-depth [106] the data rate is calculated based on [105] for uncompressed raw data, and optimized by different compression algorithms with different compression rate stated in [171]. With the help of AI technologies such as Neural Holographic Video Compression (NHVC), the bandwidth for transmitting hologram will be optimized a lot [173]
NOTE 3: The size of a small room/office.
Editor’s Note: The KPI value for holographic communication is FFS.
[PR 9.8.6-2] Subject to operator’s policy, the 6G system shall support mechanisms to adapt QoS of the media traffic in a conversational holographic communication to achieve consistent user experience under varying network conditions.
Editor’s Note: The consideration of other requirements necessary for realizing the above use case is FFS.
9.9 Use case on mixed reality gaming
9.9.1 Description
Immersive communication use cases will continue to drive requirements on mobile networks. The need for slim light-weight Mixed Reality (MR) / AR glasses has been emphasized announcement of device manufacturers. Multiplayer MR gaming is considered a target use case for 2030 timeframe.
We here consider a multiplayer game with users having a shared spatial map of the environment. The virtual scene is updated and placed in the same way and location for all players simultaneously. A 6G network should provide differentiated connectivity and beyond-connectivity services with consistency in performance.
For a shared gaming experience, the technical requirements are determined by the upstream of the environment from different players, the downstream of virtual objects, and a game logic that defines the connection between reality and virtual objects for multiple users.
The following gaming scenario is considered:
Users A and B are simultaneously acquiring and upstreaming a spatial map of the local environment to a cloud server. In the cloud, a shared virtual scene is generated and transmitted to Users A and B. The users are moving outdoors and indoors in a Dense Urban, Urban and Rural scenario.
Sustainability impact analysis:
Energy resources: The increased usage of energy connected to the new MR equipment should be considered by focusing on energy performance and consumption.
Material resources: The material resources connected to new MR equipment should be considered by focusing on total material use.
Emissions: Emissions to air, water and soil connected to the entire lifecycle of new MR equipment can be addressed with the aim to be minimized but not avoided entirely.
Education: MR gaming can be used in education such as teaching languages.
Health: On the positive side, MR gaming likely increases physical activities compared to other computer gaming. Furthermore, MR gaming can be fun and lead to well-being. On the negative side is the risk of addiction to gaming. Specific scenarios, e.g. MR gaming used in healthcare like might also be used for reintegration of difficult cases in society (e.g. post-traumatic stress syndrome).
Inclusion & equality: There is a possibility for improvement of the social / (gender) equality by e.g. helping chronically ill people to live a better life and thereby participate in society. From a inclusion point of view, affordability is important aspect to consider as those without a headset otherwise are excluded.
Trustworthiness: Privacy and integrity issues related to the is risk that sensitive data is collected; that personal data get into the wrong hands; or intrusion of bystander privacy.
9.9.2 Pre-conditions
The following pre-conditions are considered:
Two or more users are co-located and collaborating on a game in a mixed digital and physical environment.
The users are equipped with light-weight AR/MR devices and connected to a 6G network.
Low latency sensor hardware is required on the devices for per-pixel accurate alignment.
9.9.3 Service Flows
The following service flow is considered:
Immersive video (RGB and depth) and audio feed is captured on user device.
The stream is partially compressed on device and offloaded over a 6G network to the cloud. The tradeoff depends on the level of details, latency, and accuracy.
In the cloud, a shared virtual scene is generated and pre-rendered.
The virtual scene potentially including coordinates, audio, video, and depth, is transmitted to user device.
9.9.4 Post-conditions
Users have an enjoyable gaming experience.
9.9.5 Existing features partly or fully covering the use case functionality
Remote or split rendering for gaming applications, such as rendering of 360-degree video tiles in the cloud and transmitting the images to device, is already a well-known concept.
Media Codecs, such as versatile video coding, are commonly available and considered for the compression of uplink and downlink images.
In TS 22.156 [28] Mobile Metaverse services requirement for association and coordination for objects related to a session.
Table 7.1.1 in TS 22.261 [14] has requirements for interactive audio and video service defined for indoor/hotspot scenario in note 3 “For interactive audio and video services, for example, virtual meetings, the required two-way end-to-end latency (UL and DL) is 24 ms while the corresponding experienced data rate needs to be up to 8K 3D video [300 Mbit/s] in uplink and downlink”.
9.9.6 Potential New Requirements needed to support the use case
The following requirements need to be fulfilled for the Mixed Reality gaming scenario:
[PR 9.9.6-1] The 6G system shall support service continuity for mixed reality gaming between indoor and outdoor, and between Dense Urban and Rural deployments.
[PR 9.9.6-2] The KPIs in Table 9.9.6-1 should be supported in Dense Urban, Urban and Rural indoor/outdoor scenario:
Table 9.9.6-1: Performance of Mixed Reality Gaming
Profile
Characteristic parameter
Bit rate down link
Bit rate up link
Application latency
Communication service availability
Overall user density
Activity factor
UE speed
Mixed Reality gaming
[50] Mbit/s (note 1)
[50-100] Mbit/s (note 3)
[20] ms (note 2)
Dense Urban [99]%
Urban [99]%
Rural [98]%
Dense Urban [25,000] /km2
Urban [1000-10,000]/km2
Rural [100]/km2
2% (note 4)
5 km/h
NOTE 1: Remote or split rendering for gaming applications, such as rendering of 360-degree video tiles in the cloud and transmitting the images to device,
NOTE 2: The local encoding and processing on the glasses is assumed
NOTE 3: envisioned 1080P resolution 2 grayscale cameras
1920 x 1080 x 8 bit, a depth sensor 1920 x 1080 x 8 bit with 60 FPS means 1920 * 1080 * 24 * 60
bps = 3Gbps uncompressed. Depending on compression ratios that would lead to 50 Mbps to
100 Mbps
NOTE 4: Activity factor 2% is based on 20% uptake of XR service and 10% activity factor for the XR application.
9.10 Use case on smart life for aging population with immersive real time communication
9.10.1 Description
Currently, many countries are facing the major challenge in providing care support for senior citizens due to their rapidly ageing populations and declining old-age support ratios. As mentioned, in the B5G forum in 3GPP SA1 IMT-2030 Use Case Workshop [151], one of the major issues in Japan is the low birth rate and aging population. The same issue is also faced by the government in China and the EU. All those Social issues ask for the network operators to provide better support.
In recent years, the extended reality has drawn growing interest among several industries. The 5G-Advanced has already explored the potential tools and services that operators can provide to offer a futuristic experience to their users. For instance:
As defined in TS 22.156 [28]: localized mobile metaverse service, avatar-based real time communication, etc.
As defined in TS 22.261 [14]: AR/VR communication, tactile and multi-modal communication services, etc.
While previous use cases focusing on using AR/VR glasses to support an immersive experience, the collaboration between available smart devices, e.g. smart glasses, smart TVs, smart watches etc., could bring new benefits.
In the meantime, new technologies have emerged. Operators are already able to provide different kinds of AI related real time communication services, such as voice and video recognition, real time translation, fun calling, etc. [152] [[[SUGGESTION_START]]83[[SUGGESTION_END]]] There is research on how Generative AI can be combined with immersive communication services to provide an even better user experience [154], [155].
As senior citizens still prefer operator services [156], the network operator can provide immersive RTC services that combine AI technologies and new smart devices to help with practical social issues.
9.10.2 Pre-conditions
To take care of his mother Mei, Hua arranges regular medical consultations for her. Instead of going to her house in person, the medical examiner, Yang, depends on camera, sensors and smart watches to check on Mei on a virtual, immersive environment. Yang uses a smart VR glass to join the session. In order to check on his mom and monitor the session, Hua also prepares a smart AR glass for himself.
To construct a virtual immersive environment, Hua helps Mei set up equipment in her living room, which includes camera, medical sensors, smart watches etc. Not used to VR/AR glasses, Mei chooses to use a smart TV to join the session.
Before the session starts, Yang sits in his office with his VR glasses on. Hua also sits in his office with his AR glasses on. Mei waits in front of the smart TV.
9.10.3 Service Flows
Yang calls Hua and Mei using his VR glasses. Hua and Mei answer the call and join the session.
When the session is answered by Mei, the smart TV and cameras in her living room send the video data uplink to the 6G system. The 6G system performs transcoding from video to immersive media codecs to reconstruct Mei’s living room and then send the rendered immersive media both to Yang and Hua. Yang and Hua now can see Mei and her living room in a virtual setting.
In the meantime, smart watches and medical sensors start to collect and send medical data, e.g. blood pressure, heart rate, etc., uplink to the 6G system. The data is presented in a video stream and can be transformed according to the user's intent, e.g. text-to-video. However, since Yang does not want to present those data, the video stream is not rendered in the final scene.
In order to make meaningful conversation and eye-contact, Mei prefers to see Yang’s face, instead of Yang wearing a VRglasses that covers his face. To solve this, the camera in the Yang’s glasses collect eye tracking data. The network sends collected eye tracking data and Yang’s facial picture to the network. The intelligent immersive calling service uses the collected data to render Yang’s face in real time and sends the rendered video downlink to Mei’s smart TV based on Mei’s intent.
Mei now can see Yang’s face on her smart TV.
Hua’s AR glasses currently show the video stream of the left camera, and he wants to instead see the video stream of the right camera. He uses gesture to convoy his intention. The network interprets his intent and switches the video output accordingly.
Yang needs to check the medical data of Mei. He asks for her blood pressure and heart rate using voice or gesture instruction. The 6G system interprets the intention of Yang and renders the scene to add the medical data sent by the smart watch. Yang can further adjust the user interface with gesture or voice instruction.
Yang, Mei and Hua hang up the session.
9.10.4 Post-conditions
Thanks to the intelligent immersive calling service, Yang can perform remote medical consultation more effectively. Mei can enjoy remote medical consultation in the usual manner using her smart TV, and without performing any complicated operations. Hua can observe the session remotely to check on his mother.
9.10.5 Existing features partly or fully covering the use case functionality
Requirements for the ID of smart devices are defined in TS 22.101 [58]:
The user to be identified could be an individual human user, using a UE with a certain subscription, or an application running on or connecting via a UE, or a device (“thing”) behind a gateway UE.
Requirements for supporting the UE with limited power in metaverse service are defined in TS 22.156 [28]:
[R-5.1.1-001] Subject to operator policy, the 5G system shall support a mechanism that enables flexible adjustment of communication services based on e.g., the type of devices (e.g., wearables), or communication duration (e.g., more than one hour), such that the services can be operated with reduced energy utilization.
NOTE 1: Metaverse service experience over an extended period of time (e.g., 2h) requires significant power consumption by the UE. In some cases, a device with no external power supply cannot sustain downloading and rendering of media over a long interval, e.g., for the duration of an entire feature film or athletic event.
This is more related to communication service adjustment, the capabilities such as rendering/computing capability scheduling are not considered. And AI task arrangement is not considered.
Requirements for 3GPP system to support XR communication are defined in TS 22.156 [28]:
[R-5.2.2-001] The 5G system shall support 5G CN to provide real-time feedback in support of conversational XR communication among multiple users simultaneously.
NOTE 1: The feedback can include information such as network condition and achieved QoS. Such information can be used by the IMS, for example, to trigger the codec negotiation.
Requirements for 3GPP system to support multimedia conversational communication are defined in TS 22.156 [28]:
[R-5.2.2-002] Subject to operator policy and user consent, the 5G system (including IMS) shall support multimedia conversational communications between two or more users including transfer of real time avatar media and audio media.
NOTE 2: Avatar media can be transmitted on both uplink and downlink.
NOTE 3: Confidentiality of the data used to produce the avatar (e.g., from the UE cameras, etc.) is assumed.
[R-5.2.2-003] Subject to operator policy and user consent, the 5G system (including IMS) shall support change of media types between video and avatar media for parties of a multimedia conversational communication.
[R-5.2.2-004] Subject to operator policy, the 5G system (including IMS) shall support transcoding between media such as text, video and avatar media in multimedia conversational communications.
NOTE 4: Text, video or other media could allow a party to control the appearance of its avatar, e.g., to express behaviour, movement, affect, emotions, etc.
NOTE 5: The transcoding of media enables avatar communication, e.g., in scenarios in which UE participating in an IMS call or other service does not support e.g., FACS, encoding avatar media, generating avatar media, etc.
[R-5.2.2-005] Subject to operator policy, regulatory requirements and user consent, the 5G system (including IMS) shall support the capabilities of rendering the avatar based on the body movement information (e.g., body motion or facial expression) of a human user.
9.10.6 Potential New Requirements needed to support the use case
[PR 9.10.6-1] Subject to operator policy and user’s consent, the 6G system (including IMS) shall support intelligent immersive calling service for users via various UEs (e.g. smart wearables, mobile phones, intelligent devices in the home, low-power devices).
NOTE 1: Intelligent immersive calling service: An immersive calling service that is empowered by AI capabilities, e.g. generative AI, multi-modal model etc. The service takes various input from different kinds of smart devices and sensors, e.g. camera, smart watches, AR/VR glasses, to enable users to communicate with each other. It can be provided natively by the operators.
[PR 9.10.6-2] Subject to operator policy and regulatory requirement, the intelligent immersive calling service shall comply to the existing regulatory requirements (e.g. lawful interception).
[PR 9.10.6-3] The 6G system shall support collection of charging information associated with initiating and terminating an intelligent immersive calling service.
[PR 9.10.6-4] Subject to operator policy, the 6G system (including IMS) shall provide mechanisms for the intelligent immersive calling service to render media based on the received intent from a user (e.g. voice, gesture) during the calling.
NOTE 2: Media rendering could include e.g. switching video input, altering facial expression.
9.11 Use case on real time VR live service with deterministic user experience
9.11.1 Description
The immersive service (e.g. VR) is a real time service which includes audio, video and haptic related data. A typical transmission data rate for the immersive service is considerable, which makes the user experience can hardly be guaranteed at the condition when a user is at poor coverage or a bandwidth limited place. With the advent of advanced algorithm (AI or non-AI), it can help to recover the information of the receiving end even when the receiving end lost a part of information incurred by the poor communication link, which is called “Error tolerant communication” in this use case.
By leveraging AI technique, the error-tolerant information even with being discarded via transmission can still be recovered at the receiving end with a deterministic user experience, the Figure 9.11.1-1 typically illustrates the gain by using the AI based encoding-decoding method (GRACE) [162] vs. traditional codec (FEC, Error Concealment), in which by using GRACE [162], the video quality is much less affected by the increasing of packet loss rate of each video frame.
Figure 9.11.1-1: Using AI inference for content recovery with packet loss rate [162]
Furthermore, since the VR/AR content is usually composed of multiple kinds [163], the application can divide it into categories with different importance. An example is shown in the table below.
Table 9.11.1-1: Classification of immersive content
Information
Sub-classification
Error-tolerant/non error-tolerant
Description
Audio (DL)
Text information
Non error-tolerant
A person's voice can be converted into text and pitch.
Pitch information
Error-tolerant
Video (DL)
Eye-tracking content
Non error-tolerant
The content of eye-tracking and fast- moving thing is much more important, while the relatively static information can be recovered by AI
Fast-moving object information
Non error-tolerant
Background information
Error-tolerant
Haptic (UL/DL)
Haptic information
Non error- tolerant
Similar as described in TS 22.261 [14], the data size is small
When a communication link status is bad, the communication can transmit error tolerant data with high error rate or even no transmitting while still guaranteeing the low error loss rate for the non-error tolerant data, It hence greatly reduces the transmission burden while using the a proper algorithm (e.g. using AI model) to guarantee a deterministic user experience such as restore the missed content at the receiving end, which can achieve the same level of user experience (e.g. QoE) compared to a good communication link condition.
NOTE: The Application (e.g. using the Grace algorithm) does not necessarily need to know the changed QoS for error tolerant data traffic before recovering the lossy content.
Noting that the Error tolerant communication is at the cost of consuming much more energy consumption by the sending and receiving end, it is an option for deterministic user experience when the communication link status is bad but may not be always used. In other words, activating the “Error tolerant communication” is decided by application’s decision, taking into account the real time communication link status, energy consumption, and application layer capabilities.
9.11.2 Pre-conditions
1. Computing resources are deployed in the UE and AS (Edge/cloud) for immersive service.
2. The UE and AS support capability for “Error tolerant real-time service mode” where the content missed to be delivered can be recovered by AI based encoding/decoding.
3. The VR headset sensor produces user’s movement information and sensing information to the AS, and the AS responds the video, audio and haptic feedback information in real time. A typical service KPI requirement can be “120 FPS, E2E Round-trip latency: 20ms, Resolution: HDR (10-bit)”.
9.11.3 Service Flows
Figure 9.11.3-1 illustrated a scenario of Error-tolerant communication for real time immersive service, where some of data could be “error-tolerant” during the transmission. The specific service flows are as below.
1. Bob gets on a high-speed train for business travel. Since the journey is long (4 hours), Bob wants to watch football live to pass time using VR headset in the high-speed train. By using VR headset, Bob can enjoy an immersive service that makes the user feel as if on the spot, including 360-degree unobstructed viewing details of the football field and the real time performance of any player on the field.
2. When at the train station (urban area), the communication link is good. The VR application works on “regular mode”, in which all the immersive service data can be transmitted with high reliability (e.g. 99.9%) and within low latency (e.g. 10 ms).
3. When the train’s speed reached high (e.g. 300 KPH) and it moved to a rural area, the communication link becomes worse. The application becomes aware that communication link status is too bad to transmit all data for the immersive service with the current high reliability and expected latency.
3. As soon as communication link becomes bad, the application switched the “regular mode” to the “Error tolerant mode”, the application starts to:
- transmit the error-tolerant data (audio pitch information, video background information) with lower reliability and higher latency (e.g. 99.9% -> 50%, 10 ms -> 100 ms).
NOTE 1: In the bad communication link status mentioned above, instead of degrading the QoS for error-tolerant data, it may just stop transmitting the error-tolerant data.
- transmit the non error-tolerant data (audio text, fast-moving object/player, eye-tracking information, and haptic feedback) still with the high reliability and low latency (e.g. 99.9%, 10 ms). Then the communication link is affordable to the transmission data rate required by error tolerant communication.
4. The headset recovered the details which were missed due to error-tolerant data transmission (such as background information, live sound) and managed to deliver the completed VR content to Bob.
NOTE 2: The recovery can be realized by using AI model such as an AI based encoding/decoding method.
5. Later on, the application gets aware the communication link status becomes good, and the “Error tolerant mode” is changed to “regular mode” which switched to transmitting all error-tolerant and non error-tolerant data with high reliability and low latency (e.g. 99.99% and 10 ms). By doing so, the headset delivers immersive service with the same level of user experience while the energy consumption is less consumed on UE and network side.
Figure 9.11.3-1: Error-tolerant data transmission for real time immersive service
9.11.4 Post-conditions
Thanks to Error tolerant communication, Bob can enjoy immersive service with deterministic user experience (e.g. QoE) either in good or bad coverage,
9.11.5 Existing features partly or fully covering the use case functionality
In TS 22.261 [14] clause 7.6.1, there is requirement for 5G system to support interactive task completion during voice conversation: the 5G system shall support low-delay speech coding for interactive conversational services. It additionally defined the speed for AR/VR service that is up to 500 km/h.
The Edge Enabler Client (EEC) is defined in TS 23.558 [52], which provides supporting functions needed for Application Clients. There is some similarity between EEC and the "rendering task split function" described in this use case. In this use case, instead of communicating with the Edge Enabler Server (EES) in the DN that only manages the edge applications, the rendering task split function coordinates with the core network for splitting/offloading rendering tasks.
The reliability as below which requires all contents/bits are successfully transmitted, however it cannot be fulfilled via error tolerant communication. It needs to loosen the criterion for a “successfully delivered” packet. For example, assuming BER=0.1, BLER=0.8, one Transport Block may contain several IP packets or parts of the several packets, thus the high BLER very likely leads to a high Packet Error Rate (PER) e.g. 20, 30% or higher.
reliability: in the context of network layer packet transmissions, percentage value of the packets successfully delivered to a given system entity within the time constraint required by the targeted service out of all the packets transmitted.
9.11.6 Potential New Requirements needed to support the use case
[PR 9.11.6-1] Subject to operator policy and user consent, the 6G system shall provide means to allow a trusted third-party to provide assistance information in order to identify and deliver the error tolerant data traffic.
NOTE 1: The assistance information can be IP-tuple or something in existing header field that is used to mark the data for error tolerant data traffic
[PR 9.11.6-2] Subject to operator policy, the 6G system shall be able to transmit the error tolerant data traffic with adaptive QoS which is used for efficient delivery of the error tolerant data traffic, i.e. the reliability and latency can be adjusted automatically without influence from 3rd party application
NOTE 2: As an example, the range of reliability can be [99.99 to 50%], and the range of latency can be [10-100 ms] for error tolerant data transmission.
[PR 9.11.6-3] Subject to operator policy and user consent, the 6G system shall provide a means to expose to a 3rd party application the status of the communication link specific to a user upon request from 3rd party.
NOTE 3: The communication link status can be used by application to better recover the lossy content.
[PR 9.11.6-4] Subject to operator policy and user consent, the 6G system shall provide a suitable means for communication with error tolerance to evaluate the high data transmission error rate and accept the data packet that fulfil[[SUGGESTION_START]]s[[SUGGESTION_END]] the condition of high data transmission error rate (e.g. allow a percentage of error bits for a successfully delivered packet).
9.12 Use case on personalized interactive immersive guided tour
9.12.1 Description
Localized (i.e. location-aware) mobile Mixed Reality (MR) services provide users with information and services that are of local relevance. This use case focuses on a personalized interactive immersive guided tour service of a city (Figure 9.12.11) including outdoor/indoor configurations. The following technical aspects are supported:
Personalization:
Each visitor of the group can select one or multiple 6G connected device(s) he’s willing to wear during the guided tour based on his desired degree of immersion. He may also add, remove or change a 6G device during the guided tour. Therefore, heterogeneous 6G devices such as lightweight AR glasses, tablets/phones with potential tethered devices, XR video or optical see-through headsets, haptic gloves and/or wristbands, wearables, and immersive audio headsets are connected for a group of visitors during the same period.
Each visitor of the group can select a different personalized setting such as avatar or language (and/or personalize a virtual representation) of the remote touristic guide application, possibly with AI techniques for an intuitive and fast avatar and translation personalization.
The touristic information content is adapted simultaneously to the preferences of each visitor of the group (e.g. adapted overlaid text, picture, 3D virtual asset, video, audio).
Interactive immersion:
Each visitor of the group can choose a personalized degree of immersion based on the 6G device(s) he’s wearing. The rendering of different media (e.g. haptics, video, audio) needs to be spatially and timely consistent along the guided tour to maintain a good level of immersion.
An AR Spatial Computing Service is required to ensure a good level of immersion by integrating virtual content into the visitor’s real environment in a seamless manner (e.g. by managing occlusion, maintaining consistent lighting). AI techniques for real time environment analysis are necessary, especially in fast-evolving environments (e.g. crowding). Such techniques may be used for the proper placement of virtual content by ensuring that each group member has a good point of view of the virtual content.
One major requirement for the virtual content placement is the safety of the group of visitors.
The group of visitors can interact in real time with the remote touristic guide represented by a personalized avatar for each visitor. Interactions may be conversational (i.e. asking/answering a question), gesture-based (e.g. hand shaking with a haptic glove).
AI-based services may be used to analyze the facial expression/gaze of the visitors to trigger content adaptations (e.g. when some visitors are looking to particular objects, additional touristic information related to this object is displayed).
Configurated and maintained Quality-of-Experience (QoE):
Data flow metrics supporting the QoE for each visitor of the group need to be configured and maintained along the guided tour which may be composed of outdoor and indoor configurations, sparse and dense, crowded areas potentially leading to various fluctuations of 6G access type connectivity and connectivity conditions (e.g. capacity/congestion).
XR data flow metrics supporting QoE (e.g. XR pose-to-render-to-photon, roundtrip interaction delay) need to be measured and monitored periodically and the XR content and/or the network parameters need to adapt accordingly to changes in 6G connectivity conditions.
Figure 9.12.11: Interactive immersive guided tour
9.12.2 Pre-conditions
A family including adults, a teen and young child are visiting Paris. Hank is the remote activity guide and teacher for the group. Each member of the family has configurated a dedicated avatar of Hank for the guide tour.
They all wear different 6G connected devices. The mother wears a 6G smartphone and sometimes lightweight tethered AR glasses, the father wears an immersive audio headset, the children wear XR headsets and sometimes haptic gloves.
The family decides to participate in a day-long special art immersion event. The event includes participation in immersive performances (e.g. opera, ballet, theatre), immersive visual art creation (e.g. sculpture, crafts), visits to indoor landmarks (e.g. museum), etc.
9.12.3 Service Flows
The service flows are described for an interactive immersive experience in dedicated locations of the overall guided tour of Paris.
The family is provided with instructions on activities and services. Hank is configured to interact with all family members as a group virtual teacher and guide with personalization (e.g. avatar, language, age related content, etc.) for each person. The behavior of the participants (e.g. haptic feedback, captured facial expressions, gestures are analyzed by AI) triggers a personalized immersive experience.
The avatar positioning is using AR Spatial Computing Service to identify the best position within the real environment for visualization, participation and safety. Changes in the scene (such as another visitor walking through the scene) may result in changing the positioning of the avatar(s).
The AR Spatial Computing Service has identified the Palais Garnier, and the guided tour AS provides XR content composed of a dance performance with volumetric video, immersive audio, haptics and virtual objects. The XR content is placed and rendered at an optimal position relative to the participants and the sensed environment.
The application provides each family member immersive experiences, including 6 Degrees-of-Freedom (6DoF) experiences (i.e. each participant is free to change position as forward/backward, up/down, left/right translation, combined with changes in orientation).
The father asks a question to the remote guide through his avatar. The XR content animation is paused for all the family members and the answer to the question is provided to all the family members.
Once the dance performance is done, the family and the guide avatar enter the Palais Garnier. Each family member receives personalized touristic information (e.g. overlaid text, picture, 3D virtual asset, video, audio, haptics) linked to the real surrounding objects they discover during the walk.
At another location, the young kid is guided by Hank to participate in an activity creating a virtual clay and pebbles sculpture. The XR content animation is synchronized for all the family members to watch the activity and interactions.
Suddenly the 6G connection conditions fluctuate (e.g. changes in capacity and/or congestion) during a significant period of the presentation. The 6G network may adapt and/or trigger adaptations of the XR content to preserve an acceptable QoE for each family member during that period.
When exiting the Palais Garnier, the teen replies to a question and shakes hands with the guide, and everyone sees Hank’s avatar doing it with the teenager. The teen kid wears a haptic glove to sense the handshake.
9.12.4 Post Conditions
With the advancements in interactive immersive XR technologies and 6G capabilities, visitors can acquire knowledge, touristic, and historical information of a city in a comprehensive and personalized way.
9.12.5 Existing features partly or fully covering the use case functionality
The following existing features can partly cover some functionalities of this use case.
Features on localized metaverse service for receiving locally relevant XR content are provided in the TR 22.856 [49] and TS 22.156 [28]. This includes exposure and coordination requirements in TS 22.156 [28] clause 5.1.1.2 and “spatial anchors” requirements n TS 22.156 [28] clause 5.2.1. The placement of the XR content in the real environment, to ensure an optimal viewing, interaction and safety for each group member, may need additional requirements.
Features on avatar-based real time (including IMS) communication are provided in the TR 22.856 [49] and TS 22.156 [28]). The time and spatial synchronizations (e.g. coherent gestures, facial expressions, displacements) of the personalized avatars for the group of participants may need additional requirements.
Features on QoE/QoS management of multi-modal flows considering low round-trip latency, and the synchronisation of different media (e.g. haptic, video, avatar, audio) for each group member, is provided in TS 22.156 [28] (e.g. [R-5.1.1-002]). QoS management features using the NEF API are provided in TS 23.501 [140], TS 23.502 [30] and TS 23.503 [141]. A fast dynamic QoE/QoS adaptation to the fluctuations of the 6G connection conditions during a real time communication may need additional requirements.
Features on Tactile and multi-modal communication service are provided in TR 22.847 [164], including use cases related to closed loop haptics (robotic, surgery etc.), with corresponding requirements in TS 22.261 [14] clause 6.43. Other haptics-based use-cases, such as those including open loop haptics (haptics enhanced media streaming, distribution and communication, avatar etc.) were studied in TR 26.854 [165].
To support the heterogeneous 6G devices of this use case, tethered AR glasses (TR 26.998 [166], TR 26.806 [167]) and split rendering architecture (TR 26.928 [50], TS 26.565 [51]) have been studied with QoE/QoS support. The synchronization of heterogeneous data flows from/to various UEs within a group of participants may need additional requirements.
Performance KPIs and parameters partially covering the functionality include:
Synchronization threshold between audio and video for VR (TS 22.261 [14] clause 7.6.1), i.e. in the range of [125 ms to 5 ms] for audio delayed and in the range of [45 ms to 5 ms] for audio advanced.
Synchronization threshold to ensure acceptable QoE for support of haptic media in addition to other audio and video media (TR 26.854 [165] clause 10) as follows:
in the range of [25 ms to 50 ms] for audio delayed and in the range of [25 ms to 12 ms] for audio advanced.
in the range of [15 ms to 20 ms] for video delayed and in the range of [50 ms to 30 ms] for video advanced
Additional performance specifications necessary to address this use case include:
New synchronization threshold KPI ranges, dependent on scenario (e.g. max allowed end-to-end latency, user experienced data rate) are to be specified.
NOTE: The synchronisation threshold definition TS 22.261 [14] is assumed. Synchronisation threshold can be calculated based on (but not limited to) one or more of the following: network delays, transmission & synchronization delays between data flows for a user, rendering delays, sampling frequencies of the media, buffer and jitter pacer, processing delays, mismatches between estimated and real positioning; variabilities between UEs for any given user, etc. Higher synchronization thresholds values correspond to less stringent user experiences.
9.12.6 Potential New Requirements needed to support the use case
[PR 9.12.6-1] Subject to operator policy and privacy considerations, the 6G system shall provide means (e.g. association of XR content with location and relevant positioning information) to ensure an optimal placement in viewer relative to the real environment (e.g. to ensure safety or optimal interactions between participants, etc.).
[PR 9.12.6-2] Subject to operator policy and privacy considerations, the 6G system shall provide a means to synchronize media/flow and data for rendering in time and in XR space (e.g. coherent gestures, facial expressions the personalized avatars of the group of participants.
NOTE: For “XR space” clarifications, see TS 26.119 [304].
[PR 9.12.6-3] Subject to operator policy and privacy considerations, the 6G system shall support a mechanism, including enabling one or more authorized third party(ies) to adapt XR session QoS dynamically at different levels of granularity (e.g. per type of media stream, per data flow, per burst) based on the fluctuations of the 6G connection conditions and QoE policies.
[PR 9.12.6-4] Subject to operator policy and privacy considerations, the 6G system shall provide a means to synchronize heterogeneous data flows from/to a set of UEs (e.g. phone, glasses, tethered ring) associated with a single user and between UEs associated with multiple users in a multimodal communication (e.g. each user having glasses and/or phones).
[PR 9.12.6-5] Subject to operator policy and privacy considerations, the 6G system shall provide a means to keep heterogeneous media flows synchronized within an acceptable threshold whether rendered on a single or multiple UEs, for each user in a multimodal communication.
The potential new KPIs detailed in Table 9.12.6-1 are needed to support the use case:
Table 9.12.6-1: KPIs for personalized interactive immersive use-cases
Characteristic parameter (KPI)
Influence quantity
Scenario: Max allowed end-to-end latency (note 1)
Service bit rate: user-experienced data rate
Synchronization threshold
(note 2)
Audio (UL/DL): [10] ms
Audio (UL/DL) [5-512] kbit/s
Audio-to-haptics lag: [25] ms;
Haptics-to-audio lag: [12] ms
Delay (ms), Packet loss (%), Update rate (Hz), Packet size (bytes), Throughput (kbit/s) for each media type based on TR 26.854 [165] Table 10.3-1.
Immersive video (DL): [200-300] ms
Immersive video (DL) [10-20] Mbit/s
Visual-to-haptics lag: [20] ms;
Haptics-to-visual lag: [30] ms
Avatar between remote guide and UEs: [20] ms
Avatar: [0.1-30] Mbit/s (depending on the format)
Avatar animation: 2 Mbit/s uncompressed.
1 Mbit/s compressed
Audio-to-avatar lag: [25] ms;
Avatar-to-audio lag: [12] ms
Pose & action data (UL): [5] ms
Pose & action data (UL) [100 – 400] kbit/s
Avatar-to-haptics lag: [20] ms;
Haptics-to-avatar lag: [30] ms
Environment sensing data (UL): [5] ms
Environment sensing data (UL) [10 – 50] Mbit/s
Pose-to-visual lag: [50] ms (pose UL, visual DL)
Visual-to-pose lag: [20] ms (visual DL, pose UL)
Haptic (DL): [5] ms
Haptic (DL):
[0.25 – 160] kbit/s for parametric compressed format
[up to 6400] kbit/s for time sample format. See. TR 26.854 165] Table 5.4-1.
Audio-to-pose lag: [50] ms
Pose-to-audio lag: [20] ms
NOTE 1: Synchronization threshold values vary for active versus passive engagement scenarios. Scenarios other than the ones listed may require other synchronization thresholds for the same media combinations.
NOTE 2: “media X to media Y lag” refers to the positive time difference between the reference media X component and the specified media Y component. For example, an “audio-to-haptics lag” of 25ms means that haptics media arriving within 25 ms after the audio is acceptable.
9.13 Use case on intelligent transmission service for user experience improvement
9.13.1 Description
With the rise of emerging applications such as multimedia communication, augmented reality, and immersive communication, the demand for wireless spectrum resources to support massive data transmission has sharply increased, further exacerbating the tension of network resources. Moreover, the current communication system is sensitive to degrading network quality in challenging environments (e.g. signal obstruction, interference, or mobility), leading to significant deterioration in service continuity and data reliability. Therefore, existing communication system is challenged by these complex and demanding scenarios.
With AI and communication, related technologies, such as semantic communication, have shown good performance in complex environments as mentioned above. For example, semantic communication can extract key feature information from massive multimodal data, which can significantly reduce transmission bits. While current communication system experiences a sharp decline in data transmission performance under adverse network conditions, by prioritizing the transmission of key features, the reconstruction of information with high semantic integrity can be enabled even in the presence of data loss, especially when supported by prior knowledge or contextual information. For instance, in image transmission, even if some detailed information is lost, the system can still accurately recognize and reconstruct the semantic content of key targets (such as pedestrians, vehicles, etc.). Studies on image semantic transmission have demonstrated the good performance under constrained and dynamic network conditions [168].
9.13.2 Pre-conditions
Bob is traveling on a high-speed train and preparing to watch a live broadcast of a football final through AR glasses. He will face degraded network quality particularly during tunnel transits. He hopes to enjoy a smooth viewing experience even under conditions of high packet loss or low bandwidth.
He has subscribed to the intelligent transmission service from the operator and decides to use it to improve the viewing experience.
9.13.3 Service Flows
Bob puts on lightweight AR glasses and starts enjoying the live broadcast of the football final.
The 6G system acquires the real time position of the train, compares it with its wireless environment knowledge, and predicts through the AI model that Bob is about to enter the dense tunnel area.
The 6G system determines to enable the intelligent transmission service. It dynamically adjusts the network policy with end-to-end network resource planning, and coordinates UE and Application for intelligent transmission (e.g. priority transmission of key feature information).
By applying the corresponding AI model, context features of the live broadcast video are extracted, forming high-priority key feature information and low-priority redundant information (e.g. taking the athlete's movement trajectory and score changes as key feature information and the background as redundant information based on semantic importance).
By applying the corresponding AI model, the image is restored on UE according to the received information (e.g. key feature information). Meanwhile, UE can use the knowledge information (e.g. the background of the stadium) pre-stored in edge nodes (MEC) to complete the background information lost during transmission.
When the train entered a 20-kilometer-long group of mountain tunnels, the signal strength of the channel dropped sharply. In Bob’s AR glasses, the players' movements remain smooth, only the details of the stands are temporarily blurred.
9.13.4 Post-conditions
By providing the intelligent transmission service, the 6G system adapts to rapid changes in channel conditions and allows Bob to enjoy a smooth viewing experience during his high-speed train ride.
9.13.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] clause 6.40 has defined requirements for AI/ML model transfer in 5GS, which can partly support this use case.
9.13.6 Potential New Requirements needed to support the use case
[PR 9.13.6-1] Subject to operator’s policy, the 6G network shall support QoS adjustment based on real time network status to ensure user experience.
NOTE: The QoS is adjusted to realize high-priority transmission for key feature information and low-priority transmission for redundant information.
[PR 9.13.6-2] Subject to operator’s policy and user consent, the 6G network shall support prediction of network status based on context information (e.g. user location, mobility path, and environmental knowledge), and may expose such network status to authorized applications.
9.14 Use case on improved user experience
9.14.1 Description
User eXperience (UX) refers to the overall satisfaction of a user with the service provided by the network, and involves multiple aspects, such as multimedia, power, and network coverage (the rest of the proposal focuses on the multimedia aspect of UX). UX can be measured with different metrics depending on the application. For instance, in multimedia-based applications (e.g. live streaming, cloud gaming, video call and XR Split rendering), the UX may be quantified with frame/image quality and smoothness of video playback.
Typically, in 3GPP, the application presents to the network QoS requirements correlated to expected UX in terms of metrics such as bit rate and latency. However, studies have shown that higher bit rate typically results in a better UX, albeit with a diminishing return [174].
Another factor that impacts the UX is the content complexity; simpler content requires less bitrate to achieve the same UX level as a more complex content. In the context of video transmission, simple content may refer to video frames that have low temporal variation (i.e. mostly static) or spatial variation (i.e. mostly plain). Complex content, on the other hand, refers to videos that are highly dynamic and rich in spatial information. For a given user, the content complexity is dynamic (varies with time), and for the network serving multiple users, the complexity could vary across users in the network also.
In 5G and 5GA, certain AA features have been introduced in applications such as XR [175], however, the awareness is quite limited in scope. Hence, most networks still rely on simplified assumptions that higher bitrate indicates higher UX. For a user with highly dynamic content, when its application transitions from simpler content to more complex one and the requested bit rate has become obsolete, this could result in degraded user service experience. On the other hand, when a user transitions from a complex content to a simpler one and the bit rate is not appropriately reduced to reflect this, the user may consume unnecessary resources (e.g. energy consumption), this could significantly impact power sensitive application such VR/AR. A figure illustrating these transitions is shown in Figure 9.14.1-1 below.
Figure 9.14.1-1: Example of 2 UEs. UE1 switches from a complex scene to a simple one, while UE2 switches from a simple scene to a more complex one.
From a network’s perspective, inability to set the bit rate appropriately may lead to suboptimal network resource allocation. For instance, consider the scenario depicted in Figure 9.14.1-2 (left), where 2 devices are connected to the network and have similar channel conditions. UE1 is streaming complex content while UE 2 is streaming very simple content. A UX-unaware network may allocate similar bitrates to the two users, giving unnecessarily high UX to UE2 (i.e. that may be achievable with a much lower bitrate), while greatly impacting that of UE1, hence, limiting the number of UEs that can be serviced with their target user service experiences.
Figure 9.14.1-2: UX-unaware resource allocation results in reduced UX capacity and/or application coverage.
A UX aware network receives the UX information from the AS (or application client). With the awareness of such information, the network can take smarter and more optimized resource allocation decisions. For instance, in the scenario of Figure 9.14.1-2 (a), the network may decide to lower the bitrate of UE2 (with little to no impact to its UX, since it has a simple scene) and allocate the freed-up resources to UE1 with the complex content to meet its own UX target. Additional freed-up resources may even be allocated to a third UE while meeting its UX target as well (as depicted in Figure 9.14.1-2 (b)). The UX awareness increases the number of UEs that can be serviced with their desired user service experiences.
In addition, it is worth noting that an example of the UX information from the application to the network is a predicted/estimated relationship between user service experience and performance metrics such as bitrate for the application content that is sent to the network. Other examples may include a predicted/estimated relationship between user service experience and tolerable packet loss rate, where the tolerable packet loss rate is the maximum packet loss rate that the application may experience while achieving the desirable user service experience. In terms of the frequency of sending the UX inf information, the application may send it periodically or based on events (e.g. significant content complexity change).
9.14.2 Pre-conditions
John and Samantha live in the same apartment complex and use wireless communication services provided by MNO XY. John is a gamer and enjoys playing multiplayer first-person-shooter cloud games on his tablet. These games are typically very dynamic, with intense battle scenes and fast-paced actions, vibrant graphics, and multi-player interaction.
Samantha has a VR headset which she typically uses in virtual touring of museums using the wireless communication service provided by MNO XY. During the tour, she is typically engaged in a complex environment with many moving people and exhibits, enticing her to be continuously moving her head around. She follows a virtual guide, interacts with exhibits or interactive displays, etc. Sometimes, she stops to look at a specific painting or read an informational plaque.
MNO XY just advertised a new service with improved user service experience for multimedia applications such as cloud gaming and VR, so John and Samantha signed up for the service. After service registration, MNO XY configures John’s tablet and Samantha’s VR headset to send application related information to the network. Each application (either AS or application client) sends periodic UX information with an indication of the current content complexity. An example for the indication of content complexity could be a function mapping the bitrates to video quality, where more complex content requires higher bitrates to achieve a certain target quality.
9.14.3 Service Flows
On Saturday morning, John decides to relax in this apartment and connect with his friends over a multiplayer first-person-shooter game. John’s tablet may send an indication of complex content during intense battle scenes and fast-paced actions, and indication of simple content during loading screens and menu interfaces.
Around the same time, Samantha decides to take a virtual tour of a museum using her VR headset. Samantha’s VR headset may send an indication of simple content during periods of Samantha’s slow motion or focus on paintings and plaques, and an indication of complex content during periods of Samantha’s fast motion and high interactions.
The devices or the application servers may send UX information periodically or when substantial changes happen within the content (e.g. scenes change).
With the knowledge of content complexity, the network re-distributes its resources in a way that gives fewer resources to users with simpler content (thereby not impacting their UX), and more resources to users experiencing more complex content (thereby greatly improving their UX). The application (server or client) may then be notified of the network’s allocation decisions.
In a time when Samantha is reading an informational plaque in her virtual museum tour, John’s overall gaming experience in a battlefield is improved (low lag and sharper images) due to the excess network resources he can get after Samantha’s network share is reduced, without affecting Samantha’s experience. Conversely, Samantha may make use of extra network resources that are freed-up from John’s device when John pauses his game (goes to menu screen) or not moving in a safe hide.
9.14.4 Post-conditions
With this new service, John notices his cloud gaming application runs more smoothly and hence the delay from when he hits the “shoot” button to when it is displayed on the screen is shorter, improving his scores in the game. Similarly, Samantha notices a smoother viewing on her device and the battery of her VR headset has lasted longer than it typically would.
In addition, the network’s UX-aware resource allocation decisions improve the overall UX capacity (user satisfaction) in the network.
9.14.5 Existing features partly or fully covering the use case functionality
QoE: 3GPP R17/18 has defined QoE measurements configured at a specific UE by the RAN which can be used by the RAN to optimize network and improve user service experience. QoE information defined includes RTT, bitrate, frame rate, packet losses for IMS: and Throughput, buffer level, playout delay for DASH [252] [253] [254]]. This existing framework does not provide information for trading off the user service experience vs rate due to lack of information on content complexity from the application.
L4S: The L4S (Low Latency, Low Loss and Scalable Throughput) feature in 3GPP R18/19 introduced mechanisms for the network (i.e. RAN and/or core network) to notify the application of network congestion and request that the application change its bit rate accordingly [140]. Unlike this UX awareness proposal, L4S does not provide user service experience information to the network.
NWDAF: 3GPP has defined observed service experience analytics in the NWDAF in TS 23.288 [114]. In this use case, the application provides information about the target user service experience to be used by the network for efficient service delivery.
9.14.6 Potential New Requirements needed to support the use case
[PR 9.14.6-1] Based on operator policy and user consent, the 6G system shall provide means to receive information about the target user service experience from authorized 3rd party applications (such as real time multimedia services.)
[PR 9.14.6-2] Based on operator policy and/or user consent, the 6G network shall support mechanisms to efficiently meet target user service experience.
9.15 Use case on coordinating computing and communication for XR rendering
9.15.1 Description
The exploration of ultimate user experience in consumer products, and the digital and intelligent transformation in the industrial fields have promoted the wide use of computing-intensive applications such as AR/XR, cyber-physical systems, industry robots[[SUGGESTION_START]],[[SUGGESTION_END]] etc. However, the balance between computing capabilities and the size and cost of the devices raises a big challenge for the deployment of entire applications. The ITU-R report [39] points out that several emerging technologies are being envisioned to address the challenges. One trend is to process data at the network edge close to the data source for real time response, low data transport costs and energy efficiency by edge computing technologies, while another trend is to scale out device computing capability beyond its physical limitations by splitting computing workload over reachable computing resources. With the development of each technology trend, the independent control, management and orchestration of communication and the computing resources may cause significant negative impact on the performance of the end-to-end solution.
The 6G system is expected to change the situation by coordinating the communication services and computing resource utilization in the stage of system architecture design. In this way, the 6G network can make the ubiquitous single-point computing resource node connected and participate in the scheduling of various types of computing resources such as UE, MEC server, cloud server. For example, the 6G network can select appropriate computing resources based on service characteristics to achieve optimal resource usage. On the other hand, the data transmission for the computation can be more flexible and efficient with additional information (e.g. workload, available capability) about the selected computing resources.
The real time rendering of 3D scenes in a large-scale XR application is a typical case of a computing-intensive application, which requires large amounts of calculations to handle the complicated model and texture mapping. Sometimes limited to the capability of hardware, it is impossible for a single device to render the scene individually, and the distribution of the computing workload to other computing resources can solve the issue. Sometimes the duplicated rendering of the same XR scene which is being requested by multiple users can be avoided if choose to execute the rendering in the cloud server. However, the XR application can have little information on the status of computing resources so that it is difficult for the planned data transmission and computation to work together efficiently.
This use case illustrates how the 6G system enables better user experience of split XR rendering via the coordination of computing resources and communication resources as Figure 9.15.1-1 depicts.
Figure 9.15.1-1: Coordinating Computing and Communication for XR rendering
9.15.2 Pre-conditions
NOTE: The term “6G core network” used in this use case does not imply any architectural assumption, e.g. whether 6G core network is a new or evolved core network (compared to 5G).
Several computing resources (e.g. MEC servers, cloud computing centre) for XR services are deployed distributed in different locations and have been enrolled in 6G core network. MEC Servers A, B and C are provisioned as a part of rendering pipelines to execute render engine, engine adaption, rendering acceleration for the rendering task. The cloud computing centre is capable of all the functions for XR rendering. XR Application Platform provides the render services to various XR applications. MEC Server A and B are deployed in Service Hosting Environment, while MEC Server C and cloud computing centre are in 3rd party’s computing environment.
Task Management Server is responsible for task analysis, sub-task planning, graphic composition[[SUGGESTION_START]],[[SUGGESTION_END]] etc.
The UE supporting the XR application is the subscriber of the 6G network. It has registered to the 6G network and was authorized for the computing coordination service.
9.15.3 Service Flows
1. Regarding the XR application's need, the UE sends a request of XR rendering to the XR Application Platform.
2. The XR application platform decomposes the UE's request into different tasks and dispatches the render task to the Task Management Server through the IP network.
3. The Task Management Server analyses the render task and plans the computing task based on the capability of the computing resources. Then it sends the requests of computing coordination service and XR communication service to the 6G core network, including the information of computing coordination service QoS (e.g. requested computing capabilities, response time) and communication service QoS (e.g. latency).
4. The 6G core network selects MEC Server B and cloud computing centre based on the requested QoS of the computing coordination service, and the status information of all enrolled computing resources, and then sends feedback to the Task Management Server about the coordination result (e.g. selected computing resources). Meanwhile, the 6G core network establishes the communication paths between the UE and MEC Server B and the cloud computing centre to transmit data for the rendering based on the requested QoS of communication service.
5. The Task Management Server distributes the necessary data and associated information to all the selected computing resources via the IP network.
6. MEC Server B and the cloud computing centre execute individual rendering sub-tasks, sync-up with the Task Management Server about the progress of its sub-tasks and sync-up the status information (e.g. computing workload, congestion status) with the 6G core network.
7. The 6G core network detects the overload of MEC Server B, and informs the Task Management Server to replace MEC Server B with MEC Server C.
8. 6G network establishes the communication path between the UE and MEC Server C to transmit data for replacing MEC Server B to execute new rendering sub-tasks from the Task Management Server.
9. Task Management Server coordinates with the 6G network to make decision to migrate part of uncompleted sub-tasks from MEC Server B to MEC Server C in order to alleviate the workload on MEC Server B and ensure that all sub-tasks can be successfully completed.
10. 6G network establishes path between MEC Server B and MEC Server C, which is for transferring all information of uncompleted sub-tasks (e.g. sub-task context, received data), to facilitate the migration of uncompleted sub-tasks from the overloaded MEC Server B to MEC Server C, allowing the sub-tasks to be seamlessly continued on the latter.
11. The Task Management Server collects the rendering graphics from MEC Server B, MEC Server C and the cloud centre after the sub-tasks are finished and composes the XR image for the XR Application Platform. Then the XR Application Platform returns the rendered XR image to the UE.
9.15.4 Post-conditions
The render task is efficiently completed with the collaboration of several edge computing servers under the coordination of the 6G core network.
The rendered XR image is successfully provided to the UE as expected.
9.15.5 Existing features partly or fully covering the use case functionality
None.
9.15.6 Potential New Requirements needed to support the use case
[PR 9.15.6-1] The 6G network shall support mechanisms to acquire and maintain the information of trusted computing resources (i.e. edge server(s)) for coordinating the usage of computing resource on demand
NOTE: The information can be related to computing capabilities (e.g. xPUs, storage), service capabilities (e.g. supported applications, status), and network capabilities (e.g. allowed bandwidth).
[PR 9.15.6-2] The 6G network shall support mechanisms to collect status information (e.g. computing workload, congestion information, available capability, power consumption) of trusted computing resources (i.e. edge server(s) or cloud server(s)) on-demand or periodically.
[PR 9.15.6-3] The 6G network shall provide mechanisms to expose to an authorised 3rd party the information (e.g. computing capability, the location, allowed service types, status, power consumption, utilization) of computing resources (i.e. edge server(s) or cloud server(s)).
[PR 9.15.6-4] Subject to operator's policy, application needs or both, the 6G network shall support the selection of computing resource(s) (i.e. edge server(s) or cloud server(s)) based on e.g. requested computing capabilities, and support routing of data traffic between a UE and the selected computing resource(s) based on required communication service QoS.
[PR 9.15.6-5] Subject to operator’s policy, the 6G network shall be able to maintain user experience (e.g. for immersive communication) with minimum interruption when the selected computing resources switch between those within the Service Hosting Environment and those in 3rd party computing environment.
[PR 9.15.6-6] Subject to operator's policy, the 6G network shall be able to support migration of ongoing computing task from one selected computing resource (e.g. overloaded) to other computing resource(s) in Service Hosting Environments in order to maintain user experience.
9.16 Use case on communication between heterogeneous immersive terminals
9.16.1 Description
Immersive terminals include wearable and non-wearable devices, such as XR devices like VR and AR, as well as terminals in different forms such as glasses-free 3D and holography. Different types of terminals may have different capabilities and be used by users in different scenarios. It’s necessary requirement for users to connect between different types of immersive terminals. Heterogeneous terminals vary in the multi-view information (multi-view content, depth content, camera parameters etc.) required for display and collected. For example, terminals A and B are AR and light field holographic terminals respectively (Figure 9.16.1-1). Terminal A requires left and right eye view contents for display and is equipped with binocular cameras to collect 2-view information; Terminal B requires 4-view contents for display and has 4 cameras to collect 4-view information. When A and B communicate, the 2-view information collected by A's acquisition device cannot be directly applied to device B for display, and the 4-view information obtained by B cannot be directly applied to device A for display. It is impossible to obtain the form of the communication counterpart solely relying on devices A and B, nor can communication between immersive terminals of different types be completed.
This use case will illustrate how the 6G system supports users in communicating with multi-form immersive terminals by coordinating computing resources and communication resources.
Figure 9.16.1-1: Heterogeneous Immersive Terminal Communication
9.16.2 Pre-conditions
UE-A is an AR device with 2 viewpoints display. UE-B is holographic device with 4 viewpoints display.
UE-A and UE-B are connected to 6G network.
9.16.3 Service Flows
1. UE-A initiates a call to UE-B.
2. UE-B accepts the call from UE-A.
3. The 6G network /IMS set up the call between UE-A and UE-B.
4. UE-A sends 2 viewpoints video steams to the network; and UE-B sends 4 viewpoints video streams to the network.
5. The 6G network / IMS support the communication between UE-A and UE-B.
6. UE-A receives 2 viewpoints video streams; UE-B receives 4 viewpoint video streams.
9.16.4 Post-conditions
The user experience of both UEs can be satisfied.
9.16.5 Existing features partly or fully covering the use case functionality
None.
9.16.6 Potential New Requirements needed to support the use case
[PR 9.16.6-1] The 6G network and IMS shall support communication between UEs with different capabilities, e.g. that requires multi-channels video and/or multi-channels voice.
9.17 Use case on Application Context Enhanced Communication Service
9.17.1 Description
The increasing availability of contextual information on mobile devices holds tremendous promise for enhancing communication services in the era of 6G. Advancements in mobile technology have led to a surge in contextual awareness, powered by a rich array of sensors—such as GPS, accelerometers, gyroscopes, cameras, and microphones—that continuously gather data about users’ environments, behaviours, and preferences. At the same time, AI plays a crucial role in interpreting this data, transforming contextual data into meaningful insights. Using techniques like machine learning, natural language processing, and computer vision, AI can analyse application and location patterns, recognise user activities, predict intent, and customise experiences in real time.
By leveraging contextual awareness and AI, 6G systems can deliver an enriched user experience and improve communication efficiency. To harness these benefits, AI-enabled mobile devices may share some elements of user context with the network—especially those related to application characteristics—provided the appropriate user consent and permissions are obtained. It is important to note that the information related to the application is expected to give the network deeper and more comprehensive insights into applications, going beyond the traditional KPIs currently available in 5G networks. Examples of applications, KPIs, and information related to the application are presented in Table 9.17.1-1 below:
Table 9.17.1-1: Comparison between 5G KPIs and Application Context
5G KPIs
Application Context (Information related to the application)
Cloud gaming: 30Mbps GBR, 50ms PDB
Cloud XR gaming with a consistent 60 fps frame rate with frame duration of 5ms at offset of 15ms from system time. I-frame size of 1 Mbits, P-frame size of 500 kbits. Group of pictures (GOP) length (i.e. I-frame interval) = 1 second.
Live streaming: 6Mbps GBR, 500ms PDB
Video streaming at 30fps with I-frame size of 400 kbits, P-frame size of 200 kbits and std deviation of 15 kbits. GOP length = 2 seconds.
File download: Best-effort speed and latency
800MB movie file or 600 KB email downloading.
With the information related to the application, 6G systems (possessing AI capabilities) can dynamically adjust communication resources and parameter settings in the UE and the network. Especially when there is only one bearer, it’s advantageous for 6G system to know the information related to the application and set the 6G system parameters to meet the KPI of that application. By understanding the specific use case and information related to the application, the UE and network can make informed decisions to optimize user experience.
For example, when a user is doing a high-intensity cloud-based car racing game, the 6G system prioritizes low latency and high bandwidth resources to ensure a seamless experience. The resource allocation and parameter settings are dynamically adjusted to meet the high bitrates, and low latency demands of the cloud gaming application. In scenarios involving challenging wireless conditions, such as interference or obstructions, the systems may switch the user to another frequency band or adjust parameters to maintain reliability.
When the user takes a break and switches to downloading a file and update software, the 6G system transitions to a best-effort service model. It reallocates the resources to provide sufficient bandwidth for the download while freeing up low-latency resources for other active users who may need them.
If the user subsequently engages in a casual game with relaxed latency and bandwidth requirements, the 6G system recognizes this scenario and adjusts the RAN resource allocation accordingly. The casual game, which has less stringent requirements, is managed with a balanced approach, providing adequate bandwidth without the necessity for ultra-low latency.
9.17.2 Pre-conditions
Alice has a 6G smartphone with a 6G communication service subscription to MNO XYZ. Her phone has on device AI capabilities and therefore, able to collect and analyze the characteristics of the different applications (e.g. duration and traffic patterns) that she consumes on her phone.
Alice is heading to the airport for a business trip and plans to watch a movie online, play a high-intensity cloud-based game, and anticipates a call from her boss during transit.
While running an application, Alice provides information related to the application to MNO XYZ’s who leverages the information to optimize the 6G communication service delivered to her phone.
9.17.3 Service Flows
1. Shortly after leaving the office, Alice clicks on the app to start streaming a 4K movie, this triggers her 6G phone to retrieve the movie information related to the application (with traffic patterns and preferences) and sends the information related to the application to the network. Using the information related to the application and the application requirements provided by the UE, the network has more comprehensive information of the current and expected traffic patterns (such as movie length, video/audio segment sizes, segment durations, and fetching timings) and can adjust the data rates and scheduling timing to support high-speed 4K video streaming efficiently, avoiding interruption. In contrast, under 5G network, aligning resources with traffic becomes challenging without insight into the information related to the application. As a result, network may over-provision resources, leading to suboptimal resource utilization impacting the capacity.
2. When Alice starts the cloud-based car racing game, the UE recognizes the need to guarantee a low latency and high bandwidth connection. The UE shares the information related to the application (e.g. frame size distribution, frame interval for audio/video, jitter, control input packet size distribution, periodicity, offset to system time) and adjusts QoS settings to ensure a seamless gaming experience. This process involves synchronizing resource allocation with the timing and sizes of gaming video and audio frames to minimize latency, as well as allocating uplink resources in advance based on anticipated control inputs. Conversely, under 5G, without knowing the traffic pattern, network may not be able to align the resource allocation with traffic arrival times. This can affect the efficiency of queuing delay management and the level of responsiveness of gaming.
3. In the middle of the game, Alice pauses the game to take a messenger call from her boss. The UE detects the shift in application usage and notifies the network. In response, the network adjusts the resource allocation by temporarily reducing bandwidth allocation for gaming and prioritizing the resources based on the voice traffic pattern (e.g. audio frame size and frame interval) to maintain high quality voice call and efficient resource utilization. In comparison, under 5G, the network is unaware of the change in application usage and hence cannot adapt to the primary traffic from the messenger call, resulting in inferior performance and inefficient resource usage.
4. After the call, Alice resumes the game. The network side is informed of the information related to the application change and reallocate resources once again, such as aligning the resource to gaming video/audio frame timing and frame size, proactively allocate UL resource based on predictive control input on uplink, ensuring Alice enjoys a smooth gaming experience.
9.17.4 Post-conditions
Alice enjoys seamless and smooth gaming, movie downloading and calling experience over 6G on her way to the airport.
9.17.5 Existing features partly or fully covering the use case functionality
QoS framework in 5G is used to provide differentiated handling of application traffic. This is achieved by associating communication requirements with the application’s service data flows (SDF). Such communication requirements are specified by means of packet delay budget, guaranteed flow bit rate (GFBR), maximum flow bit rate (MFBR) etc.
The QoS request for application traffic can be initiated by network or UE. In the case of UE-initiated QoS, the current framework lacks the information related to the application which can be used to deliver efficient communication service while meeting the user application performance requirement.
9.17.6 Potential New Requirements needed to support the use case
[PR 9.17.6-1] Subject to operator policy, 6G network shall provide means to receive information related to the application from an authorized UE or 3rd party application.
NOTE: Example of information related to the application can include previous, current and/or expected user application characteristics. When provided by the UE, the network can verify the information.
[PR 9.17.6-2] Subject to operator policy, the 6G network shall support mechanisms to leverage information related to the application in delivering efficient communication service and meeting the application’s performance requirements.
9.18 Use Case on Immersive Audio Production in Live Events
9.18.1 Description
Definitions
transfer interval: time difference between two consecutive transfers of application data from an application via the service interface to 3GPP system. As stated in TR 22.827, transfer interval refers to the periodicity of the packet transfers. It has to be constant during the whole operation time.
end-to-end latency: (see TS 22.261 [14]) the time that it takes to transfer a given piece of information from a source to a destination, measured at the communication interface, from the moment it is transmitted by the source to the moment it is successfully received at the destination.
audio processing time: the time that it takes at application to perform all kinds of audio data processing such as, filtering, gain, encoding, decoding.
latency experienced by the user: the total one-way delay between capturing an analog audio signal and replaying it. It is the sum of all internal time delays introduced by all involved entities of the wireless system to transfer an analog signal from a source, e.g. a microphone, to a sink where the original signal is reproduced in an analog manner, e.g. at a loudspeaker. This latency does not refer to a single data packet but to all packets of a consecutive data stream with a constant periodicity, often called as real time communication.
mouth-to-ear latency: (see TS 22.263 [67]) end-to-end maximum latency between the analogue input at the audio source (e.g. wireless microphone) and the analogue output at the audio sink (e.g. IEM). It includes audio application, application interfacing and the time delay introduced by the wireless transmission path.
Real time latency budget: the overall real- ime latency budget is composed of different parts depending to which kind of latency it refers. Figure 9.18.1-1 shows the components that build up the latency experienced by the user. During the time period of the transfer interval the analog audio signal is converted from analog to digital and split into frames of a constant size in samples. Then the audio data are further processed, e.g. audio encoding, and delivered to the wireless network. The network adds its specific end-to-end latency and after receiving the data, the application decodes the audio data and converted it from digital to analog to generate an analog signal that can be replayed. In case of unsynchronized operation between application and 3GPP network, additional time for buffering at the interfaces between the two must be included.
Figure 9.18.1-1: Latency experienced by the user
In a live event or concert, immersive audio production aims to fully engage the audience by delivering three-dimensional soundscape experience. The three-dimensional listening experience can be enjoyed either on-site or remotely via a live audio stream. This is achieved by using audio devices that capture the sound coming from all directions and combine their outputs accordingly to create a three-dimensional audio stream. The production of immersive audio streams can be supported by two different types of devices:
• Type A: microphone array
o contains at least four audio transducers,
o delivers either an immersive audio output stream or the raw data of each transducer, and
o has capabilities for localization.
• Type B: single microphone
o has capabilities for localization,
o includes a mechanism for tight synchronization, and
o outputs a raw mono audio stream.
The type of capturing device used depends on the respective usage scenario.
In addition to capturing devices, monitor devices are used in audio productions that replay the audio signals on loudspeakers, commonly known as stage monitor wedge, or individual in-ear monitor systems (IEM).
IEMs typically receive a binaural, stereo, or double-mono mix, while the wedge is fed by a mono mix. Stage monitor wedges are usually not wireless, as they generally have a built-in amplifier or are connected to an external close-by amplifier. IEMs are always based on wireless transmission.
Synchronicity requirements
The devices deployed in live audio production scenarios may require support for time synchronization, in particular to:
• support alignment of user data from different audio sources (wireless microphones),
• minimize buffering when processing multiple isochronous data streams, and
• reduce latency by optimizing interfacing and audio data flow through all network entities, i.e. alignment of the processing of the audio data with the scheduled 3GPP network traffic.
Synchronicity is defined as the maximum allowed time offset between the synchronization leader and any network device synchronized with it specified in seconds.
The requirements for the alignment of the phase locked mono audio streams depend on the targeted output stream (see 5G-MAG report: “Time synchronization services for media production over 5G networks”[[SUGGESTION_START]] [363][[SUGGESTION_END]]):
• mono audio signal: 50 - 100 µs,
• stereo audio signal: 10 - 50µs, or
• immersive audio signal: 1 - 10µs.
For immersive audio capturing with independent audio devices generating a mono audio stream such as wireless microphones, the phase difference between the different audio streams must be known to compensate for it later when generating the immersive audio stream, or it must be so small and constant that it has no impact on the immersive audio output stream.
If the phase difference is in any order and varies, it might be very difficult or nearly impossible to produce an immersive audio image of the previous captured scenario.
Latency requirements
In general, latency of a data network depends on the data service, i.e. packet size and transfer interval / periodicity. Typical network performance tests use Ping measurements with 32-byte packets in random sequence without fixed transfer intervals. This might be valid to explore average network performance but does not reflect live audio applications where a streaming latency should be used to specify the overall system latency.
The overall system latency is made up of various components, including transfer interval, end-to-end latency, audio processing time, and sound propagation time in case of not using IEMs.
Latency in live monitoring scenarios can degrade the perceived quality of the audio and even affect the artist’s / user’s ability to perform. Different factors influence the effect of latency, such as the musical abilities of the musician, his critical listening skills, and the type of instrument played. In addition, the monitoring environment also affects the audibility of latency. Therefore, it does not exist a hard limit for the latency requirements but different limit ranges for different live monitoring scenarios and applications. The total tolerated latency varies from 4ms in case of live monitoring with IEM up to 40ms in case of live monitoring with wedge monitor.
9.18.2 Pre-conditions
- The local operator or the person responsible for the production network provides a network that supports all needed requirements regarding latency, synchronicity, and reliability.
- All wireless microphones and IEMs on-site are switched on, registered, and connected to the 6G system.
- Audio processing units for e.g. creating IEM streams or immersive audio streams are connected to the 6G system.
9.18.3 Service Flows
A typical immersive audio service flow in a live event is composed of several performers using immersive audio capturing devices (wireless microphones) and an audience experiencing the immersive audio content through loudspeakers installed at the venue or remotely through streaming platforms.
1. The event location is equipped with sound capturing devices according to the producer’s instructions.
2. Each sound capturing device supplies audio and position information to the 6G system.
3. Audio processing units connected to the 6G system combine the incoming audio streams from the different capturing devices and generate required audio output streams, e.g. individual IEM streams, immersive streams for on-site sound reinforcement, or immersive streams for remote consumption.
4. The generated audio streams are sent back to the respective sink.
5. The audio stream is played back through the respective audio device.
9.18.4 Post-conditions
The immersive audio production at a live event should operate as needed without any perceivable interruptions/audible impairments during the entire duration of the event.
9.18.5 Existing features partly or fully covering the use case functionality
In TS 22.263 [67] the AV production use case is described in detail and its associated requirements are explained and defined. Here in clause 4.3, it is pointed out that “the 5G system should enable non-public networks that can be deployed in an agile ad-hoc way”. The related requirements of NPNs are described in TS 22.261 [14], clause 6.25.
Periodic deterministic communication required for audio streaming applications in live scenarios is already described in TS 22.104 [64], clause 5.2, and examples for the required transfer interval can also be found there as well as in TS 22.263 [67]. The QoS model to support low latency real time communication is defined in TS 23.501 [140], clause 5.7. Standardized 5QI values are also specified that support a packet delay budget of 5ms with a guaranteed flow bit rate.
Synchronicity is a key parameter to combine individual mono audio streams and convert them into an immersive audio stream. In TS 22.263 [67] clock synchronization is already described but it refers to align video and audio streams only. In addition, TS 23.501 [140] describes features that can be used independently or in combination to enable time-sensitive communication, time synchronization and deterministic networking. To interact with the 5G system two types of translators are described: the Device-Side TSN Translator (DS-TT) on the UE side and the Network-Side TSN Translator (NW-TT) on the 5G network side.
9.18.6 Potential New Requirements needed to support the use case
[PR 9.18.6-1] The 6G system shall be able to provide deterministic low-latency with the KPI requirements summarized below:
Table 9.18.6-1: Performance requirements for immersive audio production in live events.
Scenario
# of active UEs
(Note 1)
Max. UE speed
[km/h]
Max. service area [m²]
Synchro-
nicity [µs]
(Note 2)
Max. E2E latency [ms]
(Note 3)
Positioning accuracy [m]
(Note 4)
Min. packet error rate
User data rate UL [Mbit/s]
(Note 5)
User data rate DL [Mbit/s]
(Note 6)
UE device type A:
large-scale event
15 - 80
50
500 x 500
1 - 10
0.5
0.5 - 1
10-6
5 - 20
0.5 - 2
small-scale
event
1 - 15
10
50 x 50
1 - 10
0.5
0.5 - 1
10-6
5 - 20
0.5 - 2
UE device type B:
large-scale event
50 - 300
50
500 x 500
1 - 10
0.5
0.5 - 1
10-6
1.2 - 2.5
0.5 - 2
small-scale
event
4 - 50
10
50 x 50
1 - 10
0.5
0.5 - 1
10-6
1.2 - 2.5
0.5 - 2
Note 1: the figures are estimated assuming a steady increase in the use of wireless microphones based on the development of the last decades
Note 2: according to [[SUGGESTION_START]][363].[[SUGGESTION_END]]
Note 3: more stringent values compared to TS 22.263 [67], Table 6.2.1-1 as immersive audio requires higher processing time for encoding and decoding
Note 4 estimated range based on experience with current immersive audio productions
Note 5: range from uncompressed audio with 24 bit / 48 kHz up to 24bit / 96 kHz as for immersive audio uncompressed audio is preferred and there is no compression mechanism for immersive audio available at this point in time
Note 6: range from compressed audio up to uncompressed audio with 16 bit / 48 kHz for the use in IEMs
NOTE: This table is derived from Table 6.2.1-1 of TS 22.263 [67] but specifies more stringent values for E2E latency and user data rates and specifies the additional KPIs synchronicity and position accuracy as this is required for immersive audio production.
Editor’s NOTE: Values in Table 9.18.6-1 are FFS.
10 Massive Communication
10.1 General
Massive Communication has been described as follows by ITU-R: "This usage scenario extends massive Machine Type Communication (mMTC) of IMT-2020 and involves connection of massive number of devices or sensors for a wide range of use cases and applications." [27].
Normative requirements and KPIs for M-IoT, part of massive communication have been specified for 5G in TS 22.261 [14], TS 22.104 [64] and TS 22.262 [57]. Use cases and potential new requirements included in this clause of the present document consider the gap with these existing stage 1 specifications.
10.2 Use case on wide-area coverage
10.2.1 Description
Alice and Bob are hiking in a remote area. Bob carries his smartphone, while Alice forgot her smartphone at home and only has the smart watch with her. Alice and Bob lose track of each other in the mountains. Bob tries to call Alice, but the normal connection of a smartphone does not initially get through. Thanks to the 6G phone built-in wide-area coverage capabilities, his phone automatically activates wide-area coverage capabilities enabling Bob to call Alice. In this case, the smartphone has similar coverage performance as the smart watch.
Table 10.2.1-1: Potential sustainability impacts of the use case:
(the UN SDGs/GDC matching goals of each aspect within 3GPP context)
Potential benefits of the use case (added value)
Potential areas of attention of the use case (risks to be mitigated)
Environmental sustainability aspects
(UN SDGs 12, 13, 14, 15 and indirectly 6, 7 & 11
UN GDC “Develop principles for environmental sustainability of digital technologies”)
Energy resources
(UN SDG 7, 11, 12)
Adapting to constrained availability of UE power
Increased energy use to build this ecosystem for wider coverage (e.g. devices, base stations)
Material resources
(UN SDG 11, 12)
Decrease of e-waste on land, and oceans thanks to broadband and wide-area coverage capabilities in one device (no need for extra companion IoT device for e.g. personal safety, common technology components).
Increased material use to build this ecosystem (e.g. devices, base stations)
Emissions
(UN SDG 6, 7, 11, 12, 13, 14, 15)
Helping reducing carbon emissions, air, soil & water impact & pollution by offering services in remote area via wide-area coverage capabilities (less densification needed)
Rebound effects connected to the design and development of 6G systems, while considering e.g. production-emissions
Increased land use (e.g. networks, data centres, ground stations) could potentially impact existing habitat, and thus, biodiversity
Socio-economic sustainability aspects
(UN SDGs 2, 3, 4, 5, 8, 9, 10, 11, 16 & 17 and indirectly 12)
UN GDC “Closing Digital Divides and Accelerating SDG Progress”
&
“Expanding Digital Economy Inclusion”
&
“Fostering an Inclusive, Safe Digital Space”)
Education
(UN SDG 4)
Providing wider access to connectivity to the most, thus favouring education in remote areas
Health
(UN SDG 3)
Providing wider access to connectivity to the most, thus favouring alerting of health issues and care in remote areas
Potential mental health problem due to possibility of always being connected and being traceable
Inclusion & Equality
(UN SDGs 11, 10, 4, 5 and indirectly 3, 16 & 17)
Increasing inclusivity/digital inclusion, accessibility
Maintaining affordability by avoiding dedicated extra device, and by limiting costs of low power and wide area chipsets (economies of scale) to provide access to remote areas
Potential digital inequalities for people with functional variation / ageing population / IT literacy if all services are meant to be handled digitally
Trustworthiness
(UN SDGs 11 and indirectly 3 & 17)
Increasing operation resilience and service availability in case of adversarial events (e.g. natural disasters)
Enhanced trustworthiness of digital services (availability and accountability)
Improving personal safety via a “fallback” connectivity
Potential risk to privacy if all services are meant to be handled digitally
Food
(indirectly UN SDG 2)
Increases food yield from enhanced agricultural management
Increases agricultural land leveraging wider coverage areas
Work & income
(UN SDG 8 and indirectly 12)
Increased labor opportunities in remote areas (including handling of safety risks via emergency calls/messages)
Infrastructure
(UN SDG 9)
Improving Housing, Transport, Connectivity, Water, Waste management infrastructures in remote areas
TCO reduction
(UN SDGs 8, 9 and 12)
Common technology components between smartphones and IoT devices for wide-area coverage capabilities reduce costs of research, engineering, production and operation
10.2.2 Pre-conditions
Alice’s smart watch has a subscription with terrestrial operator TerrA.
Bob’s smartphone has a subscription with terrestrial operator TerrA.
Alice’s 6G smart watch has built-in wide-area coverage capabilities.
Bob’s 6G smartphone has built-in wide-area coverage capabilities.
10.2.3 Service Flows
The service flow is shown in Figure 10.2.3-1 as follows:
1. Bob moves from the ground floor to the cellar in the house to pack his luggage for hiking. The terrestrial coverage gets poor. Bob couldn’t make a video call, so he sends a message to his family to ask where his trekking poles are.
2. When everything is set, Bob leaves the house and heads to hiking. He makes a video call to his family and is exciting about his upcoming trip.
3. Alice and Bob start hiking in the mountain area.
4. They lose sight of each other after navigating several twists and turns.
5. Bob’s smartphone has a very poor coverage in the area. But with wide-area coverage capabilities incl. e.g. coverage extension features, Bob can still call Alice.
6. Alice answers the call with her smart watch.
Figure 10.2.3-1: UE with wide-area coverage capabilities
10.2.4 Post-conditions
Alice and Bob find each other and continue hiking.
10.2.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14], clause 6.4 on resource efficiency includes the following requirements:
The 5G system shall minimize the signalling that is required prior to user data transmission.
NOTE: The amount of signalling overhead may vary based on the amount of data to be transmitted, even for the same UE.
10.2.6 Potential New Requirements needed to support the use case
[PR 10.2.6-1] The 6G system shall support UEs with both wide-area (i.e. rural area and deep indoor) coverage capabilities and broadband capabilities.
NOTE: The wide-area coverage performance target will need to be confirmed by RAN working groups.
[PR 10.2.6-2] The 6G system shall provide emergency services (e.g. emergency calls) support from rural area and deep indoor coverage scenarios, including leveraging wide-area coverage capabilities.
[PR 10.2.6-3] The 6G system shall support MO and MT basic services with low-capacity demands (e.g. messaging, SMS, and small data).
10.3 Use case on utility infrastructure monitor and control
10.3.1 Description
The electricity utility meters are central and critical across societies and enterprises. They are increasingly becoming more and more connected at large scale. This is driven by both new advanced applications having new requirements, as well as enabled by new technologies, e.g. renewable energy sources and battery technologies. Utility components in mind here include primarily electricity next-generation grid edge intelligence smart meters, but also partially or fully battery-powered devices, e.g. Fault Circuit Indicator (FCI), Direct Transfer Trip (DTT) and Fault Location, Isolation, and Service Restoration (FLISR) units, that have quite similar requirements around metering, monitoring and control of distribution assets.
The electricity utility sector has already massively deployed a first generation of smart metering infrastructures that are operational on a global scale since the last 15+ years. The primary focus has been the monitoring of consumption towards e.g. accurate billing and to automate meter readings. The utility industry is now planning for the next generation of a data driven advanced metering infrastructure (AMI 2.0) that will enable a range of new more advanced use cases for monitoring and controlling the production, distribution, and consumption of electricity, and at the same time making the electrical grid even more resilient to disruptions and to become more stable.
The introduction of distributed energy resources (DER) for more sustainable energy production as well as securing energy availability with solar panels on homes, buildings and on land as a prominent example require connectivity for proper monitoring and further distribution of surplus electricity into the overall grid (excluding high voltage lines), The electrification of road transportation (EV) requires connected charging infrastructure that can also serve as intermediate storage, and the latter is also possible by standalone batteries where DER is used. Connected smart streetlights is another example to ensure proper energy savings. Another application is Demand-Response control (DR) that can shift electricity usage to the benefit of both the consumer (cost control), and the utility operator (control connected appliances to shave off peaks) by controlling appliances and other energy intensive assets.
A related application that targets battery powered devices is the FCI. These are mounted directly on the power lines (excluding high voltage lines), with a typical distance of 1km and are today used for after the fact detection where line failures have occurred. However, with the right balance of low latency network access and lower power consumption to match its energy harvesting capabilities, the FCIs can have an active role in the protection and control scheme of the power lines. To be able to use these for real time protection and control these needs to report faults on power line with latency below 100 ms. For normal operation only heartbeat (keep alive) messages are sent every 10min. Batteries are needed since they are hanging on the power lines or the poles without external power supply. Limited energy harvesting can be made.
A further set of applications involve DTT and Fault Location, Isolation, and Service Restoration (FLISR) and target the operations of the distribution grid itself: to do load balancing, fast outage detection, fault isolation, pacing etc. All of these applications are data-driven requiring timely access to data and events, even down to “real-time” to make it possible to monitor and control the entire distribution network with all its various components in order to achieve the desired outcomes and objectives.
As a example of how the 6G system can be used is the Utility Broadband Alliance (UBBA) which is a collaboration of utilities serving more than 125 million electric customers in the United States. UBBA is championing the transition to cellular broadband network deployments for critical infrastructure. The UBBA Plugfest Task Force detailed in its 2024 Plugfest Testing Report [143] how a next generation of data driven advanced metering infrastructure (AMI 2.0) can enable highly needed operational efficiencies to grid monitoring and control and what networking requirements that are needed to achieve this. The report supports this 6G Use Case. Other references are IEC 62056-6-1:2023 Object Identification System (OBIS) [109], IEC 62056-6-2:2023 COSEM interface classes [110] and IEC 62056-5-3:2023 DLMS/COSEM application layer [111].
Sustainability impact analysis:
The positive impact from the 6G system use case will depend on where the technology is applied. For instance, a 6G system used in energy supply systems will help provide reliable power distribution to businesses and households, while massive use of a 6G system with sensors in a factory will result in other positive impacts. Below results are written from a general perspective.
Energy resources: As explained, the 6G system can play a central role in sustainable and resilient use of energy resources via a wide range of applications in the electricity distribution network. Also, the network energy consumption supporting the utility infrastructure monitor and control use case must be taken into account.
Emissions: More operational efficiency can reduce emissions in parallel to energy and material use, however there is a risk that the sensors will lead to additional emissions from electronic waste as massive amounts of equipment spread out will be expensive to collect.
Trustworthiness: There is an introduced risk of cyber-attacks from introducing connected devices across society-critical infrastructures such as utilities that require security measures towards various threat vectors. Also, the risk for privacy intrusion needs to be considered. There are mature and efficient security technologies that are adequate and fit for use in M-IoT deployments, such as eSIM, TEE and efficient secure protocols with end-to-end security.
Infrastructure: The 6G system with appropriate and new requirements can serve the growing utility infrastructure needs for better monitoring and control of distribution grid infrastructure in an efficient way. Vulnerability includes the increased risk of cyber-attacks as mentioned, something that comes with any connected operations.
10.3.2 Pre-conditions
Company NoName is a utility company that utilizes next-generation grid edge intelligence smart meters for electricity in a region NewLand. The company has the requirements that the meters can be used with 20 years longevity and should be possible to be deployed in hard-to-reach areas and basements below ground.
The company may want to receive instantaneous load and voltage status from the meters at a fixed time interval of one time per minute and in a flexible way some other battery-powered sensors should only report occasionally when the application requires it.
With a new improved technology Company NoName is also able to enhance their monitoring of their power lines. To have an active way of monitoring the power lines low latency and high reliability communication is needed.
Some of these meters needs to be battery powered, thus needs to run with limited energy consumption and can be deployed in both new 6G and existing 5G spectrum in a flexible way.
Company NoName today has services currently served by NB-IoT and eMTC but the new meters and their frequent minute level communications are overloading those networks, and therefore be supported by a more efficient radio . This is also in the interest of the mobile operator ABC, who wants to reduce the network energy consumption and cost of operations.
ABC wants to provide the new 6G service for utility infrastructure monitor and control to the customer such that the utility company NoName experience excellent service performance, while ABC also experience low total cost of operation, with efficient management, low energy consumption, and flexible use of spectrum.
10.3.3 Service Flows
Company NoName wants new meters to be future proof. This is also in the interest of the Mobile Operator ABC, who wants to reduce cost of operation and therefore have the utility infrastructure monitor and control use case supported by a 6G system.
Company NoName provides new next-generation grid edge intelligence smart meters with 6G capability to its customers.
Mobile Operator ABC experiences a reduced cost of operations.
10.3.4 Post-conditions
The customers use next-generation grid edge intelligence smart meters and associated battery-powered grid edge sensors with confidence that the distribution grid can be run at a high usage level but not be overloaded despite an increasing number of EV Supply Equipment, Roof-top Solar and other DER equipment connected at the edge.
10.3.5 Existing features partly or fully covering the use case functionality
In TS 22.261 [14] clause 6.4 Resource efficiency states some general requirements for IoT devices.
The 5G system shall minimize control and user plane resource usage for data transfer from send only UEs.
The 5G system shall minimize control and user plane resource usage for stationary UEs (e.g. lower signalling to user data resource usage ratio).
The 5G system shall minimize control and user plane resource usage for transfer of infrequent small data units.
The 5G system shall optimize the resource use of the control plane and/or user plane for transfer of small data units.
The 5G system shall optimize the resource use of the control plane and/or user plane for transfer of continuous uplink data that requires both high data rate (e.g. 10 Mbit/s) and very low end-to-end latency (e.g. 1-10 ms).
The 5G network shall optimize the resource use of the control plane and/or user plane to support high density connections (e.g. 1 million connections per square kilometre) taking into account, for example, the following criteria:
- type of mobility support;
- communication pattern (e.g. send-only, frequent or infrequent);
- characteristics of payload (e.g. small or large size data payload);
- characteristics of application (e.g. provisioning operation, normal data transfer);
- UE location;
- timing pattern of data transfer (e.g. real time or non-delay sensitive).
The 5G system shall efficiently support service discovery mechanisms where UEs can discover, subject to access rights:
- status of other UEs (e.g. sound on/off);
- capabilities of other UEs (e.g. the UE is a relay UE) and/or;
- services provided by other UEs (e.g. the UE is a colour printer).
The 5G system shall be able to minimise the amount of wireless backhaul traffic (e.g. consolidating data transmissions to 1 larger rather than many smaller), when applicable (e.g. providing service in an area subject to power outages).
The 5G system shall support small form factor UEs with single antenna.
NOTE: Small form factor UEs are typically expected to have the diagonal less than 1/5 of the lowest supported frequency wavelength.
In TS 22.261 [14] clause 8 Security, security requirement for IoT devices is listed.
In TS 22.104 [64] Annex A4.7 Advanced metering following proposed requirement can be found.
Table 10.3.5-1: Communication KPI for advanced metering (Table A.4.7-1 from TS 22.104 [64])
Use case#
Characteristic parameter
Influence quantity
Communication service availability: target value
Communication service reliability: mean time between failures
End-to-end latency: maximum
Service bit rate: user experienced data rate
Message size [byte]
Transfer interval: target value
Survival time
UE
speed
# of UEs
Service area
1 – Advanced Metering
> 99.99
Accuracy fee control: < 100 (note 1);
General information data collection:
< 3000
UL:
< 2 M
DL:
< 1 M
–
–
–
stationary
< 10 000/ km2
(note 2)
–
NOTE 1: One-way delay from 5G IoT device to backend system. The distance between the two is below 40 km (city range).
NOTE 2: It is the typical connection density in today city environment. With the evolution from centralised meters to socket meters in the home, the connection density is expected to increase 5 to 10 times.
10.3.6 Potential New Requirements needed to support the use case
[PR 10.3.6-1] The 6G system shall support communication service with the following KPI’s in Table 10.3.6-1
Table 10.3.6-1: KPI for M-IoT
Profile
Characteristic parameter
Bit rate down link (Mbit/s)
Bit rate up link (Mbit/s)
End-to-end latency: maximum (ms)
Payload Message size (Kbyte)
# of UEs
connection
Communication service availability: target value
Transfer Interval
Smart Grid monitor and control
Peak [5]
Peak [5]
TBD
TBD
(note)
TBD
[> 99.99]
TBD
NOTE: Typical message sizes are 500-byte payload meter readings to 1 MB differential and 20 MB complete firmware upgrades
11 Further Use Cases on Industry and Verticals
11.1 General
The industrial sector has been seen as promising growth area since 5G, and the potential for growth continues into 6G. Some of the vertical markets and industrial/vertical applications that have influenced the development of 5G specifications include Mission Critical (MC) services (used in public safety and railway), maritime services, Uncrewed Aerial Systems (UAS), V2X, industrial applications, medical, audio-visual production, and cyber-physical control applications.
In 6G, newer technologies (e.g. AI, sensing) may offer industry and vertical markets enriched services and capabilities from what is possible in 5G. It is envisioned that 6G will support diverse devices, equipment, and appliances (e.g. robots) to leverage network services and capabilities.
11.2 Use case on communication on board of UAM aircrafts
11.2.1 Description
Urban air mobility (UAM) is a new safe, secure and more sustainable air transportation system for passengers and cargo in urban environments, enabled by new technologies and integrated into multimodal transportation systems. The transportation is performed by eVTOL aircrafts, remotely piloted or with a pilot onboard [32]. In February 2024 an eVTOL named "PROSPERITY" [33] that can contain 5 passengers conducted a Shenzhen-Zhuhai test flight, which was a cross-sea and cross-city route. KT showed, at the MWC2024 [362], their UAM Skypath solution, which can provide 5G service for UAM aircrafts flying at 300 – 600 meters altitude.
Compared with the common UAVs, the UAM aircrafts have the following major differences:
- Large size and heavy weight, higher AGL (above ground level)
Low-altitude airspace usually refers to the airspace with a vertical distance of less than 1000 m from the ground, and it can be extended to less than 3000 m according to the characteristics and actual needs of different regions [34]. The AGL of UAM aircrafts can be up to 1000 m, while the AGL of small UAVs is less than 300m. Communication for the UAM aircrafts with higher AGL need to be considered.
- Higher reliability and safety requirement with human beings onboard
UAM aircrafts share some common low altitude airspace. As predicted by Professor Shen, there will be about 100,000 UAVs flying in Shenzhen's sky at the same time in the future [36]. Considering the area of Shenzhen is 1997 km2, there will be about 50 UAVs flying in 1 km2 airspace. To ensure high reliability and safety, the UAM aircrafts must be aware of the object information that are near its flight trajectory.
To guarantee the safety of aircrafts, Detect and Avoid (DAA) technology is widely used in UAVs by using a combination of sensors, cameras, and radar to continuously monitor the UAV's surroundings [38]. These sensors detect obstacles, other aircraft, and potential hazards in the flight path. The system then processes this information in real time and adjusts the UAV's flight path to avoid collisions. However, the capabilities of the sensors on UAVs are not able to sense the blockage far away. Since the UAM aircrafts carry human beings on board, UAV's DAA system is not adequate to ensure the safety of passengers onboard. By also utilizing the 3GPP sensing service, the safety of UAM aircrafts flying in the common airspace can be guaranteed, as well as the running efficiency of UAM aircraft can be improved so as to transport more passengers or goods.
Typically, the message size for sensing one object could be 1 kbyte [37], including information of size/position/speed/direction. Around 25 objects (25 kbyte) per frame (20 ms) need to be sensed for an aircraft. The reliability requirement for UAM aircrafts is about 99.9 %.
In addition, with passengers onboard, human communication on board the UAM has to be considered. Passengers onboard can access internet, use video conferencing or video chat, and enjoy immersive multimedia services such as cloud gaming [305].
11.2.2 Pre-conditions
The UAM aircrafts are connected to the 6G network, which provides the following services:
- Sensing services to the UAM aircrafts;
- Communication services to the passengers onboard.
11.2.3 Service Flows
1. May and Fei plan to visit City B from City A by UAM, and the aircraft can contain up to 4 passengers.
2. To guarantee the safety of the passengers onboard, the UAM aircraft requests the sensing service from the 6G network. The base station(s) along the flight path will sense the environment information especially other aircrafts within its interesting area, e.g. the interesting area is an airspace with the size of 1 square kilometre and height from 0 to 1000 meters.
3. As part of the sensing service, the 6G network sends the sensing and/or the warning information to the UAM aircraft.
4. Upon receipt of the above information, the UAM aircraft further processes the information to identify any collision threats. If there are some threats to flight safety, the UAM aircraft will perform collision avoidance in advance.
5. Meanwhile passengers onboard either watch HD video or surf the Internet using their smartphone. The view is very beautiful during the flight, May is very happy to share the scenery with her friends through 4K real time video on social media by smartphone. Fei is not interested in the scenery during the flight, and he is enjoying a football match live broadcast. The core network sends data of the football game with 4K or 8K live broadcast to the base station, then Fei can receive the football game data by his smartphone.
11.2.4 Post-conditions
Thanks to the sensing services and communication services provided by the 6G, May and Fei had a great onboard experience and safely arrived at the destination via the UAM aircraft.
11.2.5 Existing features partly or fully covering the use case functionality
From TS 22.125 [35] , KPIs for services provided to the UAV applications are defined, especially Table 7.1-1 KPIs for services provided to the UAV applications and Table 7.2-1 KPIs for command and control of UAV operation.
However, the message size of UAV command and control is less than < 10 kbyte. Latency and reliability requirement are not sufficient for UAM aircrafts. In addition, the AGL for UAV is not high enough for UAM aircrafts.
11.2.6 Potential New Requirements needed to support the use case
[PR 11.2.6-1] The 6G System shall support the transmission of sensing result information to a UAM aircraft about its surroundings with the following KPI requirements.
Table 11.2.6-1: Performance requirements for transmission of sensing result information to a UAM aircraft
Typical transmission interval
Altitude AGL
Typical message Size
End to end Latency
Reliability
Sensing result to a UAM aircraft
[20 ms]
up to 1000 m
[25 kbyte]
(note)
[20 ms]
[99.9 %]
NOTE: Typically, the message size for one object detected and/or tracked via sensing is 1 kbyte [37]. It is assumed that around 25 objects (25 kbyte) per frame (20 ms) are sensed surrounding an aircraft. The reliability requirement for UAM aircrafts is about 99.9 %.
[PR 11.2.6-2] The 6G System shall support services provided to the UAM applications (for the passengers and devices onboard the aircraft) with the following KPI requirements.
Table 11.2.6-2: Performance requirements for UAM onboard aircraft services
Services
Data rate
End to end Latency
Altitude AGL
Reliability
(note 4)
Service area
8K video live broadcast
100 Mbps
Traffic from the UAM aircraft
(note 1)
200 ms
(note 1)
up to 1000 m
95 %
Urban, scenic area
600 kbps
Traffic towards the UAM aircraft
(note 1)
20 ms
(note 1)
up to 1000 m
95 %
Video streaming
4 Mbps for 720p video
9 Mbps for 1080p video
Traffic from the UAM aircraft
(note 1)
100 ms
(note 1)
up to 1000 m
95 %
Urban, rural area
100 Mbps for 8K video
Traffic from the UAM aircraft
(note 1)
100 ms
(note 1)
up to 1000 m
95 %
Remote controller through HD video
>=25 Mbps
Traffic from the UAM aircraft
(note 1)
100ms
(note 1)
up to 1000 m
99 %
Urban, rural area
300 kbps
Traffic towards the UAM aircraft
(note 1)
20 ms
(note 1)
up to 1000 m
99 %
Video conferencing or video chat
25 Mbps
Traffic from passengers onboard UAM aircraft
(note 1) (note 2)
100 ms
(note 1)
up to 1000 m
99 %
Urban, rural area
25 Mbps
Traffic towards passengers onboard UAM aircraft
(note 1) (note 2)
100 ms
(note 1)
up to 1000 m
99 %
Immersive multimedia service (e.g. cloud gaming)
500 kbps
Traffic from passengers onboard UAM aircraft
(note 2)
50 ms
(note 3)
up to 1000 m
99 %
Urban, rural area
100~500 Mbps
Traffic towards passengers onboard UAM aircraft
(note 2) (note 5)
50 ms
(note 3)
up to 1000 m
99 %
NOTE 1: These values are aligned with the KPIs for services provided to the UAV applications in TS 22.125 [35], Table 7.1-1.
NOTE 2: The value is per passenger; and it is assumed that up to 4 passengers per UAM aircraft use communication services simultaneously.
NOTE 3: According to TR 26.928 [50], typically a 50ms latency is required for cloud gaming use case.
NOTE 4: According to [306], the reliability of real time services is set to 99%, and that of video streaming is set to 95%.
NOTE 5: Data rate is calculated assuming typical parameters (e.g. resolution, refresh rate and compression rate). Some codecs may further drop the bitrate requirement.
11.3 Use case on cooperating mobile robots
11.3.1 Description
At the centre of this use case are autonomous robots with the ability to move, sense their environment, and perform a productive task collaboratively. These robots can communicate with one another, with other machines, and with nearby humans to perform individual tasks that contribute to a common cooperative objective. The purpose of communication is safety and cooperation, to enable a group of robots to perform tasks beyond their individual capabilities and to enable individual robots to perceive their environment beyond their local capabilities.
This use case focuses primarily on local ad hoc connectivity embedded in private networks. Scenarios may include industrial manufacturing campus, construction site, and smart living.
In this context, the network of the future is envisioned to enable local cooperation among robots, to support autonomous task-solving by the robots, to enhance the safety of human-machine interaction, and to ensure that only authorized machines and humans can participate in task solving.
Figure 11.3.1-1: Cooperating Mobile Robots Use Case, highlighting two example scenarios
Scenarios revolve around the need to complete a task that surpasses the capabilities of a single robot. The solution lies in robot cooperation, where multiple robots form a cluster to collaboratively accomplish the task. These robots may utilize local ad hoc connectivity for coordinating and controlling their actions for successful task execution, exchanging raw or processed sensor data, sharing AI/ML models, weights, and FL, as well as ensuring safety for humans nearby in the event of an emergency.
AMRs collaborate for a specific task in a spatially confined area for a limited time. Such a group of AMRs needs to communicate with each other, requiring periodic-deterministic communication (time synchronization, high reliability and very low latency) for their collaborative industrial control. Characteristics of the scenarios need to be leveraged, for instance, such as no relative mobile between AMRs moving in unison and potential line of sight when using direct device communication instead of direct network communication.
The 6G system can be an important enabler for addressing the communication requirements of machines in the future. This includes existing industrial automation systems, as well as the emerging type of autonomous embodied agents that encompasses cooperating industrial robots, service robots, and cobots.
Sustainability impact analysis:
Potential sustainability impacts of the use case are clustered by topic. For each topic, a selection of key impacts is provided together with some background information.
Energy: i.e. focus on the energy usage for the correct operation of robots, both on communication links, as well as in the data processing, data storage, and data collection aspects
- [ to be decreased] Total energy usage (kWh) in communication dimensions for the cobots
- [ to be decreased] Total energy usage (kWh) for data transfer (optimising packets, and data volume)
Selected background information:
- Potential benefits: Resource efficiency: Functionalities may be provided by machines with less materials, energy, and waste generated
- Potential areas of attention (risks to be mitigated): Energy is consumed, and materials are used to manufacture, deploy, and operate cobots and associated services.
Materials/Waste:
- [ to be increased] Life expectancy of robots: making better and longer-lasting hardware and/or software, easily upgradable, modular. Avoid having to replace the entire cobot because of failures in the 6G-related aspects
- [ to be increased] # of virtualised functionalities: Preventing or avoiding material usage for dedicated hardware, when these features / or capabilities could be virtualised.
Selected background information:
- Potential areas of attention (risks to be mitigated): The disposal of machines and devices results in increased electronic waste.
Safety:
- [ to be decreased] # of injuries at work/level of severity of work-related injuries/perception of risk: Aimed at ensuring the system will be reliable and thus, no accidents will be produced because of failures in the communication links, or misinterpretation of data in the AI/ML algorithms/Sensing/Positioning/etc.
Selected background information:
- Potential benefits: Safer work environment leading to less injuries.
Trustworthiness/Privacy/Security:
- [ to be increased] Level of acceptance of cobots by humans: Ensuring that the appropriate privacy and security requirements are in place, that no personal data is at risk, that cobots will be resilient and responsive in case of hacking, etc. Overall, ensuring they are trustworthy and accepted.
- [ to be decreased] # of data leaks/breaches/cyber-attacks, with personal information compromised: Measuring the impact of the above metric.
Selected background information:
- Potential benefits: Increased autonomy by robots supporting people with disabilities.
- Potential areas of attention (risks to be mitigated): People’s privacy may be breached by unauthorized use of robots’ and cobots’ sensors.
Productivity/Efficiency: Ensure that 6G is reliable and resilient, and so, no downtimes in a given production scenario are due to failures in the communication-related aspects.
- [ to be decreased] Downtimes.
- [ to be increased] Production KPIs (distinctive per factory).
Selected background information:
- Potential benefits: Increased local and global productivity, cost efficiency and enhanced competitiveness from the use of collaborative robots.
11.3.2 Pre-conditions
The 6G network is dedicated to a specific purpose and to private use, e.g. manufacturing, construction, farming, living, and similar. The 6G network is located in a well-confined geographical area such as factories, industrial sites, enterprises, construction sites, or fields. In several scenarios, functionality and data storage are on-premise.
In outdoor scenarios, wireless 6G communication for wide area connectivity is available.
Electrical power is generally available. Power efficiency is assumed for battery-driven devices like mobile robots.
11.3.3 Service Flows
11.3.3.1 Scenario: Cooperative Carrying with Mobile Robots
Cooperative carrying with mobile robots is a concept where multiple AMRs work together to transport an object that exceeds the carrying capacity of a single robot (see also Figure 11.3.1-1). The special focus is on collaboration between several AMRs, deterministic industrial communication between AMRs, including direct device connectivity (time synchronization, highly reliable and low latency communication).
1. The AMRs gather around the object to be carried collaboratively. They are in close proximity of each other.
2. One of the AMRs initiates the communication group of related UEs, the other AMRs join this group. Communication resources are properly allocated for providing the AMRs with the required level of QoS.
3. At different stages of the service flow, the AMRs may communicate with e.g. digital twin applications or AI/ML compute servers in the 6G network.
4. Industrial communication with time synchronization and deterministic highly reliable and low latency communication is used between the collaborating AMRs. The collaborative robots exchange sensor data and industrial control information with each other for cooperative carrying.
5. The collaborating AMRs move in unison as a group. They have sensing capabilities as well as AI capabilities for task planning and autonomous operation.
6. After placing the object at its final destination, the AMRs leave the communication group of related UEs.
11.3.3.2 Scenario: Autonomous Construction Site
On an autonomous construction site, different types of robots work collaboratively in unison to construct a building (see also Figure 11.3.1-1). The transportation of materials, such as bricks, must seamlessly integrate with advanced construction machinery like 3D printers. Other robots are responsible for post-processing tasks, such as drilling holes autonomously. Humans interact on-site and remotely, for instance, with digital twins and video cameras for virtual guidance and for remote control of construction machines.
This scenario uses:
- Industrial communication for the exchange of industrial control information (time synchronization, deterministic URLLC communication).
- sensing and localization capabilities for, e.g. positioning, location services (absolute and relative), collision avoidance, and detection of human presence, using various sources such as 6G devices with ICAS or HD video cameras.
- Immersive Extended Reality for local and outdoor walkthroughs of future construction stages at the construction site but also in remote offices. Virtual guidance for complex construction aspects for previews and better understanding, remote participation of stakeholders.
- Support for digital twins of the building that provide detailed information about all project stages.
- Support for artificial intelligence applications on autonomous construction machinery.
11.3.4 Post-conditions
The cooperating mobile robots finished the assigned task(s) collaboratively. They will continue to the next task to be done collaboratively.
11.3.5 Existing features partly or fully covering the use case functionality
Direct device connection: TS 22.104 [64] clause 7.2 provides service requirements on direct device connection for cyber-physical control applications. In TS 22.104 [64] clause 7.2.1, however, the use of direct device connection is bound to certain conditions (not being served by a RAN, use of different spectrum).
Non-Public Networks: TS 22.261 [14] clause 6.25.2 provides requirements on non-public networks.
Energy efficiency: Energy efficiency is a general requirement on wireless communication. However, energy saving in the 6G network must not endanger successful, efficient, and high-quality industrial manufacturing.
TS 22.104 [64] clause 7.2.2 provides service requirements to support direct device connection between a group of UEs for periodic deterministic communication (both unicast and multicast) with respective service performance requirements in Table 5.2-1 related to cooperative carrying. These service performance requirements cover also the service performance requirements of this use case expect increased localized density and increased UE speed. Also, the mobility of the group of UEs is covered in this clause.
TS 22.104 [64] clause 7.2.3.2 provides service requirements for clock synchronization over direct device connection.
TS 22.104 [64] clause 7.2.4 requires service continuity only between an NPN and a PLMN, but not the general case.
TS 22.261 [14] Table 7.3.2.2-1 defines performance requirements for horizontal and vertical positioning. While the required speed would be covered by every positioning service level, the necessary accuracies are only covered partially (1 m) or not at all (0.1 m).
11.3.6 Potential New Requirements needed to support the use case
[PR 11.3.6-1] The 6G System shall be able to support the following performance requirements to support Cooperating Mobile Robots.
Table 11.3.6-1: Performance Requirements for Cooperating Mobile Robots use case [112]
Use Cases
Characteristic parameter (KPI)
Influence quantity
Remarks
Max allowed end-to-end latency (ms)
Application data rate (Mb/s)
Reliability (%)
Message size (byte)
Service area
(m²)
Transfer interval (ms)
Connection density
[devices/m2]
UE speed (km/h)
(2) Cooperating Mobile Robots – 6G Network Services
[1 to 10]
[<10]
[<250]
(note 2)
[99.999 to 99.99999]
(note 4)
(note 3)
[20 up to
1,000,000]
(note 5)
[≤ 10]
[≤ 0.5]
(note 1)
[< 20]
Robot to Network for services such as digital twin applications, AI/ML compute servers.
NOTE 1: Local concentration of mobile robots, for instance, cooperative carrying assuming 8 robots on a space of 4 m x 4 m carrying an object.
NOTE 2: The higher data rate applies when e.g. immersive XR, AI/ML traffic, digital twin data, is exchanged. Otherwise, the lower data rate applies. It is expected uplink video will be most demanding.
NOTE 3: Any common message size to be expected. Message size depends on type of exchanged data, for instance, sensor data, video streams, immersive XR, AI/ML traffic, digital twin data, etc.
NOTE 4: The control of real time industrial processes requires very high service reliability. Can be lower for non-real time digital twin or in other contexts (e.g. training, engineering, planning).
NOTE 5: Service area depends on scenario and size of location/collaboration area (collaborative carrying mobile robots (11.x.3.1), factory, construction site (11 x 3.5), field (11 x 3.4))
[PR 11.3.6-2] The 6G System shall be able to support the following positioning performance requirements to support Cooperating Mobile Robots.
Table 11.3.6-2: Positioning Performance Requirements for Cooperating Mobile Robots use case [112]
Use Cases
Positioning accuracy (m)
Velocity (km/h)
Remarks
Cooperating Mobile Robots
[<0.1]
[<1]
[< 20]
Collaborative tasks such as handing over material and objects or robot navigation in close distance to other objects require more accurate positioning. Tasks like robot localization can work with less accurate positioning.
Human presence detection
[0.4]
Pedestrian
[40 cm] resolution within an area defined by [1 m] range around the robot
11.4 Use case on real time digital twins
11.4.1 Description
A digital twin can represent any combination of processes, products, persons, and functionalities of real-world items such as in industry, smart cities, or construction sectors. This contextual digital equivalent of the real world offers a unified access to users and/or agents and is used for interaction, control, prediction, test, maintenance, and management of processes and components. To do so, it needs network connectivity to ingest data from multiple sources, e.g. databases, sensors, tags, network data, data models, and optionally steer / control the respective systems via feedback loops. A digital twin is aggregated, generated, and visualized by running dedicated software, including specialized AI/ML algorithms.
The real time aspect, enabled by the low latency capability of the 6G network, allows to extend the digital twin also towards direct control of the actual physical processes.
This use case covers the following applications (non-exhaustive list)
• DT in a manufacturing plant
• DT for water management and improved traffic management (smart cities)
• DT aggregation of sub-DT to cover complex systems (e.g. full smart cities)
• DT for 6G network planning and operation itself (Network Digital Twin)
• DT in port operations.
Table 11.4.1-1: Potential sustainability impacts of the use case
(the UN SDGs/GDC matching goals of each aspect within 3GPP context)
Potential benefits of the use case (added value)
Potential areas of attention of the use case (risks to be mitigated)
Environmental sustainability aspects
(UN SDGs 12, 13, 14, 15 and indirectly 6, 7 & 11.
UN GDC “Develop principles for environmental sustainability of digital technologies”)
Energy resources
(UN SDG 7, 11, 12)
Energy consumption required for processing real time data, DT generation, data centres, IoT devices, and computing resources
Increased dependency and risks to energy shortage and/or disruptions _ unless local energy production is used
Material resources
(UN SDG 11, 12)
the optimization of industrial and manufacturing processes can lead to better resource efficiency, resulting in lower raw materials extraction and water usage
Reduced waste from the increase of the production quality and efficiency
In some industries, DT can help product design/engineering towards improved modular and sustainable design aiming at circularity patterns and improved lifetime
require manufacturing, and increased material extraction and consumption of resources including water and land to produce sensors and IoT devices
increased material efficiency is likely to reduce costs and consequently create a rebound effect, which will further push for resource depletion, unless sustainable design practices and lifecycle monitoring of each element are put in place, including the usage of eco-friendly materials, etc[[SUGGESTION_START]].[[SUGGESTION_END]]
Electronic waste resulting from the end of life of sensors and equipment, unless proper circularity patterns are in place
Emissions
(UN SDG 6, 7, 11, 12, 13, 14, 15)
Reduced usage of natural resource (e.g. water management, smart cities) by improved monitoring
Reduced GHG emissions from avoided visits to monitored areas, and avoiding operations visits for network planning and operation
Increased emission to produce additional sensors and IoT devices required to construct DTs, and to operate the DT, unless renewable energy is used
Socio-economic sustainability aspects
(UN SDGs 2, 3, 4, 5, 8, 9, 10, 11, 16 & 17 and indirectly 12.
UN GDC “Closing Digital Divides and Accelerating SDG Progress”
&
“Expanding Digital Economy Inclusion”
&
“Fostering an Inclusive, Safe Digital Space”)
Education
(UN SDG 4)
Upskilling on the use of digital technologies
Upskilling on the use of the physical system (training, testing)
Health
(UN SDG 3)
Reduced dependency on humans (e.g. avoid 24/7 monitoring on site, shifts), leading to increased well-being for humans
Reduced exposure to physical risks & EMFs due to proximity with hazardous machines
Inclusion & Equality
(UN SDGs 11, 10, 4, 5 and indirectly 3, 16 & 17)
Enhanced inclusion/opportunities for particular roles, irrespective of age, gender, disabilities, geographies etc.
Creates a new gap related to IT literacy, eg with ageing populations
Potential exclusion of worker populations who do not have coverage to interact remotely with the DT
Trustworthiness
(UN SDGs 11 and indirectly 3 & 17)
Increased trustworthiness on data /information availability due to real time monitoring /control
Improved resilience and flexibility
Improved safety from using digital twin for virtual safety checks (help to predict and reduce the risks in the event of any issue (e.g. plant failure), which can affect the lives of the workers)
Potential risks to the privacy in the event of a cyber-attack attempting to read DT data
Potential risks to the safety in the event of a cyber-attack attempting to control the physical world via DT
Safety/predictability of operations – increased business risk from using technology instead of human perception
Food
(indirectly UN SDG 2)
Enhanced accessibility to drinking water and food and its management from DT related to water/food supply, agriculture, etc[[SUGGESTION_START]].[[SUGGESTION_END]]
Work & income
(UN SDG 8 and indirectly 12)
Facilitates job filing avoiding specific requirements in physical access to site, including facilitating expert replacement
Remote access to the twin can enable employment in other countries, including in other timezones (reducing digital divide)
Potential impact on employability and labour market (replacement of given jobs by digital processes)
Infrastructure
(UN SDG 9)
Enable sustainable urban development (Smart Cities DT): improving smart city infrastructures (e[[SUGGESTION_START]].[[SUGGESTION_END]]g[[SUGGESTION_START]].[[SUGGESTION_END]] water, waste, energy, transport, housing etc[[SUGGESTION_START]].[[SUGGESTION_END]])
TCO reduction
(UN SDGs 8, 9 and 12)
Economic efficiency improvements from optimizing operations via digital twins, e[[SUGGESTION_START]].[[SUGGESTION_END]]g[[SUGGESTION_START]].[[SUGGESTION_END]] allowing many stakeholders to meet in a virtual place, testing before production with end users/customers etc[[SUGGESTION_START]].[[SUGGESTION_END]]
Possible increased costs from complexity of multi-stakeholder ecosystem
Increased CAPEX from digital twin model creation, connectivity and physical system equipment investment
Increased OPEX from energy, digital twin model training & maintenance, and connectivity
11.4.2 Pre-conditions
As example service scenario for the Real Time (RT) Digital Twin use case, a production plant is used (e.g. chemical but could be any manufacturing or production plant).
Typically, these plants are spread over a large area (e.g. one or more km2), mostly outdoor area’s but also including indoor facilities. The constructions are complex and build in all three dimensions with potential metal obstructions preventing a line-of-sight coverage. Still, full network coverage is mandatory over the full geographical area, to which the Digital Twin is associated. Besides the Digital Twin traffic itself, also the day-to-day traffic needs to be handled by the same network.
The network provides the connectivity, sensing and computing power while enabling reliable connections in an energy efficient manner and guaranteeing the required low latency (down to the ms level) to control the chemical processes in real time.
The machines, robots and vehicles are modelled for accurate planning for 24/7 usage and maintenance needs.
11.4.3 Service Flows
Scenario 1: The first scenario indicates the real time digital twins without assistance from network, as shown in Figure 11.4.3-1.
Connected Sensors extend the 6G network wireless sensing capabilities where necessary, to offer the extra required data from every step of the production process. As the digital twin also needs to include all moving items on premise also the tracking of movements and locations (via network sensing and optional external sensors) is enabled. This dense grid of sensors is connected to the Digital Twin, potentially incorporating non-3GPP connection technologies, including via gateways with legacy and/or proprietary technologies to allow re-use of already deployed sensors/systems.
The RT Digital Twin is used for testing purposes, to simulate a specific configuration or setting before really applying it, in this way facilitating any decision or to consolidate and to prevent dangerous situations and/or environmental challenges and come to a continuously adapting control of the installation.
To meet these challenges, the DT (a virtual model of the full petrochemical plant, a complex physical entity, including the necessary AI /ML capabilities) is concurrently accessed via multiple users and devices.
In some cases, the DT is rendered via immersive visualization capabilities for a seamless user interaction.
Immersive Real-Time Digital twin
Immersive Real-Time Digital twin
Figure 11.4.3-1: Real-time Digital Twins Example Scenario
Scenario 2: The second scenario indicates the real time digital twins with assistance from network, as shown in Figure 11.4.3-2.
This scenario addresses cases where real time digital twin services are required, but some terminals in the scene have limited computing power or specific QoS requirements—such as strict guarantees on latency, jitter, and packet loss for critical service flows. In this case, in addition to Scenario 1, more 6G network capabilities are needed to compensate for terminal limitations and meet stringent QoS requirements, thereby ensuring the ultra-low latency, high reliability, and real time response required by users for digital twin applications.
In the scenario, the terminal computing power is limited, so the terminal device focuses on perception and execution of hardware operations; the 6G network is responsible for performing tasks such as data processing, multimodal data fusion, and predictive simulation, and can cooperate with third-party AI capabilities when necessary. In addition to 1, 3, 4 in Scenario 1:
In the scenario, the terminal-side DT implements real time multimodal data perception and final decision execution to achieve continuous control of the devices.
The 6G cloud network provides built-in general AI capabilities (such as support for real time multimodal data analytics, predictive simulation, and intelligent decision-making).
The 6G cloud network should be able to collaborate with 3rd party AI capabilities (e.g. large AI models of vertical industries) at the service and inference levels (e.g. exchanging data input and processing results, providing computing power to call external models, and obtaining external reasoning results) to assist corresponding real time digital twin services.
Figure 11.4.3-2: Real-time Digital Twins Example Scenario with Limited Computing Power and Specific QoS Requirements for Some Terminals.
11.4.4 Post-conditions
The RT DT is being used both by the Research, IT and the OT team of the industrial plant for multiple purposes to save costs and smoothen operations, as well as plan the evolution of the plant.
11.4.5 Existing features partly or fully covering the use case functionality
In 5G, TS 22.261 [14] has documented use cases around factory automation in Annex D, and specific requirements for low latency and high reliability in clauses 6.28 and 7.2. TS 22.104 [64] also specifies requirements and KPIs for cyber-physical control applications in vertical domains.
Access to digital twins via mobile metaverse services is also documented in TS 22.156 [28].
Requirements related to wireless sensing capability have been documented in TS 22.137 [6]. In particular, requirements exist to provide a wireless sensing service in a target geographical area requested by a trusted 3rd party. Such area could be defined to match with the geographic area covering the physical system being represented by the Digital Twin.
In 5G, NWDAF provides network performance analytics for an area of interest and observed service experience related to a (group of) UE as per TS 23.288 [114].
11.4.6 Potential New Requirements needed to support the use case
[PR 11.4.6-1] The 6G system should be able to enable a Real Time Digital Twin service with following KPIs.
Table 11.4.6-1: Performance requirements for Real Time Digital Twins
Use Cases
Characteristic parameter (KPI)
Influence quantity
Remarks
Max allowed end-to-end latency (ms)
Service bit rate: user-experienced data rate (Mb/s)
Reliability (%)
Service Area
Location accuracy (cm)
Connection density
[devices / m2]
UE Speed
Real-Time Digital Twin
(note 1)
[1-10]
(note 2)
[<100] (UL) (note 3)
[99.999-99.99999]
(note 4)
[up to 1km2]
(note 5)
[≤ 10]
[1-10]
Up to vehicular speed
Communication between physical system and the network (note 6)
NOTE 1: Wireless-sensing capability is expected to contribute to enrich the Digital Twin model, with KPIs related to e.g. location accuracy, resolution, range and latency
NOTE 2: Very low latency for the Real-time aspect, both in UL (to report up-to-date data) and DL (to send timely controls)
NOTE 3: It is expected uplink video will be most demanding.
NOTE 4: Very high: The control of Real-time industrial processes requires very high service reliability. Can be lower for non-real time DT or in other contexts (e.g. training, engineering, planning).
NOTE 5: Service coverage both outdoor & indoor (e.g. industrial plant, city area) [31]
NOTE 6: Performance requirements related to the communication between the DT and the end-user device accessing the DT (including network to UE communication) are not included in this table and may depend on the actual application (e.g. web, immersive/XR etc) used to access the DT
Editor’s Note: the KPIs related to sensing capabilities are FFS.
[PR 11.4.6-2] The 6G network with AI capabilities should be able to collaborate with the AI capabilities in the authorized third-party (e.g. split inference) to assist Real Time Digital Twin in meeting the required service performance.
[PR 11.4.6-3] The 6G network should be able to provide multiple types of data, such as network status data, sensing data[[SUGGESTION_START]],[[SUGGESTION_END]] etc., to enable Real Time Digital Twin.
11.5 Immersive media services for advanced air mobility (AAM) enabled by 6G NTN
11.5.1 Description
In an Advanced Air Mobility (AAM, also referred to as UAM) setting, passengers on AAM vehicles (e.g. air taxis, drones) can access immersive media services such as (ultra-) high-definition live-streaming, real time news, and interactive 3D content [115-121]. Leveraging a hybrid network solution that combines 6G cellular (i.e. terrestrial network and non-terrestrial network connectivity (e.g. satellite), passengers experience uninterrupted high-quality media services with immersive content tailored to passenger preferences, regardless of altitude or coverage gaps. This hybrid connectivity ensures consistent quality even when AAM vehicles transition through urban corridors or low-coverage areas.
To ensure uninterrupted immersive media services on AAM flights, a hybrid network infrastructure with 6G TN and NTN provides overlapping coverage with seamless handovers. AAM vehicles are equipped with devices compatible with both 6G and satellite signals, allowing “non-conventional” continuous connectivity (i.e. seamless and ultra-reliable against potential interruption during mobility). Immersive media content is delivered efficiently through edge computing, while passengers authenticate and receive personalized content recommendations based on connectivity and their preferences.
11.5.2 Pre-conditions
The following conditions are considered necessary to support the aforementioned immersive media service with respect to network infrastructure, UE (or AAM as a communication entity), data availability/sharing for edge computing, and security (such as authorised use of data and/or information).
UE Compatibility (of AAM vehicle): AAM vehicles are equipped with hybrid 6G-NTN compatible onboard devices (in the form or UE or base station) and relevant transmit/receive capabilities optimized to transmit/receive both terrestrial and satellite signals.
Typical Cruise Speeds of AAM vehicles: Most AAM vehicles (including eVTOL aircraft) are designed to operate within the cruise speed ranges between 100-200 mph (or 160-320 km/h).
Network Infrastructure: Radio network of 6G system and 6G NTN satellites provide overlapping coverage, with dynamic handover capabilities that support seamless switching between 6G TN and NTN, ensuring continuous immersive media service during flight.
NOTE 1: An LEO setting is assumed but other constellations (such as GEO) are also applicable.
Data Availability and Edge Computing: immersive media content providers are connected to both terrestrial and satellite edge computing infrastructure, enabling efficient, latency-sensitive access to media content.
NOTE 2: The service scenario can employ different types of edge computing: for the immersive media streaming (as opposed to interactive media communications), “edge server” can be located on the ground or in the AAM, whichever is more relevant; for supporting advanced communication features, “edge server” can be located in the NTN, minimizing the propagation delay to/from the “onboard system” for the AAM vehicle.
Security / Authentication: Passengers share their profiles or preference with the immersive media service provider, allowing the system to adjust and optimize media recommendations based on connectivity availability and passenger preferences.
NOTE 3: Passengers can be authenticated for media services during pre-flight check-in or during flights. The immersive media service provider can be the air carrier or an authorized third party related to the AAM service. However, detailed subscription models are not the primary focus.
11.5.3 Service Flows
In order to clarify the intended time window where the immersive media service will be provided for passengers during AAM services, two steps (i.e. preparation step and completion step for AAM operation) are included in the service flow.
Figure 11.5.3-1: An AAM vehicle has an “onboard system” (e.g. that can act as a Mobile Base Station Relay or UE Relay) that can connect to NTN and/or TN and provide an extension of the backhaul connectivity to its passenger UEs
1. Pre-Flight Setup:
• During flight check-in, passengers connect their devices to the AAM’s “onboard system” for media service access and select from various content options.
• The service is enabled to tailor media options for each passenger based on preferences, dynamically adjusting recommendations based on anticipated network coverage along the flight path.
NOTE 1: The term “onboard system” mounted on an AAM vehicle is a 3GPP entity that can provide connectivity to one or more UEs, e.g. acting as a Mobile Base Station Relay or UE Relay (as in Figure 11.5.3-1).
2. Initiation of Immersive Media Service:
• Upon (vertical) takeoff, the “onboard system” mounted on AAM (e.g. Mobile Base Station Relay or UE Relay customized to AAM) initiates HD streaming for its passenger devices (i.e. UEs) via the 6G cellular network if terrestrial coverage is available for that onboard system.
• The onboard system keeps checking the availability of NTN connectivity and compare the relevance of choice between TN and NTN connectivity, getting ready for a seamless HO if cellular coverage becomes limited and/or if other applicable criteria, defined by the mobile (satellite) network operator or by a service agreement, are satisfied.
• The 6G system has historical data regarding communication disruptions along the planned travel routes from its trusted third party.
• While the communication is being established for Immersive Media Service, (a) the media service provider indicates to 6G system that the service will require a high-volume of traffic so that the 6G system can be aware of the network status in order to prepare necessary setup and (b) the user / passenger is prompted to choose “Proceed” or “Stop” if it is expected that communication disruptions are expected to some extent above certain tolerance level.
• If the user still wants to “Proceed”, the 6G system notifies to the media service provider that the user still wants to receive the media service and that it would be necessary to dynamically adjust the communication parameter in order to maintain the communication quality to some extent.
NOTE 2: The US Federal Aviation Administration (FAA) and other aviation authorities mandate so-called “airplane mode” rules for safety reasons. However, details on how such rules affect the service scenario is FFS and is not the primary focus in this use case.
3. Media Streaming with Hybrid Connectivity:
• As the AAM vehicle travels, the “onboard system” seamlessly maintain the connectivity to meet the traffic demand incurred by the passengers’ devices.
• When approaching low-coverage areas or higher altitudes, the system shifts to NTN to maintain continuity.
• The network dynamically adjusts data flow between 6G and NTN networks as needed to optimize bandwidth and minimize interruptions.
• For interactive content (e.g. VR), low-latency satellite links help maintain responsiveness, supporting immersive passenger experiences.
4. Ultra-fine Handover during Flight:
• The “onboard system” synchronizes data between edge servers and satellite networks, reducing latency when switching networks, ensuring uninterrupted streaming, and minimizing buffering during handovers.
• The “onboard system” is able to maintain ultra-fine synchronization using a variety of novel methods with both so-called the source base station and the target base station even when using LEO satellite(s): e.g. when switching from an LEO to another, from an LEO to TN, or from TN to an LEO.
5. End of Service Inherently Determined by the AAM Transportation Service Completion:
• As the AAM vehicle approaches its destination, the hybrid system transitions back to a 6G terrestrial connection, preparing passengers for network continuity after disembarkation. Passengers receive prompts to save content or queue downloads before arrival.
NOTE 3: Details about the service or business model for ending the immersive media service upon completion of the AAM transportation service are not the primary focus of this use case.
11.5.4 Post-conditions
• The “onboard system” maintained ultra-fine synchronization with the source base station and target base station, which helped the AMR (UE) complete HO in a very reliable and fast way with a minimal disruption for the immersive medial service/applications.
• Passengers enjoyed immersive media service from the “onboard system” connectivity to personal network connections upon disembarkation, allowing content access continuity if desired.
11.5.5 Existing features partly or fully covering the use case functionality
Some normative requirements related to support of NTN connectivity in TS 22.261 [14]:
(a) In clause 6.46.3 (Service continuity), for a 5G system with satellite access, the following requirements apply:
- A 5G system with satellite access shall support service continuity between 5G terrestrial access network and 5G satellite access networks owned by the same operator or owned by different operators having an agreement.
- Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall support service continuity (with minimum service interruption) for a UE engaged in an active communication, when the UE changes from a direct network connection via 5G terrestrial access to an indirect network connection via a relay UE (using satellite access) and vice-versa.
NOTE 1: It is assumed that the 5G terrestrial access network and the satellite access network belong to the same operator.
- Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall be able to support service continuity (with minimum service interruption) of a UE-Satellite-UE communication when the UE communication path moves between serving satellites (due to the movement of the UE and/or the satellites).
- Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall support service continuity (with minimum service interruption) of a UE-Satellite-UE communication when the communication path between UEs extends to additional satellites (through ISLs).
In clause 6.46.7 (Satellite and Relay UEs), for a 5G system with satellite access, the following requirements apply:
(b) For a 5G system with satellite access, the following requirements apply:
- A 5G system with satellite access shall be able to support relay UEs with satellite access.
NOTE 2: The connection between a relay UE and a remote UE is the same regardless of whether the relay UE is using satellite access or not.
- A 5G system with satellite access shall support mobility management of relay UEs and the remote UEs connected to the relay UE between a 5G satellite access network and a 5G terrestrial network, and between 5G satellite access networks.
- A 5G system with satellite access shall support joint roaming between different 5G networks of a relay UE and the remote UEs connected to that relay UE.
(c) KPI related to satellite access in TS 22.261 [14]:
Table 11.5.5-1: Performance requirements for satellite access (Table 7.4.2-1 from [14])
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
(note 1)
Area traffic capacity
(UL)
(note 1)
Overall user density
Activity factor
UE speed
UE type
Pedestrian
(note 2)
[1] Mbit/s
[100] kbit/s
1,5 Mbit/s/km2
150 kbit/s/km2
[100] / km2
[1,5] %
Pedestrian
Handheld
Public safety
[3,5] Mbit/s
[3,5] Mbit/s
TBD
TBD
TBD
N/A
100 km/h
Handheld
Vehicular connectivity
(note 3)
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
50 %
Up to 250 km/h
Vehicle mounted
Airplanes connectivity
(note 4)
360 Mbit/s/ plane
180 Mbit/s/ plane
TBD
TBD
TBD
N/A
Up to 1000 km/h
Airplane mounted
Stationary
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
N/A
Stationary
Building mounted
Video surveillance
(note 4a)
[0,5] Mbit/s
[3] Mbit/s
TBD
TBD
TBD
N/A
Up to 120km/h or
stationary
(note 4b)
Vehicle mounted or fixed installation
Narrowband IoT connectivity
[2] kbit/s
[10] kbit/s
8 kbit/s/km2
40 kbit/s/km2
[400]/km2
[1] %
[Up to 100 km/h]
IoT
NOTE 1: Area capacity is averaged over a satellite beam.
NOTE 2: Data rates based on Extreme long-range coverage target values in clause 6.17.2. User density based on rural area in Table 7.1-1.
NOTE 3: Based on Table 7.1-1
NOTE 4: Based on an assumption of 120 users per plane 15/7.5 Mbit/s data rate and 20 % activity factor per user
NOTE 4a: Refer to video surveillance data transmitted (in UL) from a UE on the ground (e.g. picture or video from a camera) using satellite NG-RAN to connect to 5GC, and video surveillance-related configuration or control data sent (in DL) to the UE/device. 0.5 Mbit/s for DL experienced data rate is based on MAVLINK protocol that is widely used for UAV control. 3 Mbit/s for UL experienced data rate is based on the assumed sum from 2.5 Mbit/s for video streaming and 0.5 Mbit/s for data transmission.
NOTE 4b: Up to 120km/h applies to vehicle mounted while stationary applies to fixed installation.
NOTE 5: All the values in this table are targeted values and not strict requirements.
NOTE 6: Performance requirements for all the values in this table should be analysed independently for each scenario.
Some normative requirements related to support of media service are in TS 22.263 [67].
11.5.6 Potential New Requirements needed to support the use case
[PR 11.5.6-1] 6G system with satellite access shall provide a means to support immersive media services with the following KPIs:
Table 11.5.6-1: Performance requirements for immersive media service via satellite access
Scenario
Service interruption time (ms)
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
Area traffic capacity
(UL)
Overall user density
Activity factor
UE speed
UE type
Immersive media service via AAM/UAM
(direction: from network to user, uncompressed)
[10ms] for 60 fps,
[20ms] for 30 fps
(note 1)
[1.3] Gbits/s
(note 2)
N/A
TBD
N/A
[4]/km2
N/A
Up to [300] km/h
UAM mounted
Immersive media service via AAM/UAM
(direction: from network to user, compressed)
[10ms] for 60 fps,
[20ms] for 30 fps
(note 1)
[25] Mbits/s
(note 3)
N/A
TBD
N/A
[4]/km2
N/A
Up to [300] km/h
UAM mounted
NOTE 1: It is assumed that the interruption time is less than a single frame (16ms for 60 fps, 32 ms for 30 fps) plus a margin of an order of millisecond (e.g. for other processing time).
NOTE 2: Uncompressed (0.8 M points per frame, total 300 frames are considered: i.e. 43.2 Mbits/s, 30 fps)
NOTE 3: Compressed (Video-based Point Cloud Compression (V-PCC), bpip (bits per input points) total 1.14 (Color 0.9, Geometry 0,11, Occupancy 0.13) are considered: 25 Mbits/s)
NOTE 4: It is assumed that the number of passengers using immersive media service per UAM is up to 2.
[PR 11.5.6-2] 6G system with satellite access shall provide a means to obtain and expose application service interruption information (e.g. expected interruptions to the service) to authorised third party to minimize interruption for immersive media services.
11.6 Use cases on high-rate aircraft communication services in 6G
11.6.1 Description
The use cases described in clauses 5.20 and 5.22 of TR 22.887 [122] set out a 5G-advanced aviation use case, which is focused on delivering the capability to provide passenger access to internet services. In the advent of 6G, higher rates of such services are anticipated [123], [124] and the broader adoption of massive data transfer on the ground or near ground [125]. Therefore, the performance parameters of the use case in TS 22.261 [14] are to be updated.
This use case proposal is based on the work in the CELTIC-NEXT research project 6G-SKY [354].
11.6.2 Pre-conditions
The pre-conditions are as described in clause 5.22 of [122].
Additionally, massive data transfer on the ground or near the ground (i.e. during aircraft approach, taxing, parking, take-off) is realized.
11.6.3 Service Flows
1. Aircraft gathered data during flights for predictive maintenance but cannot store more data than for one flight.
2. Aircraft starts (at t1) to offload data from its local storage during approach or after touchdown with the help of a TN at high rates.
3. Aircraft prepares for next take off and eventually stops the TN linkage (at t2).
4. Before t2 the gathered data of the flight is offloaded to a ground data storage system.
5. An analogous process follows in the case of data transfer from ground to the aircraft, e.g. for in-flight entertainment data.
11.6.4 Post-conditions
The post-conditions are as described in clause 5.22 of TR 22.887 [122].
Additionally, in massive data transfer on the ground or near the ground, the complete data of the previous flight is offloaded to ground. In the reverse direction, the complete data before the next flight is transferred to the aircraft.
11.6.5 Existing features partly or fully covering the use case functionality
See clause 5.22 of TR 22.887 [122] and TS 22.261 [14].
11.6.6 Potential New Requirements needed to support the use case
[PR 11.6.6-1] The 6G System shall support high-data rate aircraft communications using satellite access with the following KPI requirements.
Table 11.6.6-1: Performance requirements for satellite access
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
Area traffic capacity
(UL)
Overall user density
Activity factor
UE speed
UE type
Airplanes connectivity
(note)
3 Gbit/s
per plane
1.5 Gbit/s
per plane
N/A
N/A
N/A
40%
Up to 1500 km/h
Airplane mounted
NOTE:
Required experienced peak data rate corresponding to the aggregated passenger traffic at aircraft level
Based on an assumption of 450 seats, average take rate of 75% (free model) and load factor of 85%
Assumption of 2:1 Downlink / Uplink ratio, anticipating future usages
The Downlink and Uplink throughput can be achieved using one or multiple satellite links
[PR 11.6.6-2] The 6G System shall support high data rate aircraft communications for on-board UEs with the following KPI requirements.
Table 11.6.6-2: Performance requirements for on-board UEs
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
Area traffic capacity
(UL)
Overall user density
Activity factor
UE speed
UE type
Airplanes connectivity
50 Mbit/s
25 Mbit/s
6 Gbit/s/plane
3 Gbit/s/plane
400/plane
30 %
Users in airplanes (up to 1500 km/h)
On-board UE
(note)
NOTE: These KPIs are related to UEs inside the aircraft where the UE can be connected to on-board base station. These KPIs enable the case of XR UEs for applications in highly dynamic environments, where UEs offload processing to an edge-cloud.
[PR 11.6.6-3] The 6G System shall support high data-rate aircraft communications with massive data transfer when the aircraft is on the ground or near the ground with the following KPI requirements.
Table 11.6.6-3: Performance requirements for airport scenario (massive data transfer on/near ground)
Scenario
Experienced data rate (DL)
(note)
Experienced data rate (UL)
(note)
Area traffic capacity
(DL)
Area traffic capacty
(UL)
Overall user density
Activity factor
UE speed
UE type
Airplanes connectivity in airport vicinity (within 20 km airport radius, up to 1500 m)
11 Gbit/s/ plane
22 Gbit/s/ plane
TBD
TBD
TBD
N/A
Up to 350km/h
Airplane mounted
NOTE: These KPIs are related to massive data transfer including operational data when the aircraft is on the ground or near the ground. The need is to send 10 TByte from aircraft to the ground (aircraft 1 hour in 20 km radius → 22 Gbit/s) [125].
11.7 Use case on assisted airspace management of UAV and UAM aircrafts
11.7.1 Description
Over the past decades, UAVs have been widely applied in many industries and are expected to be applied into the fields related to people's daily lives, such as last-mile delivery and urban governance. Meanwhile, the passenger-carrying aerial vehicle (e.g. autonomous eVTOL) is rapidly developing and expected to become a critical part of UAM in the near future.
The complexity of future low-attitude aerial transportation (including UAV, UAM aircraft) has brought new challenges to the regulatory authorities. For instance, the autonomy of UAVs and UAM aircrafts increase the risk of loss of control, illegal flights and the collision with other aircrafts, which could potentially threaten the aerial and public safety, and thus request effective airspace and traffic management. Moreover, the technical failure, cybersecurity concerns and the need to carry passengers impose higher demands on the security and reliability of managing these aircrafts.
To address these challenges, many countries have enacted regulations and established individual management platforms to facilitate the supervision. For example, the FAA has issued a series of acts (e.g. “Safe Drone Act of 2017”, “FAA Reauthorization Act of 2018”) and developed UTM framework to improve the regulations and airspace integration [126]. The European Union Aviation Safety Agency) has issued the UAS regulation (2019/947) and provided U-Space for safety supervision of drones and eVTOL [127]. The Civil Aviation Administration of China has issued “Civil Aviation Law” and “Interim regulations on the UAV management” including the introduction of UAS Management platform for dynamic supervision and monitoring [128]. Besides, the Japan’s Flight Information Management System can manage the flight plans, handle the alarms and send commands [129]. Correspondingly, the UAVs and UAM aircrafts are requested to report the flight data to the remote pilot platform on demand and also keep the flight data locally or in cloud for backtracking when needed (e.g. recording the access to certain areas). However, airspace management still faces issues like
• Incompatible rules: the cross-border flight requires standard rules to resolve the conflict of the regulations of different regions or nations,
• Insufficient compliant check: there are limited resources to monitor rogue operators, illegal flights, and out-of-control UAVs /UAM aircrafts that do not comply with the regulations,
• High cost and security risk of retrieving the flight data: it’s mandatory for the aircraft or its operator to store and report the flight data on demand. However, when an aircraft crashes or is unexpectedly lost, it can be difficult or costly to retrieve the operation data from the moment before the accident. Additionally, the stored data may be tampered with and untrusted.
Therefore, it’s expected that more technical tools will be used to solve the above issues and improve the management efficiency of UAV/UAM aircraft.
The 5G network has already supported some UAV-specific services such as remote identification, monitoring the location and status information, reporting network conditions and QoS along a flight path at specific times as illustrated in TS 22.125 [35]. The use cases about UAV intrusion detection and flight trajectory tracing using 3GPP sensing service were discussed in TR 22.837 [9] and the associated capabilities of 3GPP sensing and the result reporting (e.g. location, relevant distance between UAVs and other objects) to UTM and UAVs were introduced to TS 22.137 [6] and TS 22.125 [35]. The 6G system, besides the above services, has big potential to offer more assisted services for the airspace management of UAVs and UAM aircrafts under the coordination of the remote pilot platform as Figure 11.7.1-1 shows. For example, logging the flight data acquired from 3GPP services (e.g. positioning service, sensing service) on demand will help to ensure the data integrity in case that the local data is damaged. Moreover, logging the service intermediate data can support additional data mining when needed e.g. for the issue analysis.
Figure 11.7.1-1: Assisted Airspace Management of UAVs and UAM Aircrafts
11.7.2 Pre-conditions
The 6G network operator “FlyNet” has the agreement with UAV/UAM operator “AirExpress” to provide a series of flight assistant services for its aircrafts, such as:
• Full coverage of communication services along the planned routes of the aircrafts,
• Sensing services for flight trajectory tracing, collision prediction, etc.,
• High-accuracy positioning services,
• Edge computing services and storage service.
FlyNet’s services comply with regulations and authorized by AirExpress for data access to the regulatory authority.
AirExpress uses UTM to manage its operated aircrafts and provide remote piloting.
11.7.3 Service Flows
1. A UAV/UAM aircraft connects to UTM via the 6G network and exchange the data about the flight tasks.
2. UTM requests the 6G network to provide sensing service and positioning service to the authorized aircrafts for a certain period of time and in specific areas for the flight trajectory planning and tracing.
3. The 6G core network collects the sensing data and positioning measurement data form the UEs/the base stations in the specific areas, processes the data into sensing results and the location information, and then transmits the processing results to the aircraft for route planning as well as exposes them to UTM for tracing. In parallel, the 6G network can store the service intermediate data (e.g. sensing data, positioning measurement data) and processing result (e.g. sensing results, and location information) in the network for a period as requested by UTM and/or by the regulatory authority.
NOTE: The term “6G core network” does not imply any architectural assumption, e.g. whether 6G core network is a new or evolved core network (compared to 5G).
4. Suddenly, the aircraft loses the connection with UTM and “disappears”. Later, UTM identifies that the aircraft has collided into the power grid infrastructure and crashed into pieces.
5. AirExpress requests the 6G network to process the sensing data within the accident period based on the new request (e.g. taking the power grid, environment as the sensing target, based on new trajectory planning model), in order to investigate the root cause of the collision. Also, the regulatory authority retrieves the flight information (e.g. allowed service intermediate data, processing result) and operation data of the aircraft from 6G network and UTM separately to evaluate the responsibilities of the accident.
11.7.4 Post-conditions
Thanks to the in-network data logging service, AirExpress identifies the root cause of the collision (e.g. a fault of the prediction algorithms or operational decision). The regulatory authority justifies the responsibility of the accident (e.g. the aircraft exceeds the speed limit).
11.7.5 Existing features partly or fully covering the use case functionality
TS 22.137 [6] and TS 22.261 [14] have defined the functional and performance requirements for 3GPP sensing services and positioning services. And the information exposure of sensing results and positioning information is included.
TS 22.125 [35] has defined the requirements for remote identification of UAS, UAV traffic and network exposure for UAV services.
11.7.6 Potential New Requirements needed to support the use case
[PR 11.7.6-1] Subject to regulatory requirements and user consent, the 6G network shall be able to securely store the service data for a UAV or a UAM aircraft based on the request information (e.g. service type, storage duration, time expiry).
NOTE: The service data may be the processing data for a specific network service such as sensing data, positioning measurement data, or the exposed data such as sensing results, positioning information.
[PR 11.7.6-2] Subject to regulatory requirements and user consent, the 6G network shall provide secure means to expose to an authorized 3rd party application the information related to a UAV or a UAM aircraft from the stored service data.
11.8 Use case on 3D factory model based AR guided task
11.8.1 Description
5G empowering the digital transformation of factories, in the whole process of R&D and design, manufacturing, testing and monitoring, warehousing and logistics, and operation and management, the comprehensive coverage of “5G+Industrial Internet” scenarios are now being developed. Some statistic of typical smart factory, the total output value of the factory is increased by 41%, the per capita output value has increased by 81%, the delivery cycle has been shortened from 20 days to 14 days, the carbon emission has been reduced by 29%, and the energy consumption of a single product has been reduced by 19%. These results demonstrate the potential of 5G technology in improving productivity, reducing operational costs and promoting green development. Based on 5G, 6G which not only with higher performance on communication but also AI, sensing and computing capabilities, will empower factory with more intelligent future.
In a 6G factory scenario, AR type of devices could be used for guiding tasks and remote assistance which will be a key component of smart manufacturing. Workers and engineers onsite could receive real time operation step-by-step guidance, equipment maintenance instructions, operation process monitoring and other information through AR glasses or HMD devices . In this process, it not only intuitively superimposes digital information on the real world, but also supports remote experts to provide real time guidance and assistance to help workers complete complex operation tasks, e.g. equipment maintenance and troubleshooting, assembly line task guidance, remote assistance and training, etc.
It needs to handle large amounts of video and sensor data, especially when it comes to real time video streams overlaid with 3D factory digital twin models. Remote experts not only need a high-definition view of the worker (perhaps 4K or 8K video) but also need to receive other sensor data (e.g. temperature, pressure, equipment status, etc.) in real time and then provide feedback in real time to assure efficient remote operation. At the same time, the worker's AR device needs to obtain high-definition 3D factory models and operational steps from the remote experts, which require network to support large-scale concurrent data transmission.
The ultra-low-latency, high-bandwidth, and reliability features of the 6G system will enable such AR applications in factory to work more efficiently and accurately. At the same time, the messages exchanged between the AR type devices and remote assistance need to be deterministic to enhance factory productivity and operational safety that means the information needs to arrive within a defined time window which requires low communication latency.
Typically, the data rate to deliver video streams, could be calculated by the following:
Downlink user experienced data rate:
An uncompressed 4K video stream requires about 12 Gbits of data (4K resolution (3840×2160), 60 fps, 8-bit color depth, RGB format (no sampling compression)) to be transmitted per second. However, efficient video compression technologies such as H.265 or High Efficiency Video Coding (HEVC) can significantly reduce the amount of data. Assuming a high quality lossless compression ratio of 25 times up to 50 times based on H.265 (HEVC) or AV1, the compressed data rate for 4K AR video stream is about 12 Gbps/50 = 240 Mbps to 12 Gbps / 25 = 480 Mbps. If it is 8K or 12K, the data rate will be increased.
In addition to the video data itself, AR applications also need to transmit other types of data, such as control signals, and interaction information. Although these data are relatively small, they also require a certain amount of bandwidth to ensure low-latency and higher reliability transmission. So, it is reasonable to request 120 Mbps to 240 Mbps for downlink user experienced data rate.
Uplink user experienced data rate:
The calculation of the uplink data rate needs to comprehensively consider the various data types generated by AR glasses or devices during real time interaction, including video streams, audio streams, sensor data (such as position, gestures, depth), environmental data, etc. Based on [24], for AR the typical steaming video and audio bitrates for real time 4K Ultra HD is from 5 Mbps to 25 Mbps with 4K resolution (3840 × 2160), audio (Stereo, 48 kHz, AAC encoding) needs 64 kpbs~128 kbps. The amount of sensors (including position, posture, gesture, High-resolution depth sensor, etc.) data depends on the data frequency and resolution which in general are 25 Mbps. In summary, the Uplink data rate is about 50 Mbps. If it is 8K or 12K, the data rate will be increased more.
Regarding to the reliability and latency, TS 22.261 [14], it has specified 99.999% for industry control. So, for 6G, it is suggested to also consider 99.999% for similar scenarios.
As to the jitter, according to the practice of typical smart factory, in general, the jitter is 10% of communication latency.
Beyond it, the AR system involves a large number of graphic rendering and 3D factory model construction and update, these tasks need to be performed on time for these tasks even for multiple AR devices cases.
In AR guidance tasks, virtual objects (e.g. 3D models of equipment, operating tips, etc.) must be accurately superimposed on real objects, which is critical for workers’ operation. Furthermore, it is normal that multiple remote AR guided tasks are performed at the same time. If the AR system is not accurately localized, the positional offset of the virtual information can lead to the worker not being able to complete the task correctly, and may even lead to operation errors. Therefore, 6G systems need to provide high-precision spatial positioning and synchronization capabilities for the multiple AR guided tasks simultaneously in the factory area.
11.8.2 Pre-conditions
Operator TT provides “Immersive Smart Factory” service through its 6G network for factory AR applications.
Manufactory MM has subscribed the “Immersive Smart Factory” service from Operator TT to operate its equipment maintenance task.
11.8.3 Service Flows
1. Workers receive the notification that the equipment needs to be maintained through the AR glasses they wear, and the location of the equipment, the 3D model of the equipment, the internal structure diagram and the maintenance steps are received through 6G network and displayed on related workers’ field of view.
2. Some of the factory equipment are in the remote wilderness, others are indoors.
3. Through sensors (e.g. High-resolution camera, 3D scanner, depth sensor, temperature/humidity/pressure sensors etc.) in the equipment, the latest status data of the equipment (e.g. sensor data, temperature, pressure, etc.) are collected via the 6G network and processed by servers deployed in Operator TT’s data network. The processed results are displayed overlaid on the worker's field of view. During the procedure, the status data from different sensors are collected synchronously to ensure that they are processed together.
4. Worker Smith, who is in charge of equipment in the remote wilderness, encounters a complex problem, he initiates a request for remote assistance through the AR glasses via 6G network. The remote expert accesses the worker's field of view in real time and understands the worker's operating environment through HD real time video streaming delivered by 6G network.
5. With the ultra-low latency, low jitter, high data rate, and reliability and high accuracy positioning capabilities of 6G, the remote expert can immediately see the equipment in front of the worker and add accurately superimposed instruction markers, annotations or directly overlay 3D factory models in the AR system to guide the worker to the next operation.
6. As the worker performs the operation, the AR content is dynamically updated based on real time sensor data collection, the real time positioning data and the progress of the operation. Each step and instruction markers are accurately displayed in the worker's field of view, ensuring that the worker follows standard procedures. The 6G network ensures real time (e.g. microsecond level) loading and updating of AR content, allowing workers to receive seamless operational guidance during execution.
7. After the task is completed, the worker submits a maintenance report, and all operation steps and sensor data are automatically synchronized to the factory's central system for recording and analysis. The high reliability of the 6G network guarantees complete data delivery and ensures that the factory system can be further optimized and prevented from failure based on the data collected this time.
11.8.4 Post-conditions
The equipment maintenance task is successfully completed.
11.8.5 Existing features partly or fully covering the use case functionality
The typical synchronisation thresholds of multi-modality is listed in [14] clause 6.43.1.
The AR/VR performance requirement for gaming has been specified in [14] Table 7.6.1-1 clause 7.6. Considering the AR applied to factory control, the more strict reliability requirement is new compared with the existing one.
The basic positioning KPI is specified in [14] Table 7.3.2.2-1 which has not considered how many UE could be positioned with the required positioning performance simultaneously in the service area.
In [64], one use case is listed which the periodic telemetry data and video images are used from the digital twin in the production system for analysis and then the processed outcome is sent back to the system for any adjustment of the machine components. The periodic communication requirements are specified in Table A.2.3.4, which is very different with the aperiodic deterministic communication in the factory required by this use case.
In [28] Table 6.2-1, the KPI for Metaverse-based Tele-Operated Driving was specified while it is for periodic communication either. The aperiodic deterministic communication for this new use case is needed.
11.8.6 Potential New Requirements needed to support the use case
[PR 11.8.6-1] Subject to operator policy, agreement with the 3rd party and user’s consent, the 6G network shall support mechanisms to process the data collected from 3GPP UEs (e.g. AR split-rendering), in the Service Hosting Environment.
[PR 11.8.6-2] Subject to operator’s policy, regulatory requirements, agreement with the 3rd party, and user consent, the 6G system shall support to collect data from multiple 3GPP UEs (e.g. XR device, High-resolution camera, 3D scanner, depth sensors etc.) which belong to one 3rd party, within an given timeframe, and associate the collected data with one application of the 3rd party.
[PR 11.8.6-3] Subject to operator’s policy, agreement with the 3rd party, the 6G network shall be able to expose and update data processing result to the 3rd party.
[PR 11.8.6-4] Subject to operator’s policy and agreement with the 3rd party, the 6G system shall support positioning performance requirements in Table 11.8.6-1.
Table 11.8.6-1: Positioning performance requirements for AR remote guided task
Positioning scenario
Accuracy
(95 % confidence level)
(note 1)
Positioning service availability
(note 2)
Positioning service latency
(note 3)
# Positioning UE
(note 4)
Positioning service area
(note 5)
Horizontal Accuracy
Vertical Accuracy
(note 1)
Indoor factory
0,2-1m
1-3 m
99.9 %
15ms
≥200
200,000m2
outdoor
1-3m
3-5m
99.9%
15ms
TBD
TBD
NOTE 1: The positioning accuracies are based on the UEs on boarded on the AR devices and factory equipment which are used to operate AR remote guided tasks. They are almost same as the values in level 5 in TS 22.261 [14] Table 7.3.2.2-1.
NOTE 2: The availability reuses the values in level 4 in TS 22.261 [14] Table 7.3.2.2-1.
NOTE 3: It reuses the values in level 4 in TS 22.261 [14] Table 7.3.2.2-1.
NOTE 4: The number of positioning UE is the number of UEs which the 6G system is asked to position simultaneously in the positioning service area with the required positioning performance
NOTE 5: The 6G positioning service area is based on the typical single industry factory size.
[PR 11.8.6-5] The 6G system shall support communication performance requirements in Table 11.8.6-2.
Table 11.8.6-2: Performance requirements for simultaneous high data rate, lower latency and higher reliability in factory
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
Area traffic capacity
(UL)
Network transmission Latency
Reliability
Overall UE density
Activity factor
UE speed
Coverage
AR guided task
(note 3)
240-500 M
bit/s
50 M
bit/s
[50-100] Gbit/s/km2
[10] G
bit/s/km2
1 to 5ms
(note 4)
Up to 99.999%
[200 00]/km2
(note 2)
(note 1)
Pedestrians
Factory
NOTE 1: A certain traffic mix is assumed; only some users use services that require the highest data rates.
NOTE 2: Not all UEs are consuming the same 3GPP services at the same time.
NOTE 3: All the values in this table are targeted values and not strict requirements. The DL data rate is assumed 4K video flow + control information + audio data with data compression ratio of 1:25 to 1:50; and UL data rate is assumed video flow 25 Mbps [24] + high resolution depth sensor data 25 Mbps + audio data.
NOTE 4: It is for downlink or uplink one way delay.
11.9 Use case on collaborative awareness in dynamic environments - enhancing mutual decision-making through real time data sharing
11.9.1 Description
In a high-demand warehouse setting, a fleet of AMRs operates collectively to handle complex tasks such as inventory retrieval, sorting, and material transport [130], [131], [132]. These robots rely on collaborative decision-making enabled by real time data exchange and processing to optimize efficiency, avoid obstacles, and manage potential conflicts in task priority or navigation. Each robot is equipped with sensors (e.g. LiDAR, cameras) and onboard processing power to sense its surroundings and determine its actions. However, the efficiency of the fleet depends on the robots’ ability to share data and make decisions collectively, with guidance from a central fleet management system that coordinates their individual tasks based on warehouse conditions, task priorities, and the real time location of each robot. Recently, the expected benefits of “data and AI sharing” was addressed with the focus on an interesting concept of “Delta Sharing”, which is supposedly an open standard for secure sharing of data and AI assets in digital economy [133]. Along with philosophy laid behind the concept, it would be a great benefit for the telecom industry verticals to consider diverse applications of the concept of “sharing of data and AI (or AI assets)” among a group of autonomous robots [134], [135], [136].
11.9.2 Pre-conditions
1. Key - The warehouse is equipped with a dedicated network that enables low-latency data sharing among robots and between the robots and the edge servers. See Figure 11.9.2-1 for the network coverage plans. Each NPN has one or more edge servers sufficient to support the users (UE’s) dwelling in each coverage areas at any given time. Zone 1 and Zone 2 are places where each site is shared by AMRs and human workers, respectively. Zone 3 is shared by different types of AMRs but not by human workers. Zone 1 is served by NPN1 whereas Zone 2 and Zone 3 are served by NPN2. When two or more AMRs served by one or two different NPNs need to use the shared road, they share collaborative awareness data to mutually help each other in that shared area.
2. Each AMR (UE) has multi-modal sensors, such as 3GPP sensing modules, iIDAR, cameras, and ultrasonic sensors, for obstacle detection and environment mapping. They also have antennas compatible with the warehouse’s high-speed, low-latency wireless communication network.
3. The edge servers process large volumes of data and facilitate real time decision-making for collaboration among multiple AMRs.
4. Key - There is no central coordination system; instead, the edge servers put necessary sub-tasks in the common bulletin boards so that different participating AMRs can collaboratively volunteer to take some of those posted sub-tasks.
5. Each AMR is fully charged, calibrated, and assigned an initial set of tasks by the fleet management system. It is pre-configured to autonomously assess its battery status, task completion level, and current location in real time.
6. Key - The warehouse layout includes common areas, narrow aisles, and designated docking or charging stations. Certain areas may have higher traffic or more obstacles depending on the time of day and operations, requiring careful planning and dynamic rerouting. See Figure 11.8.2-1 for the zoning plans.
7. Key - There are multiple levels of manoeuvring speed allowed for different zones, including shared roads, and for different types of AMRs according to the level of potential risk, mobility capabilities, and types of role (e.g. carrying a simple item, carrying a stack of heavy items which leads to more ample time to prepare when something has happened).
Figure 11.9.2-1: High-demand complex warehouse with multiple zones served by different NPNs
NOTE: The use case has the primary focus on single story warehouse setting. Consideration points needed for communication service in a multi-story setting is FFS.
11.9.3 Service Flows
NOTE: The AMRs in this scenario are UEs.
1. Posting a list of sub-tasks and taking some of them by AMRs:
a) Each edge server puts the new sub-task (including its characteristics, such as urgency, load) on a common bulletin board. Example: transporting items from one aisle to a packing area or sorting items in specific zones.
b) Each AMR with its capability available for additional load of sub-task regularly scans the bulletin board.
c) Each AMR on its own decision volunteers to take a specific sub-task on a contention basis. Contention resolution is performed among those AMRs involved.
2. Real-Time Awareness Data Sharing:
a) AMRs share awareness data with nearby AMRs. The awareness data that an AMR shares with others can include the real time control/manoeuvre with its immediate plans, and its allowed range on ground mobility during a specific time window of common interests, types of roles with which other AMRs can get aware of what would be necessary for that AMR in mutual decision-making in the warehouse
b) For example, AMR1 is coming out of zone 1 with a set of ground mobility related parameters based on its capability and the route planned to travel. AMR2 and AMR3 are also coming out of zone 2 and zone 3, respectively, with their sets of parameters as well.
c) While AMR1 is attempting to share its awareness data, it has temporarily lost the connectivity.
d) The 6G system promptly help recover the connectivity from communication interruption. AMR1 can continue to share the data with a minimal interruption.
e) AMRs 1, 2, and 3 will need to share a certain spot on the shared road at certain point in time, and they share their awareness data with each other.
3. Collaborative Decision-Making:
a) Each AMR uses this data to adjust its navigation strategy and plan around obstacles or bottlenecks in high-traffic areas on the shared road.
b) Each AMR can solve the navigation strategy planning problem, e.g. using game-theoretic approach (given that other AMR’s options are known to each other), or requesting an edge server to solve that problem in coordination.
11.9.4 Post-conditions
AMR1 was able to share the awareness data with a minimum level of interruption owing to the immediate support from 6G system. In turn,
AMR2 and AMR3 were able to receive AMR1’s awareness data promptly.
AMR1 was able to receive AMR2’s and AMR3’s awareness data promptly.
AMR1, AMR2, and AMR3 were able to make mutual decisions so that they can avoid collision (ideal case), minimize the collision (e.g. within a given threshold value in practice), and/or avoid disturbing other nearby AMR from moving its optimal speed within the allowance.
11.9.5 Existing features partly or fully covering the use case functionality
Requirements on communicating indications in operations of cyber-physical systems (TS 22.186 [66], TS 22.104 [64]).
The data sharing between UEs served by different networks has been specified by 5G LAN-type service defined in TS 22.261 [14], however, no solutions has been concluded in stage 2 work. The data sharing between UEs within the same network can be partially covered by 5G LAN-type service. However, the group is managed by the subscription which cannot satisfy the requirement of group initiation by the AMR itself. In addition, for a short term or emergency task, the communication service provided to the group of AMRs should be established in a relatively short time to match time-efficiency of the task. However, the high time-efficiency requirement is hard to meet by 5G-LAN since 5G-LAN service requires lots of manual operations, e.g. LAN configuration, subscription, Data Network Name/Single Network Slice Selection Assistance Information configuration, etc. The same gap can also be applied to the NPN defined in TS 22.261 [14].
11.9.6 Potential New Requirements needed to support the use case
[PR 11.9.6-1] Subject to operator policy and regulatory requirements, the 6G system shall be able to provide a means to support data sharing among multiple UEs (AMRs) served by one or more 6G networks moving in a coverage area according to the following KPIs (Table 11.9.6-1).
NOTE 1: The shared data is collaborative awareness data that a UE (AMR) shares with other UEs (AMRs) and can include the real time control/manoeuvre with its immediate plans, and its allowed range on ground mobility, types of role with which other UEs (AMRs) can get aware of what would be necessary for that UE (AMR) in mutual decision-making in the warehouse.
NOTE 2: The data sharing is via direct network connection or via direct device connection.
Table 11.9.6-1: Performance requirements on collaborative awareness data sharing
Scenario
latency (ms)
(note 1)
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
Area traffic capacity
(UL)
Overall user density
Reliability
UE speed
(note 2)
UE type
Communication range (km)
(note 5)
AMRs performing tasks with low acceleration-sensitivity
[100]
N/A
N/A
TBD
N/A
N/A
[99.9999%]
Up to [10] m/s
AMR
> 0.4
(note 4)
AMRs performing tasks with relatively high acceleration-sensitivity
[10]
N/A
N/A
TBD
N/A
N/A
[99.9999%]
Up to [10] m/s
AMR
> 0.6
(note 4)
NOTE 1: The latency considered for up to two hops only. The latency is measured in the time interval between the time that collaborative awareness data is ready to be sent and the time that the data is received so that the information can be delivered to a nearby UE to prepare for necessary action to take.
NOTE 2: Relative speed between a pair of UEs (AMRs) is considered.
NOTE 3: The latency and reliability may vary depending on the type of application and the role of AMR performing different sub-tasks.
NOTE 4: It is assumed that AMRs with heavy payloads are operating passively (e.g. at a very low acceleration level) such that the time it takes for them to make a complete stop is long (e.g. more than 20 seconds).
NOTE 5: This means the minimum required distance when the UEs need to start communication in order to provide minimum ample time for UEs (AMRs) to prepare.
[PR 11.9.6-2] Subject to operator policy and regulatory requirements, the 6G system shall be able to provide a means to support efficient data sharing among multiple UEs (e.g. AMRs) initiated by a UE (e.g. AMR) considering the dynamics of the communication, e.g. frequent change of the involved UEs and the varied duration of the group communication to match the lifecycle of the task, etc.
NOTE 3: The shared data is collaborative awareness data that a UE (AMR) shares with other UEs (AMRs) and can include the real time control/manoeuvre with its immediate plans.
11.10 Use case on 6G localized network for vertical
11.10.1 Description
The vertical industry always requests specific requirements to PLMN operators for the network serving the vertical, which is not the same for personal users. Compared to 5G network, the 6G network should provide a more flexible network deployment, customized capabilities, and more localized management to the verticals, in order to satisfied the following new requirements according to the actual deployment and experienced feedback:
- The different verticals always have different requirements on the local operator’s network serving their needs. For example, some verticals require only fundamental communication capabilities, such as traffic or IMS. But other verticals may have interest on AIoT inventory, sensing or AI. Operators should provide the differentiated network or capabilities to different verticals.
- Some of the 5G experiences show that because the 3rd party has the data privacy or security concerns, the 3rd party prefers to manage the vertical network (or maybe only the UPF deployed in the 3rd party’s data centr[[SUGGESTION_START]]e[[SUGGESTION_END]]) by itself. The vertical prefers to have more freedom to local control of the vertical network, for example, local subscription, local signalling control, and local traffic forwarding for users, under the lightweight control of the PLMN. So, this may require both control plane and user plane to be localized for deployment and managed by the 3rd party vertical. The 3rd party may want to have some control of control plane or user plane.
- Preferring more convenient lightweight management, the vertical requires to have the capability to authorize or authenticate UEs locally within the local network. The UE that is only authorized and authenticated by the local network for the vertical can only access the services provided by the local network. If the UE also requires access to the PLMN, the interaction between local operator’s network for the vertical and PLMN is needed, while AAA server and NSSAAF may not be required.
- one Vertical may have more than one dedicated local networks which are deployed in different location. For example, there may exist several factories of the same vertical located in some cities. And the inter-connection between these factories is still needed for data collection, service coordination and service exposure.
11.10.2 Pre-conditions
As shown in Figure 11.10.2-1, a manufacturing company named XXX has several factories to produce the products. These factories are in different cities, A, B, C and D respectively. There is also a central office of Company XXX. The central office is in area E. The central office is responsible for giving task instructions to each factory and monitor the status of production processing in each factory.
Factories B and C have connections with the PLMN core network of the 6G system, but Factories A and D don’t. All the factories have connections with the central office.
Factory B deploys the IMS related network function and Factory C deploys sufficient edge computing resources.
Factory A uses uncrewed vehicles to transport raw materials internal the Factory A, or between Factory A and Factory B. Factory A and the central office should sense the road condition information and vehicle location. Also, Factory A is an unattended factory.
Factory D sometimes requires AI tools to enhance the efficiency of product manufacturing, fault monitoring and analysis.
Company XXX purchases a group of robots. Company XXX has been authorized by a PLMN operator that the company can perform the local authorization and authentication of these robots. But the authorized and authenticated robots can only access the vertical network in designated restricted area, such as inside the factory, and cannot access the operator’s PLMN. Also, these robots can only consume the limited services provided by the network inside the factory. If the robots require access to the PLMN or consume the services from the PLMN, authorization and authentication from the PLMN is needed.
Figure 11.10.2-1: Network Topology for Company XXX
11.10.3 Service Flows
1. Company XXX has four factories and one central office. Due to each factory requiring the different network services, the operator deploys different network functions to these factories and realizes the differentiated network deployment. See Table 11.10.3-1 for details.
Table 11.10.3-1: Network Capabilities required by different Area
Area
Network Capabilities
Central Office
Basic network communication capability. Local authorization and authentication for robots and other UEs owned by Company XXX itself. It may also provide support for UE’s service access and continuity between different factories or between factory and PLMN.
Factory A
Basic network communication capability, but no IMS. Support sensing features.
Factory B
Basic network communication capability including IMS.
Factory C
Basic network communication capability. Requirements of edge computing resource.
Factory D
Basic network communication capability. Support AI capabilities.
2. Company XXX has the requirement of data security and privacy. The company has the requirement that the network in the factory should still be able to operate normally even after it is disconnected from the PLMN of the operator. So, the network control plane should be locally deployed in factory A, B, C and D.
3. The central office purchases the robots. The purchased robots need to be used for delivering goods internal to Factory A and B. So, each of the robots is authorized and authenticated locally in Factory A and Factory B to access the network of Factory A and B. The local authorized and authenticated robots can only communicate with each other and consume the service of each internal vertical network in Factory A and Factory B.
4. The robots also wants to consume the IMS service or wants to communicate with the UEs in PLMN. But due to these robots being only authorized and authenticated locally, these robots are not authorized to consume the service provided by operator’s PLMN. So, the central office may perform another interaction with PLMN to request the PLMN to allow the robots to access the PLMN or to consume the services provided by PLMN.
5. If the connection between Factory A and central office building fails, Factory A can still operate normally, such as the robots will still be able to deliver goods to dedicated locations in Factory A and Factory A continues to produce product locally. Also, if the localized network in Factory B loses the connection with PLMN, it has no impact to the management of localized network.
6. The product line in Factory A is broken down. Company XXX asks engineer Alice to fix that problem. Alice arrives at Factory A and checks the product line, but she can’t figure out the fault cause. So, she asks for remote support. However, Factory A is an unattended factory and did not deploy IMS features. Alice discovers the IMS service in Factory B and invokes the IMS service-related function from the adjacent Factory B network. It supports the interconnection between Factory A and Factory B.
7. Senior engineer Bob analyses the fault of product line in Factory A and gives remote instruction to Alice by video call.
8. The Company XXX requires the AI capabilities to improve efficiency of production. This capability is deployed in Factory D network. Factory D network needs to train an AI model for fault analysis. But the edge computing resource in the Factory D network is not enough to train a model. The Factory D network discovers the edge computing resource in Factory C network and performs computational task offloading procedure to offload the task to Factory C.
9. During the peak supply period, Factory A requires AI capability to assist in improving the accuracy of quality inspection of goods. Therefore, during this peak period, the factory needs the operator to deploy AI capabilities to conduct product inspections through machine vision. Once the peak supply period is pass, the relevant AI capabilities will be disabled and released.
11.10.4 Post-conditions
The robots are authorized and authenticated successfully in factory A to access the network in factory A.
With the help of central office and PLMN, the robots access the PLMN and consume the services provided by PLMN, such as IMS or data service.
Alice and Bob find the fault cause of the product line broken down and fix the problem. The product line starts to work again.
Factory D network discovers the edge computing resource in Factory C network and with the help of the edge computing resource, the AI model is trained at the given time.
11.10.5 Existing features partly or fully covering the use case functionality
1) Network Slice:
TS 22.261 [14] clause 6.1.1 describes the network slicing as following:
Network slicing allows the operator to provide customized networks. For example, there can be different requirements on functionality (e.g., priority, charging, policy control, security, and mobility), differences in performance requirements (e.g. latency, mobility, availability, reliability and data rates), or they can serve only specific users (e.g. MPS users, Public Safety users, corporate customers, roamers, or hosting an MVNO).
A network slice can provide the functionality of a complete network, including radio access network functions, core network functions (e.g., potentially from different vendors) and IMS functions. One network can support one or several network slices.
TS 23.501 [140] defines the Network Slice as following:
Network Slice: A logical network that provides specific network capabilities and network characteristics.
and the feature of NS is described in clause 5.15.
All the UEs within the same slice share the resources in this slice. The UEs in network for vertical share the resources in this local network. Slice is the E2E logical network. The network for vertical can be seen as local deployment network and does not impact the E2E configuration of network.
2) NPN:
TS 22.261 [14] clause 6.25 describes NPN as:
Non-public networks are intended for the sole use of a private entity such as an enterprise, and can be deployed in a variety of configurations, utilizing both virtual and physical elements. Specifically, they can be deployed as completely standalone networks, they can be hosted by a PLMN, or they can be offered as a slice of a PLMN.
TS 23.501 [140] clause 5.30 describes NPN as:
An NPN is either:
- a Stand-alone Non-Public Network (SNPN), i.e., operated by an NPN operator and not relying on network functions provided by a PLMN, or
- a Public Network Integrated NPN (PNI-NPN), i.e., a non-public network deployed with the support of a PLMN.
If the SNPN requires to interact with PLMN, the SNPN is seen as the untrusted non-3GPP access and needs the N3IWF.
In 5G, only the service continuity is supported by the SNPN. In order to support continuity, the SNPN is seen as the untrusted non-3GPP access and this requires the operator to have the N3IWF deployment.
11.10.6 Potential New Requirements needed to support the use case
[PR 11.10.6-1] Subject to an agreement between a PLMN operator and authorized 3rd party, operator policies and regulatory requirements, the 6G network shall support a mechanism to authorize UEs owned by a trusted authorized 3rd party (e.g. vertical industry) to access services provided by a localized network (deployed by the PLMN operator for the authorized 3rd party).
[PR 11.10.6-2] Subject to an agreement between a PLMN operator and authorized 3rd party, operator policies and regulatory requirements, the 6G network shall support a mechanism to authorize and authenticate UEs owned by a trusted authorized 3rd party to access services from the PLMN operator.
NOTE: The realization or deployment of localized network may be an enhancement of NPN, PALS or other network topology options.
[PR 11.10.6-3] The 6G network shall support a mechanism to allow the operator or authorized 3rd party to perform local control of the localized network when the localized network loses connection with the PLMN network.
[PR 11.10.6-4] The 6G system shall support a mechanism to allow the UE to discover and select the services provided by a localized network.
11.11 Use case on in-vehicle local communication
11.11.1 Description
Wireless local communication within a vehicle is required in many scenarios to complement existing communication technologies, such as Ethernet. Wireless communication can offer several benefits, including reduced complexity, increased flexibility, and lower costs, for example, by simplifying the vehicle’s wiring harness. However, the existing 3GPP communication modes and topologies may not be optimal for this type of localized communication. Integrating and operating all the vehicle’s devices and subsystems, i.e. sensors, actuators, and controllers, through the 3GPP infrastructure can be cumbersome or even unfeasible when only short-range, direct communication within the vehicle is needed. Furthermore, any loss of connection to the 3GPP network may cause outages in the vehicle's systems or subsystems, which is unacceptable. Routing vehicle traffic and management functions through public mobile networks may also result in increased latency, higher network load, and inefficient use of available spectrum.
In-vehicle networks are local wireless networks connected to a mobile network (e.g. PLMN, NPN) and possess partial autonomy to operate during a loss of connectivity, for example, when a vehicle moves into an area without mobile network coverage. The mobile network is intended to provide external connectivity for the vehicle and offer bulk communication resources for internal communication between the vehicle’s devices.
Local control enabled within the vehicle can achieve partial autonomy from the mobile network, thereby mitigating the impact of connectivity loss. Thus, radio resource management can be done locally for local communication, based on the resources and configurations received from the mobile network.
11.11.2 Pre-conditions
As shown in Figure 11.11.2-1, in a system such as a vehicle, there is a set of devices that need to connect with each other and exchange data reliably. Any component of the vehicle that can operate as a UE-type device must be authorized by a mobile network (e.g. PLMN, NPN) before initiating communication. Local control enabled within the vehicle manages in-vehicle local communication based on the necessary configurations and radio resources received from the mobile network. The in-vehicle network can be configured with partial autonomy from the mobile network to support at least limited operation when the vehicle is out of coverage, based on network policies. The enabled local control includes local authorization, resources allocation and configuration of the devices for the local communication.
Figure 11.11.2-1: Local connectivity in a vehicle
11.11.3 Service Flows
1. The local network in a vehicle receives a set of configurations from the mobile network for the local control of local communications within the vehicle.
2. The devices in the vehicle, which need to communicate locally, are authorized by the mobile network, or locally, if the vehicle is not in-coverage of the mobile network.
NOTE 1: The local network in a vehicle should receive the required information from the mobile network to be able to perform local authorization for the UEs joining the local network when the vehicle is out of coverage.
3. Resources for local communication inside the vehicle are allocated by the mobile network to the vehicle in bulk based on the announced location of the vehicle and communication requirements. When out of coverage of the mobile network, the local network can be configured with a pre-defined set of resources to enable basic services.
4. The resources are allocated to the devices inside the vehicle for local communication by local control.
5. The local control of in-vehicle network can configure devices for local communication in the vehicle based on use cases and application requirements.
NOTE 2: The direct communication between two devices of in-vehicle local network can be via for e.g. sidelink.
11.11.4 Post-conditions
The in-vehicle network can support local connectivity between different wireless devices such as sensors, controllers, and passenger’s devices even when the vehicle is out of the coverage of a mobile network. The load of the mobile network is reduced due to local management and communication of in-vehicle networks.
11.11.5 Existing features partly or fully covering the use case functionality
NPN, described in TS 22.261 [14] clause 6.25, has all necessary core network functionalities and may not need any configuration or management by the public network. On the other hand, a local network inside a vehicle does not need all core network functionalities except functionalities for local authorization of devices when the car is out of coverage of mobile network, and this makes it more efficient [14].
Sidelink devices, described in TS 38.300 [221] clause 5.7.1, must be authorized by public network and no sidelink device can take over controlling other devices without having these extended capabilities. This means we cannot have any local network control using existing sidelink capabilities.
PIN, described in TS 22.261 [14] clause 6.38.2.3, allows the utilization of licensed spectrum only for public safety PIN when PIN element is out of the coverage of the mobile network, otherwise unlicensed spectrum should be used [14]. Therefore, there is no local management and authorization in PIN, if there is a loss of the connection to the mobile network.
In a CPN, described in TS 22.261 [14] clause 6.38.2.3, the PRAS should also deactivate the radio interface when it is not connected to the mobile network. Besides, the connectivity between eRG, UE and non-3GPP devices are based on non-3GPP technologies [14].
11.11.6 Potential New Requirements needed to support the use case
Potential new requirements to enable local connectivity within a vehicle are listed as follows:
[PR 11.11.6-1] Subject to regulatory requirements and the operators’ policy, 6G system shall be able to support (partial-)autonomous local networks for the communication between UEs and devices.
NOTE: (Partial-)autonomous means that the management of local network can be done locally even in case of loss of connectivity to the mobile network.
[PR 11.11.6-2] Subject to regulatory requirements and operator policy, the 6G network shall allow the operator to provide the required control information including configurations to the local network that enables it to provide necessary configurations for the local communication and to operate also out of coverage of the mobile network.
[PR 11.11.6-3] Subject to regulatory requirements and the operators’ policy, the 6G system shall provide the possibility of local authorization for a new device or UE joining the local network.
11.12 Use case on supporting collaborative intelligence using multiple service robots
11.12.1 Description
As an example of supporting collaborative intelligence among multiple service robots , this use case considers an energy-efficient collaboration scenario in which a group of robots jointly constructs a 3D map. The objective is to enable operation in unstructured environments, such as enterprise building cleaning, large-scale disinfection preparation, and agricultural automation. By coordinating measurement data collection among multiple robots, it becomes possible to reduce energy consumption, enhance the quality of the mapping outcome, or achieve both simultaneously. [123], [222].
NOTE 1: Certain aspects of agricultural automation can also be explored through a combined scenario involving both ground and aerial mobility.
NOTE 2: In this use case, the term 'map' is not limited to geographic features; it may also encompass static objects that are useful or essential for robots operating in irregular and/or unstructured environments.
11.12.2 Pre-conditions
A group of service robots that are equipped with capabilities of multi-dimensional ambient sensing, computing (standalone and/or via compute fabric), federation in learning and model building, and 3GPP subscription-based communication, are in cooperation for a single joint project (see Figure 11.12.3-1).
The availability of communication service to/from edge (or cloud) is threefold: not available, temporarily unavailable, or available (for certain period of time; positive interpretation although the term “available” does not mean “permanently available”).
NOTE: This use case is mostly focused on direct device connection with partial or intermittent direct network connection to radio network of 6G system (or to edge server via radio network of 6G system).
11.12.3 Service Flows
The edge (a server), if available for one or more of these service robots, will assist them to alleviate their computational burdens (that are or are not within the scope of 3GPP), giving rise to a demand of accessing service-specific network slice(s) or other forms of network resources with certain performance requirements.
An operator of robotic applications starts operating a group of service robots which are UEs.
These service robots discover each other and share their capabilities.
NOTE 1: For each service robot (UE), capabilities include certain characteristics such as types of supported RATs (e.g. RAT for 6G, 5G NR, E-UTRA, or non-3GPP access technology) and information that are not within the scope of communications layer, such as remaining battery life.
All or some of these service robots form a working group (with one or more leader robots) and start communicating.
Member robots send measurement data to a leader robot so that the leader robot can perform the next step to build a 3D map.
NOTE 2: The roles of leader robot(s) include coordination required for the operation of the working group of service robots, such as acting as sync master for other robots (sync devices) within the working clock domain.
These service robots scan environmental parameters, including 3GPP service availability, and collaboratively decide which operational scenario they should choose (i.e. direct network connection or direct device connection).
Each service robot in the working group walks in coordination with each other, forming a gregarious cluster (i.e. distance between any pair is not unnecessarily far, degrading the performance of map building outcome).
Each service robot is exposed to uneven surface along its trajectory (e.g. signal angle measurement is not static, unpredicted loss of measurement accuracy level is likely to happen).
Depending on the accuracy level of 3D map at certain spot of the job site and decision made by the leader robot(s), the application layer of the leader robot requests to adjust the clock synchronisation target value within the clock synchronisation budget.
While moving along, one member robot, say robot A, faces some issue, resulting an unexpected drop in the moving speed.
Member robot A has already predicted this issue beforehand: its follow-up actions include reporting this information to a leader robot and marking time stamp on the measurement data with this outlier situation.
It is up to member robot A whether or not, to send the measurement data with outlier indication to a leader robot.
It is up to the leader robot whether or not, to use the received data with outlier indication, if received from member robot A, for 3D map building.
Later, member robot A gets a little bit away from the gregarious cluster, leading to a temporary loss of connection to a relay UE robot (or to radio network of 6G system in direct network connection scenario). Member robot A promptly resumes a connection.
Figure 11.12.3-1: Inter-robot operation example when a network of service robots that have ambient intelligence (e.g. intra-robot operation) are in cooperation for a joint project [123], [222]
11.12.4 Post-conditions
The working group of service robots can build up a 3D map with only necessary level of accuracy so that they do not have to consume computing and communication resources to build up a 3D map of an area that is overly accurate.
Also, for an important area, they could adjust the level of accuracy.
They could prevent potential noise factors that could have contributed to the quality of 3D map with the help of prediction-based indication.
A robot that has instantaneously lost a connection can resume a connection very promptly and send time-critical information to other member(s).
11.12.5 Existing features partly or fully covering the use case functionality
Clock synchronisation: TS 22.104 [64]
- clause 5.6.1 Clock synchronisation service level requirements
- clause 5.6.2 Clock synchronisation service performance requirements
- clause 7.2.3.2 Clock synchronisation requirements
Timing resiliency: TS 22.261 [14]
- clause 6.36.2 General requirements to ensure timing resiliency
- clause 6.36.3 Monitoring and reporting
- clause 6.36.4 Exposure
Multi-path relay: TS 22.261 [14]
- clause 6.9.2.1 support of a traffic flow of a remote UE via different indirect network connection paths
Positioning: TS 22.261 [14]
- clause 7.3.2 High accuracy positioning performance requirements (see also clause 5.7.1 of TS 22.104 [64] for Factory of the Future scenario)
Service continuity: TS 22.263 [67]
- clause 5.5 Service continuity
11.12.6 Potential New Requirements needed to support the use case
[PR 11.12.6-1] The 6G system shall be able to provide a means to expose its ability to meet the accuracy level of clock synchronization for a group of UEs (service robots) requested by trusted third party.
[PR 11.12.6-2] The 6G system shall be able to provide a means to check its ability to meet the recovery time from direct network connection interruption, requested by trusted third party.
[PR 11.12.6-3] The 6G system shall be able to provide a means to expose its ability to meet the recovery time from direct network connection interruption, requested by trusted third party
11.13 Use case on cooperative networking under extreme conditions – mining, agriculture, and more
11.13.1 Description
The use of a group of robots in mining:
It is expected that the mining industry will pave new pathways toward global sustainability, including “greater inclusion and diversity effort” (e.g. worker safety, well-being of employees in general), green/clean energy transition, material consumption reduction, deep sea mining exploration, greenfield exploration [223]. More interestingly, the industry is with no exceptions as other industries, exploring a new path to “cloud-integrated mining processes”.
Figure 11.13.1-1: Examples of extreme working conditions in mining site (underground)
Figure 11.13.1-2: Examples of extreme working conditions with workers’ roles replaced by robots. ISAC, distributed sensing and communication (data collections), and AI-enabled compute are expected
Among these, (1) greater inclusion and diversity effort (with a narrower angle of worker safety using tele-operated robots in mining) and (2) cloud-integrated mining processes are interesting topics for consideration in the telco domain (refer to Figure 11.13.1-1 and Figure 11.13.1-2).
Robots have a range of applications in the mining industry, contributing to increased safety, efficiency, and productivity. Here are some tasks that robots can perform in mining:
1. Exploration and Mapping: Robots can be equipped with various sensors, such as LiDAR and cameras, to explore and map underground or hazardous areas that might be dangerous for humans.
2. Drilling and Blasting: Automated drilling and blasting robots can accurately and safely bore holes for explosives, increasing precision and minimizing the risk to human operators.
3. Hauling and Transport: Robotic vehicles can be used for hauling materials, removing the need for human drivers in dangerous environments. These robots can transport materials within mines and even across long distances.
4. Inspection and Maintenance: Robots can inspect equipment and infrastructure, identifying issues before they become serious. They can also perform maintenance tasks in hazardous areas, reducing the need for human workers in risky environments. Note: Maintenance includes corrective maintenance and preventive maintenance. Predictive maintenance is related to preventive maintenance.
5. Remote Operation: Teleoperated or semi-autonomous robots can be controlled by operators from a safe location, allowing them to work in environments that are unsafe for humans.
6. Hazardous Environment Exploration: Robots can be deployed in areas with extreme temperatures, toxic gases, or other hazardous conditions, where human presence would be dangerous.
7. Material Sorting and Processing: Robots can be programmed to sort and process mined materials, improving efficiency and accuracy in material separation.
8. Surveying and Mapping: Robots equipped with advanced sensors can create detailed 3D maps of mining sites, helping with planning and optimization.
9. Search and Rescue: In the event of a mine collapse or other emergency, robots equipped with cameras and sensors can be used to search for trapped miners and assess the situation.
10. Environmental Monitoring: Robots can be used to monitor air quality, water quality, and other environmental factors in and around mining sites.
11. Dust Suppression: Robots can be designed to control dust levels, which is crucial for the health and safety of miners.
12. Rehabilitation and Land Restoration: After mining operations cease, robots can be employed to rehabilitate and restore mined areas, aiding in reforestation or other environmental recovery efforts.
The use of robots in mining can improve safety for human workers, increase operational efficiency, and enable the extraction of resources from challenging and hazardous environments.
Societal Impact and Sustainable Development Goals (SDGs)
The UN's Sustainable Development Goals (SDGs) [87] address worker safety through several targets. Notably, SDG 8.8 specifically focuses on protecting labor rights and promoting safe and secure working environments for all workers, including migrant workers, women migrants, and those in precarious employment. Additionally, SDG 3.9 aims to reduce deaths and illnesses from hazardous chemicals and pollution. These goals highlight the importance of creating healthy and safe work conditions for all.
Mining as a whole takes place in extreme environments under the ground surfaces (e.g. search, monitor, preparation, processing, maintenance, repair shop, mining actuation (i.e. drilling operations), loading and underground delivery to off-surface station) and some tasks require their completion on the ground surface.
According to the US Energy Information Administration [224] as of 2021, the average number of employees at underground and surface mines differs from one State to another: 755 at underground and 272 at surface mines in Pennsylvania; 2103 at underground in West Virgina (Northern), 184 at underground and 36 at surface mines in West Virgina (Southern). Along a single or multiple tunnels, a set of tandem communication sub-networks can be formed as depicted in Figure 11.13.1-3. Each sub-network might include a 3GPP UE-type entity that only requires intermittent communications (e.g. Ambient IoT device).
NOTE: It is assumed that a set of multihop UE relays consists of a group of service robots that are performing specific task(s) with (autonomous or tele-operated) physical mobility inside the mining job site. However, non-robot type of UE’s can also be part of a set of multihop UE relays.
Figure 11.13.1-3: Some examples of set of multihop UE relays of robots at underground mines
11.13.2 Pre-conditions
There are a number of robots (UEs), which are robot 1, robot 2, robot 3 and so on, working on underground mining site (e.g. in mining tunnels) and on the surface of the Earth (that are moving ore that has been moved out of underground tunnels to another place in the mining job site). The term “ore” refers to naturally occurred rock that contains enough valuable minerals. In the on-surface environment of radio signal propagation (i.e. one being line-of-sight reception and the other a slightly longer reflected one, which are combined at the receiver’s side), the signal transmission and reception situation is not
Motivation and Discussion:
In the on-surface environment of radio signal propagation (i.e. one being line-of-sight reception and the other a slightly longer reflected one, which are combined at the receiver’s side), the signal transmission and reception situation is not ideal due to the inherent 'phase difference' caused by reflection, as depicted in Figure 11.13.2-1 below. The point that we bring in is to lead to the main motivating message that the signal propagation situation is so difficult in underground tunnels (as justified in the point #2 below).
Figure 11.13.2-1: Phase difference caused between two antennas on a horizontal ground surface with different heights. The phase difference is dependent upon several parameters, the antenna heights, and some other parameters, such as a, b, l, and θ
In the underground tunnel environment, there are two characteristics that are simply observed:
1) motivating argument #1: The signal reflections are multifold around the inner surface of the tunnel which is furthermore “uneven”:
2) motivating argument #2: The communication nodes (i.e. UEs/Robots or mining carts that have UE functionality) are distributed along the tunnel pathway.
Justification on point #1 (as motivating argument #1):
In underground tunnels, radio propagation faces significant challenges due to the confined and reflective nature of the environment. Signals can be absorbed, diffracted, or reflected by the tunnel walls, leading to multipath propagation. This phenomenon creates multiple signal paths between the transmitter and receiver, causing delays and phase differences in the received signals. As a result, the radio propagation situation in underground tunnels is complex, making it difficult to achieve reliable and stable communication. Specialized techniques and equipment are often required to mitigate these challenges and ensure effective wireless communication in such environments.
Justification on point #2 (as motivating argument #2):
The distribution of mining carts (which we assume are equipped with UE functionality and called robots) in a mining tunnel typically involves strategic planning and organization to ensure efficient transportation of materials and resources within the mine. Mining carts, also known as mine cars or skips, are used to transport extracted ore, waste, or other materials from the mining face to the surface or processing area. The distribution process involves several key considerations:
1) Loading and Filling: Mining carts are loaded with ore or other materials at the mining face. Proper loading ensures maximum utilization of the cart's capacity while maintaining safety standards.
2) Transportation Routes: Mining tunnels are designed with specific transportation routes for the mining carts. These routes are planned to optimize the movement of carts, minimize congestion, and ensure a smooth flow of materials within the mine.
3) Track Systems: Mining carts often run on track systems embedded in the tunnel floor. These tracks guide the carts, preventing derailments and ensuring stable movement along the designated routes.
4) Automated Systems: In modern mining operations, automated systems, such as conveyor belts or autonomous vehicles, might be used to transport materials. These systems can enhance efficiency and reduce the need for manual distribution of mining carts.
5) Monitoring and Control: Mining companies use monitoring systems to track the movement of mining carts. Sensors and communication technologies are employed to monitor the location, load capacity, and maintenance needs of the carts. This data helps optimize the distribution process and prevent bottlenecks.
6) Safety Protocols: Safety is paramount in mining operations. Adequate safety protocols, including signaling systems, speed limits, and emergency procedures, are in place to ensure the safe distribution of mining carts and the protection of workers in the tunnels.
Overall, the distribution of mining carts in mining tunnels requires careful planning, efficient logistics, and the integration of technology to optimize the transportation of materials and maintain a safe working environment.
It is commonly agreed that carts cannot remain outside the designated pathway space of the tunnel unless unintended events occur.
11.13.3 Service Flows
NOTE: The term 6G RAN used below does not imply any architectural assumption, e.g. whether 6G RAN is a new or evolved RAN (compared to 5G).
1. Robot 1 (UE1) is connected to the 6G RAN via Robot 2 (UE2), Robot 3 (UE3), and so on, as illustrated in Figure 11.13.3-1(a).
2. The communication link between neighbouring robots (e.g. Robot i and Robot i+1) experiences QoS degradation under various underground tunnel conditions, leading to intermittent communication interruptions.
3. 6G RAN nodes collect data related to these interruptions and evaluate the operational behaviour of various communication parameters, leveraging intelligent computing capabilities provided by a trusted third party associated with the operator.
4. The 6G system develops a strategy to manage communication parameters, including the use of advanced (non-conventional) methods and determining the allowable number of UE relay hops under such underground tunnel conditions.
5. An event of communication interruption occurs, and Robot 1 experiences a 30-second communication outage. During this time, the 6G system does not initiate any corrective actions, as no conventional methods are recognized as effective countermeasures for the interruption.
6. However, the 6G system is capable of predicting communication condition degradation. Based on this predictive capability, it selects a new operational configuration of communication parameters—such as employing non-conventional methods like cooperative networking or communication—and reconfigures the robots accordingly [136], [307], [308], [309], [310], [311].
7. When another interruption event occurs, Robot 1 maintains uninterrupted communication. This is because the 6G system had already anticipated the degradation and applied a proactive reconfiguration using non-conventional techniques that would not have been feasible under conventional methods.
Characteristics of the ‘set of UE relays’ consisting of robots in mining tunnel:
1) Serially distributed; occasional allowance to use multiple paths per UE (see Figure 11.13.3-1(b))
2) the random variable of inter-node distance depends on the mining operation strategy
3) if the communication nodes (e.g. carts with UE functionality) are available to use non-conventional (or advanced) communication/networking schemes [136], [307], [308], [309], [310], [311], such as cooperative diversity [307], [308], [311] (or cooperative communication/networking) [308], [309], [310], [311] and network coding, the topology of the network is not completely serial but is a combined one as shown in Figure 11.13.3-1(b) [307], [308], [311].
Figure 11.13.3-1: (a) conventional, serially connected multihop network (b) non-conventional (occasional allowance to use multiple paths per UE), serially connected multihop network with a supplementary link supported from UE 1 to UE 3 (e.g. when cooperative diversity is applied).
11.13.4 Post-conditions
Before the second occurrence of communication degradation, the 6G system was able to predict the issue and establish a feasible strategy that allowed the robots to maintain communication without experiencing any interruption.
11.13.5 Existing features partly or fully covering the use case functionality
Clock synchronisation: TS 22.104 [64]
- clause 5.6.1 Clock synchronisation service level requirements
- clause 5.6.2 Clock synchronisation service performance requirements
- clause 7.2.3.2 Clock synchronisation requirements
NOTE 1: The MEC scenario described in clause 11.31.1 of the present document assumes collaboration among the group of aerial robots. The data collection and sensor fusion aspects are still important considerations for this MEC scenario.
NOTE 2: The types of sensor data and media that robots are collecting, pre-processing and sharing with each other and/or with edge cloud (or edge server, cloud server) are related to the need of fulfilling the above sets of requirements.
Multi-path relay: TS 22.261 [14]
- clause 6.9.2.1 support of a traffic flow of a remote UE via different indirect network connection paths
Positioning: TS 22.261 [14]
- clause 7.3.2 High accuracy positioning performance requirements (see also clause 5.7.1 of TS 22.104 [64] for Factory of the Future scenario)
Efficient user plane: TS 22.261 [14]
- clause 6.5 Efficient user plane
Service continuity: TS 22.263 [67]
- clause 5.5 Service continuity, when required by the application
Multi-hop connectivity: TS 22.261 [14]
Energy efficiency: TS 22.261 [14]
Integrated Sensing and Communication features: TS 22.261 [14], TS 22.137 [6]
Key Requirements for Multihop Relay Support: TS 22.261 [14]
Indirect Network Connection: The specification covers scenarios where a remote UE connects to the 3GPP network via relay UEs, explicitly allowing for "one or more relay UE(s) (more than one hop)"—i.e. multihop relay support is contemplated, not just single-hop relays.
Relay UE Selection: The system must support selection and reselection of relay UEs based on various criteria, such as traffic characteristics, subscriptions, relay capabilities/capacity/coverage, achievable QoS, power consumption, pairing status, and whether the relay UE uses 3GPP or non-3GPP access for network connection. This enables dynamic chains of relays adapted to context—a foundational need for robust multihop relay networks.
Application Scenarios: The specification notes anticipated use cases for multihop relay, such as extending communication range and improving reliability in verticals (e.g. smart mines, public safety, disaster relief), with relays assisting to span large distances or coverage gaps.
Stage 1 Normative Requirements (Release 18/19): Recent updates and work items highlight that both UE-to-network and UE-to-UE multihop relay requirements are being further codified.
NOTE: Among these features, the multihop UE relay features are based on a series of connected UEs, in which the end UE uses only one UE at any cut of the communication path, such that one UE relay is connected to no more than one single next node (UE or base station) in the previous releases.
11.13.6 Potential New Requirements needed to support the use case
[PR 11.13.6-1] Subject to the operator’s policy and service agreement, the 6G system shall provide a suitable means that enables remote UE to operate under extreme conditions via indirect network connection.
NOTE: UEs operating under extreme conditions include UEs under difficult networking conditions where more advanced mechanisms are needed, such as mining robots, carts and collaborative robots in an underground mining site.
11.14 Use case on seamless connectivity for 6G-enabled Mission Critical services
11.14.1 Description
As public safety communities and society at large grow increasingly reliant on mobile networks, building a resilient foundation for critical communications is essential connectivity is the lifeline. According to TCCA [258], mission-critical communications must consistently meet core requirements: coverage, availability, resilience, performance, and scalability, ensuring 24/7 instant and reliable connectivity, particularly during life-saving emergency operations.
Thanks to 3GPP standardisation, Mission Critical Services (MCPTT [53], MCVideo [55], MCData [56]) over 4G and 5G are now well-defined. With regulatory support and operator deployments, LTE and 5G have become globally recognised, all-inclusive platforms for delivering mission-critical services. Concurrently, continued hardening of MNO networks is essential to meet public safety-grade requirements such as 99.999% availability and robust cybersecurity.
6G is expected to bridge existing gaps and enhance capabilities to support 6G-enabled Mission Critical Services. Its ubiquitous and resilient infrastructure will be essential for search and rescue operations in remote or disaster-affected areas where terrestrial networks are unavailable or damaged, ensuring service availability, reliability and seamless positioning. Deep integration of different network topologies (e.g. TN and NTN) will facilitate seamless transitions and interoperability among agencies across all phases of emergency management, particularly in large-scale disaster responses involving multiple agencies and disciplines coordination.
However, current 6G use cases lack a comprehensive view of 6G-enabled Mission Critical Services. This use case aims to fill that gap by illustrating a disaster response scenario enhanced by the 6G system, strengthening emergency responders’ ability to save lives, protect assets, and reduce environmental impact. Supporting this use case can also reinforce MNOs’ confidence in delivering Networks as a Service with fully shared Public Safety Broadband Networks (PSBNs) for mission-critical services, while enabling advanced capabilities such as Sensing as a Service model in the 6G era.
Reference and Background:
The NGMN Alliance [225] points out that IMT-2030 should not compromise existing services, for example, voice, existing public safety services. Further national/regional regulatory requirements should be delivered with the launch of IMT-2030. Interworking between IMT-2030 and non-IMT systems will need to be embraced, and standards for mobile networks should be global, reflecting industry consensus.
At the international level, advancing disaster radiocommunication supporting prevention, mitigation, and relief has become a key priority for the ITU. The Public Protection and Disaster Relief Liaison Rapporteur [226] and Question ITU-R 209-7/5 [227] (Use of the Mobile, Amateur, and Amateur-Satellite Services in Support of Disaster Radiocommunications) stress the need for collaborative studies across ITU-R (including WPs 5A and 5D), ITU-T, and ITU-D to address the technical, operational, and procedural aspects of disaster communications.
In Europe, the EU Critical Communication System (EUCCS) is a European Commission-led initiative aimed at developing a secure, interoperable, and resilient broadband communication system for public safety organisations and emergency services across the EU by 2030 [355]. Built upon 3GPP-standizised mission-critical services, EUCCS will enable seamless cross-border communication for emergency responders, enhancing Europe’s crisis response and operational mobility.
In China, one of the world’s most disaster-prone nations, has launched a range of initiatives to strengthen its emergency communication infrastructure, dedicated to disaster relief. In December 2024, the Ministry of Industry and Information Technology (MIIT), together with 13 other government agencies, issued guidelines [228] to further enhance emergency communication capabilities, especially in extreme disaster scenarios. The strategy emphasises inter-agency coordination and infrastructure modernisation, with a focus on integrating satellite systems, private networks (e.g. Professional/Police Digital Trunking (PDT)), and MNO infrastructure—laying the groundwork for a unified national emergency communication framework.
11.14.2 Pre-conditions
1. The UE (e.g. handheld device) is operated by the disaster responders, hosts MCX client and is registered with MCX service in the 6G system.
2. The UE supports directly connecting with satellite access.
3. The UE supports access to Land Mobile Radio (LMR) networks, by integrating LMR technology (e.g. hybrid devices).
4. The UE is equipped with radio receivers capable of receiving GNSS signals.
5. The UE supports ProSe-enabled UE relay or remote UE capability and/or MANET routing, capable of forming a mobile ad-hoc network with other UEs.
NOTE: The control room solution in the Emergency Operation Centre (EOC), which may be based on 3GPP technologies, non-3GPP technologies, or a combination of both, is out of scope for this use case.
11.14.3 Service Flows
Figure 11.14.3-1: 6G-enabled MCX service with seamless connectivity for large-scale disaster response
As illustrated in Figure 11.14.3-1, the services flows proceed as follows:
1. The EOC receives alerts of an earthquake striking Jiuzhaigou, formulates a response strategy based on situational awareness, and initiates the dispatch resources.
2. Upon receiving instruction via LMR networks (e.g. through call-out functionality), Rescue Team A at a nearby station swiftly mobilises, gathers necessary equipment, including handheld devices, and departs for the scene.
3. En route, the Incident Commander (IC) remains connected with dispatchers to view comments and accessing critical data (e.g. weather updates) via MCX application across PSBN (if applicable), MNO-1 and MNO-2 networks.
4. Built-in navigation tools assist first responders in finding optimal routes through traffic, while real time location tracking in the control room enables dispatchers to monitor all personnel and vehicles, ensuring swift dispatch and enhanced safety.
5. As the EOC gains further situational awareness of the disaster’s scale, the response strategy is updated, and additional rescue team B and C are dispatched to the scene through LMR networks.
6. Upon arriving in a remote mountain area with no TN or access routes, the IC’s handheld device automatically connects to satellite (NTN) access, enabling uninterrupted communication with dispatchers (e.g. via private call) for real time updates.
7. Based on the operational strategy, a UAV-based (or HAPS-based) relay platform is deployed. IC then coordinates on-scene rescue team A to begin victim search operations, while their handheld devices switch (automatically or manually) to off-network, mesh-enabled mode. The established infrastructure-less mobile ad-hoc network, extended by UAV relay node, enhances local connectivity and coverage. IC’s UE and/or UAV, acting as a relay gateway via satellite, enable communication between on-scene responders and dispatchers.
8. Meanwhile, as the focal point of communication, the IC continues to coordinate team B and C are en route and connected to the MNO-2 network. Group communication spans satellite, TN and mobile ad-hoc network access, enabling seamless interoperability across agencies, jurisdictions and disciplines.
9. Upon arrival, Teams B and C transition (manually and automatically) from TN access (on-network) to mobile ad-hoc network access (off-network), maintaining communication with Team A and IC for coordinated rescue operation.
10. Once the crisis is contained, teams coordinate with other agencies, utilities, and local authorities to support organised recovery efforts. After task completion, they return to base, debrief, and provide feedback.
11. Throughout all response phases—from call-out to return—reliable positioning ensures continuous personnel tracking and situational visibility, even in scenarios where GNSS signals are compromised.
11.14.4 Post-conditions
Thanks to the support of 6G systems, the MCX service system, and a 6G-enabled MCX hybrid device, first responders can communicate seamlessly across different networks without interruptions, ensuring fast and efficient mission execution.
11.14.5 Existing features partly or fully covering the use case functionality
Clause 6.17.2 of TS 22.280 [54] defines requirements for interworking between MCX service systems. This use case proposes that the 6G system shall continue to support MCX services while ensuring service continuity during UE mobility across different MNO networks, as well as between PLMNs and NPNs. This capability is essential for seamless MCX operations in cross-border coordination, network sharing, and co-construction scenarios—not only in the early stages of 6G deployment but throughout the entire 6G era—to enhance service availability and coverage.
TS 23.304 [245] by SA2 defines the use of MANET protocol (IETF RFC 7181 [246]) as the mobile ad-hoc routing protocol for normative support of multi-hop UE-to-UE relay, where relay UE have a collocated MANET routing functionality. These advancements enable ProSe-capable MCX UE form a mobile Ad-hoc network with mess connectivity, addressing a key requirement for seamless mobility on the scene in this use case. However, the clarification is still required for alignment and consistency across WGs.
In 5G, clause 6.46.7 of TS 22.261 [14] specifics requirements for 5GS to support relay UE with satellites access. However, support for relay UEs using UAVs and HAPS access is not specified, which is essential for extending communication range and enhancing local connectivity in this use case.
Clauses 7.12 and 7.13 of TS 22.280 [54] specify a set of requirements for MCX services streaming for ProSe UE-to-UE relay, UE-to-Network relay and off-network operation, as well as switching to off-network MCX services; and TR 22.887 [122] also studies use cases and requirements for 5G satellite access Phase 4, including mission-critical services. However, they do not address MCX service continuity during UE transitions between TN access and NTN access (e.g. UAVs, HAPS, and satellites).
In 5G, clause 6.27.2 of TS 22.261 [14] defines requirements for single and hybrid positioning, including MCX UE use of 5G positioning services. However, it does not specify network-assisted GNSS via satellite access in scenarios where terrestrial networks are unavailable in order to improve resiliency and availability.
Clause 6.18.3 of TS 22.179 [53] specifies requirements for interworking between MCPTT and non-3GPP PTT systems (e.g. P25, TETRA, legacy LMR). Clause 7.1 of TS 22.282 [56] specifies interworking between MCData and TETRA SDS. This use case adds requirements for MCPTT-PDT interworking, with a gap analysis against P25/TETRA provided in Table 11.14.5-1. Stage 2 and Stage 3 specifications are expected to build on the existing framework, reusing most functional and protocol elements. Nevertheless, the MCPTT-PDT interworking requirements shall be specified and supported in the 6G system.
Table 11.14.5-1: Gap analysis for MCPTT service with various LMR technologies
No.
Interworking with P25
Interworking with TETRA
Interworking with PDT
1
[R-6.18.3.2-001] The MCPTT Service shall enable interworking with non-3GPP PTT Systems that are compliant with the TIA-102 (P25) standards.
[R-6.18.3.3-001] The MCPTT Service shall enable interworking with non-3GPP PTT Systems that are compliant with the ETSI TETRA standards.
Similar requirement as defined for P25/TETRA.
2
[R-6.18.3.2-002] Interworking between the MCPTT Service and P25 shall be capable of interworking with a multiplicity of independently administered Project 25 Radio Frequency Subsystems (RFSS).
[R-6.18.3.3-002] Interworking between the MCPTT Service and TETRA shall be capable of interworking with a multiplicity of independently administered TETRA systems (Switching and management Infrastructures).
Similar requirement as defined for P25/TETRA.
3
[R-6.18.3.2-003] Interworking between the MCPTT Service and P25 shall support interoperable MCPTT Group Calls between MCPTT Users and P25 subscriber units and consoles.
[R-6.18.3.3-003] Interworking between the MCPTT Service and TETRA shall support interoperable MCPTT Group Calls between MCPTT Users and TETRA mobile stations and consoles.
Similar requirement as defined for P25/TETRA.
4
[R-6.18.3.2-004] Interworking between the MCPTT Service and P25 shall support interoperable MCPTT Emergency Group Calls and P25 emergency calls.
[R-6.18.3.3-004] Interworking between the MCPTT Service and TETRA shall support interoperable MCPTT Emergency Group Calls and TETRA emergency calls.
Similar requirement as defined for P25/TETRA.
5
[R-6.18.3.2-006] Interworking between the MCPTT Service and P25 shall provide a means for an authorized user to initiate an override of a PTT Group call between MCPTT Users and P25 subscriber units and consoles.
[R-6.18.3.3-006] Interworking between the MCPTT Service and TETRA shall provide a means for an authorized user to initiate an override of a PTT Group call between MCPTT Users and TETRA mobile stations and consoles.
Similar requirement as defined for P25/TETRA.
6
[R-6.18.3.2-007] The MCPTT Service shall provide a mechanism for an MCPTT Administrator to authorize an MCPTT User to be able to initiate an override of a PTT Group call between MCPTT Users and P25 subscriber units and consoles.
N/A
Similar requirement as defined for P25.
7
[R-6.18.3.2-008] Interworking between the MCPTT Service and P25 shall provide a means for an authorized P25 subscriber units and consoles to initiate an override of a PTT Group call between MCPTT Users and P25 subscriber units and consoles.
[R-6.18.3.3-007] Interworking between the MCPTT Service and TETRA shall provide a means for an authorized TETRA mobile station or console to initiate an override of a PTT Group call between MCPTT Users and TETRA mobile stations and consoles.
Similar requirement as defined for P25/TETRA.
8
[R-6.18.3.2-009] The MCPTT Service shall provide a mechanism for an MCPTT Administrator to authorize a P25 subscriber unit or P25 console to be able to initiate an override of a PTT Group call between MCPTT Users and P25 subscriber units and consoles.
N/A
Similar requirement as defined for P25.
9
[R-6.18.3.2-021] For Private Call (with Floor control) interworking, between the MCPTT Service and non-3GPP PTT systems that do support Private Call override (e.g., Project 25 Phase 2 systems), the MCPTT Service shall provide a mechanism for Participants to override an active MCPTT transmission of a transmitting Participant when the priority level of the overriding Participant is ranked higher than the priority level of the transmitting Participant.
[R-6.18.3.3-014] For Private Call (with Floor control) interworking, between the MCPTT Service and non-3GPP PTT systems that do support Private Call override, the MCPTT Service shall provide a mechanism for Participants to override an active MCPTT transmission of a transmitting Participant when the priority level of the overriding Participant is ranked higher than the priority level of the transmitting Participant.
Similar requirement as defined for P25/TETRA.
10
[R-6.18.3.2-010] Interworking between the MCPTT Service and P25 shall support Group Regrouping that includes both MCPTT Groups and P25 groups.
[R-6.18.3.3-008] Interworking between the MCPTT Service and TETRA shall support Group Regrouping that includes both MCPTT Groups and TETRA groups.
n/a
11
[R-6.18.3.2-011] Interworking between the MCPTT Service and P25 shall support User Regrouping that includes both MCPTT Users and P25 subscriber units.
[R-6.18.3.3-009] Interworking between the MCPTT Service and TETRA shall support User Regrouping that includes both MCPTT Users and TETRA mobile stations.
Similar requirement as defined for P25/TETRA.
12
[R-6.18.3.2-013] Interworking between the MCPTT Service and P25 shall support interoperable User IDs and P25 subscriber IDs.
[R-6.18.3.3-010] Interworking between the MCPTT Service and TETRA shall support interoperable User IDs and TETRA IDs.
Specific requirement in addition to those for P25/TETRA is needed: MCPTT service shall support use of the PDT ID and PDT numbering scheme, enabling a unified user identity for interworking between MCPTT Service and PDT system.
13
[R-6.18.3.2-014] Interworking between the MCPTT Service and P25 shall support interoperable PTT Private Calls (with Floor control) between an MCPTT User and a P25 subscriber unit or console.
[R-6.18.3.3-011] Interworking between the MCPTT Service and TETRA shall support interoperable PTT Private Calls between an MCPTT User and a TETRA mobile station or console.
Additional requirement to those for P25/TETRA: supporting interworking of Private Calls without floor control.
14
[R-6.18.3.2-005] Interworking between the MCPTT Service and P25 shall support end-to-end encrypted MCPTT Group Calls between MCPTT Users and P25 subscriber units and consoles.
[R-6.18.3.3-005] Interworking between the MCPTT Service and TETRA shall support end-to-end encrypted MCPTT Group Calls between MCPTT Users supporting the TETRA voice codec and end-to-end encryption and TETRA mobile stations and consoles.
Similar requirement as defined for P25/TETRA, with “MCPPT Group Calls” updated to “PTT Group Calls”.
15
[R-6.18.3.2-016] Interworking between the MCPTT Service and P25 shall support end-to-end encrypted PTT Private Calls (with Floor control) between an MCPTT User and a P25 subscriber unit or console.
[R-6.18.3.3-012] Interworking between the MCPTT Service and TETRA shall support end-to-end encrypted PTT Private Calls between an MCPTT User supporting TETRA codec and encryption and a TETRA mobile station or console.
Additional requirement to those for P25/TETRA: supporting interworking of Private Calls without Floor Control.
16
[R-6.18.3.2-017] Interworking between the MCPTT Service and P25 shall support a means of reconciling codecs between interoperable calls.
[R-6.18.3.3-013] Interworking between the MCPTT Service and TETRA shall support a means of reconciling codecs between interoperable calls when not end-to-end encrypted.
Similar requirement as defined for P25/TETRA.
17
[R-6.18.3.2-012] Interworking between the MCPTT Service and P25 shall support interworking of Group-Broadcast Group Calls and P25 announcement group calls.
N/A
Similar requirement as defined for P25.
18
[R-6.18.3.2-015] Interworking between the MCPTT Service and P25 shall provide a mechanism to reconcile the Private Call (with Floor control) commencement mode between an MCPTT User and a P25 subscriber unit or console.
N/A
Similar requirement as defined for P25.
19
[R-6.18.3.2-018] Interworking between the MCPTT Service and P25 shall support conveyance of Losing audio from P25 subscriber units and consoles to authorized MCPTT Users.
N/A
N/A
20
[R-6.18.3.2-019] The MCPTT Service shall provide a mechanism for an MCPTT Administrator to authorize MCPTT Users to be able to receive Losing audio from P25 subscribers units and consoles.
N/A
N/A
21
[R-6.18.3.2-020] For Private Calls (with Floor control) interworking between the MCPTT Service and non-3GPP PTT systems that do not support Private Call override (e.g., Project 25 Phase 1 systems), the Participant attempting to override shall be notified that the override can not be accomplished.
N/A
N/A
22
N/A
[R-7.1-001] Interworking between the MCData SDS and TETRA Short Data Service shall be supported.
Similar requirement as defined for TETRA.
11.14.6 Potential New Requirements needed to support the use case
[PR 11.14.6-1]: The 6G system shall support MCX service continuity during UE mobility across inter-PLMN, as well as between PLMN and PSBN, subject to regulatory requirements, inter-operator agreements and capabilities (e.g. agreed Quality, Priority and Pre-emption parameter settings).
NOTE 1: (PSBN is a network generally owned and operated by a public safety network operator and specially designed for public safety users (e.g. police, ambulance, fire service), having their own PLMN identity.
[PR 11.14.6-2]: The 6G system shall minimize interruption to ongoing MCX service during UE mobility across different access (e.g. terrestrial access and satellite access), and/or when MCX service switch between on-network and off-network operations with or without UE relay through UAVs, HAPS, and Satellites access.
NOTE 2: The requirement above may include also ProSe-enabled access, where relay UEs are equipped with co-located routing functionality, capable of maintaining connectivity in dynamic and infrastructure-less environments.
[PR 11.14.6-3]: The 6G system shall enable an MCX UE to determine its position using hybrid positioning methods, including network-assisted GNSS via satellite access, to improve availability and resiliency, and to convey its position information to the MCX server, triggered by an event, or periodically.
[PR 11.14.6-4]: The 6G system shall support the MCPTT Service to enable interworking with non-3GPP PTT Systems that are compliant with the PDT standards, in alignment with P25 and TETRA.
[PR 11.14.6-5]: The 6G system shall support interworking between the MCData SDS and PDT Short Data Service, in alignment with TETRA.
11.15 Use case on service robots in smart community
11.15.1 Description
Continuing Care Retirement Community (CCRC) is the community that provides continuing care for the elderly. There are several types of assistance and care required in the community. There are several types of assistance and care required in the community. First is the independent living, in which residents can take care of themselves and require limited assistance. Second is the assisted living, in which residents need help with daily tasks such as bathing and dressing. Third is the 24-hour nursing home care, in which residents usually have a dedicated skilled nursing facility.
The service robots within the community could help to maintain and improve the liveability of the community. CCRC requires a lot of labour to take care of the residents, therefore, service robots could help by liberating the work from human beings.
11.15.2 Pre-conditions
Service robots (UEs) are equipped with cameras or sensing entities that could record the surroundings or execute sensing service. The service robots could record the videos or take photos of the surroundings and then send the video or photo to the server for processing and detection of the potential crime.
The service robots might not have the computation and resources to process the gathered information by themselves, in order to have rapid action on the potential crime, the video or photo could be uploaded to the local edge server for processing.
Service robots are equipped with gesture and natural language recognition capability. Service robots could deliver the groceries or parcels to customers.
11.15.3 Service Flows
Case#1 Patrol robots for crime prevention and medical assistance:
Patrol robots could help with the community security and personnel security, such as detecting the fallen elderly around the community for a quick rescue. There could be UAV patrol robots and patrol robots on the ground as shown in the following figure.
The cellular coverage usually doesn’t cover the whole community, as shown in Figure 11.15.3-1. Relays could be placed within the community to extend the cellular coverage for ensuring the communication of the patrol robots, for example, placing them with the surveillance cameras, which are assumed to be well located within the community.
Figure 11.15.3-1: Patrol robots in the CCRC
Daily patrolling:
1. When the patrol robot is in weak cellular coverage, it automatically switches to the indirect network connection. It could connect to the base station via the stationary relays that are placed with the surveillance camera. When it detects the connection to the base station is well enough, it would switch to the direct network connection. When the patrol robots send the surveillance video to the control centre, they could establish multiple indirect network connection paths.
2. The patrol robot would take videos or photos when it detects the security event from its sensing unit and send to the control centre.
3. During the daily patrolling, the patrol robots might detect the risk to the community safety (e.g. the thief) and the patrol robots will track the thief and report the real time location to the security department. According to the report from the patrol robots, the security department might provide tracking assistant information and tracking instructions to the patrol robots from its surveillance system. The patrol robots are moving around the community, and the connection paths might be changed during the movement of the patrol robots. Service continuity is required for robots to get clear instructions for tracking with multiple indirect network connection paths.
Medical assistance event:
Figure 11.15.3-2: Medical assistance event with patrol robots
1. When an elderly person feels uncomfortable at home, he/she could ask for medical assistance from the control centre of the CCRC by pressing a bottom in his/her home. As depicted in Figure 11.15.3-2, the control centre would dispatch the nearest patrol robots for measuring and monitoring the vital signs of the elderly, such as heart rate, blood pressure and blood oxygen by connecting with the sensing devices.
2. In order to dispatch the nearest patrol robot, the control centre should obtain the location information of the patrol robots, and the patrol robots should be equipped with dynamic path planning to find the nearest path. Communication between the control centre and the patrol robots could be direct network connection or indirect network connection.
3. The patrol robot gets a temporary authorization to connect to the elderly personal smart devices to monitor his/her health condition.
4. When the patrol robot arrives, it connects with the smart watch that the elderly is wearing or smart devices at home with sensing capabilities, which could monitor the vital signs. The vital signs of the elderly could be transmitted to the first-aider via the smart watch and the patrol robot. Also, the patrol robot is equipped with camera that could capture the live video of the elderly for health monitoring. The live video from the camera and the vital signs transmission should be synchronised.
5. Once the first-aider arrives, the first-aider could operate the medical treatment immediately.
Case#2 Natural language or gesture recognition from the service robots:
Residents could use the service robots with natural language processing capability for smart home appliances, e.g. smart refrigerators, smart speakers, and smart washing machines. Additionally, the gesture recognition capability of the service robots could also help with the remote control of smart home appliances. Natural language and gesture recognition requires computation capabilities on the service robots, and the traffic of voice or video could be offloaded to the operator managed data network for processing to meet the low latency requirement.
Case#3 Smart transportation for delivery robots:
Smart delivery route planning could be based on the local map of the community. The local map might include the privacy information of the resident, which could be stored at the local edge server for privacy consideration.
NOTE: Depending on regional or national laws, a delivery robot is considered in a different ways: (a) if they are considered as a vehicle (e.g. in South Korea) they should follow the rules that a regular road vehicle does and some or all of V2X-related aspects are suitable for service scenarios; (b) if they are considered as a vulnerable road user under certain conditions (e.g. speed limit, weight limit), they are allowed to operate in sidewalks (e.g. in State of Pennsylvania).
11.15.4 Post-conditions
The safety of the community and personnel safety could be ensured. Service robots could help with natural language or gesture recognition and delivery to customers in an efficient way.
11.15.5 Existing features partly or fully covering the use case functionality
Requirements on positioning, management of a PIN, efficient user plane are specified in TS 22.261 [14] clauses 6.5, 6.27.2, and 6.38.2.2.
Requirements on V2X advanced driving and remote driving are specified in TS 22.186 [66] clauses 5.3 and 5.5.
There are requirements for multiple paths indirect network connection in TS 22.261 [14] clause 6.9. The requirement on service continuity of multiple paths indirect network connection has been identified but does not apply to a traffic flow of a remote UE using different indirect network connection paths.
The 5G system shall be able to maintain service continuity of indirect network connection for a remote UE when the communication path to the network changes (i.e. change of one or more of the relay UEs, change of the gNB).
NOTE: It does not apply to a traffic flow of a remote UE using different indirect network connection paths.
11.15.6 Potential New Requirements needed to support the use case
[PR 11.15.6-1] While the remote UE is using multiple indirect network connection paths for a single traffic flow, the 6G system shall be able to maintain service continuity when the communication path(s) to the network changes, e.g. when existing path is released and a new path is added.
11.16 Use case on critical infrastructure monitoring
11.16.1 Description
According to [229], given the worsening and more frequent occurrence of natural disasters, it is imperative to continue to pursue strategies for developing critical infrastructures that directly contribute to national resilience, such as disaster risk reduction and mitigation and countermeasures for aging infrastructures. To achieve this, appropriate and efficient maintenance of critical infrastructures is crucial. There are two types of maintenance: corrective maintenance and predictive maintenance. Corrective maintenance is performed after critical infrastructures have been damaged, deteriorated, or has lost their function or performance. On the other hand, predictive maintenance involves planned inspections, repairs, and replacements of critical infrastructures to prevent a decline in function or performance during use. To reduce overall cost, to improve safety, to increase reliability, and to extend lifespan of critical infrastructures, predictive maintenance becomes important.
In predictive maintenance, it is necessary to continuously monitor the health (current performance) of critical infrastructures and evaluate them. To monitor the health of critical infrastructures, the monitoring of displacement is important. Displacement is the term in construction sector which means the movement or shift of a structure or its components due to loads, forces, or environmental conditions. Conventionally, monitoring critical infrastructures is performed manually, such as visual and sound inspection. Recently, methods of monitoring critical infrastructure using ICT are attracting attention.
Critical infrastructure monitoring is a generic term for techniques used to objectively and continuously monitor changes in the condition of structures and buildings. Two types of results are utilised to monitor critical infrastructure: sensing results and imaging results. Sensing results are gathered by non-3GPP sensors attached to critical infrastructure (such as temperature, humidity, acceleration and strain) and sent by communication devices attached to critical infrastructure. Note that the communication device serves not only as a device to send results from non-3GPP sensors, but also as a device to measure precise time and position by 3GPP network and attach precise positioning and timestamp information to sensing results and also as a device to obtain displacement by measuring positional variation precisely. Imaging results are gathered by surveillance cameras, drones and robots and sent by communication devices attached to surveillance camera, drones and robots. Note that the communication device serves not only as a device to send results from surveillance cameras, drones and robots, but also as a device to measure time and precise position by 3GPP network and attach precise positioning and timestamp information to imaging results. These results are mapped onto critical infrastructure in cyberspace and then diagnosed and predicted structural damage and deterioration. Therefore, to consistently utilize huge amounts of results from many sensors, cameras, drones and robots, precise positioning results and timestamp information are required. And, to measure displacement of critical infrastructure, precise displacement information is essential.
11.16.2 Pre-conditions
Company A is entrusted with the management and maintenance of bridge B and building C. Non-3GPP sensors (e.g. temperature, humidity, acceleration and strain) and surveillance cameras are attached on multiple critical locations of bridge B and building C in terms of critical infrastructure monitoring, as shown in Figure 11.16.2-1. Communication devices are mounted beside the non-3GPP sensors and the surveillance cameras, connected to the non-3GPP sensors and the surveillance cameras via cable, and stored in environmentally resistant boxes with long-life battery. Mobile Operator D hsa installed a base station covering bridge B and building C and communicates with Company A’s control centre.
Figure 11.16.2-1: Critical Infrastructure Monitoring
11.16.3 Service Flows
1. The non-3GPP sensors gather sensing results, and the surveillance cameras gather imaging results. The communication devices measure time and position by 3GPP network. Also, the communication device measures displacements by measuring the change in position of the communication device or by measuring the change in distance between the communication device and the base station.
2. The communication devices collect the sensing results from the non-3GPP sensors and the imaging results from the surveillance cameras via cable, stores these results to memory, and adds precise positioning results and timestamp information to these results.
3. The communication devices process the stored results of the sensing results, the imaging results and the positioning results to make the summary periodically and report the summary to Company A’s control centre via the 3GPP network.
4. After large event (e.g. earthquake or typhoon), Company A’s control centre may request the communication devices to send full results. In this case, the communication devices retrieve full results from their memory and send to Company A’s control centre.
5. Company A’s control centre analyses the reported results, e.g. by comparing the story drift information calculated from the communication device’s displacement results with threshold values calculated from the country’s construction regulations, such as [230]. There are two types of analyses. The one is to diagnose aging deterioration by measuring long-term displacement. The other is to diagnose the degree of destruction by measuring the magnitude of shaking during earthquakes, typhoons, etc.
11.16.4 Post-conditions
Company A can develop an appropriate plan of maintenance of bridge B and building C.
11.16.5 Existing features partly or fully covering the use case functionality
In [64] clause 7.3.2.2, it is described that the 5G system shall be able to provide positioning services with the performances requirements reported in Table 7.3.2.2-1, and in Table 7.3.2.2-1, it is described that horizontal accuracy 0.3m with 99% availability is required. Also, the 5G system supports accurate positioning capability with aid of Positioning Reference Unit. Using Positioning Reference Unit, cm-level accuracy is achieved, but it is mainly for indoors. However, cm-level accuracy of positioning and displacement in outdoor is required in the use case, especially the use case of measuring displacement of critical infrastructure.
11.16.6 Potential New Requirements needed to support the use case
[PR 11.16.6-1] The 6G system shall be able to support the following KPIs.
Table 11.16.6-1: KPIs for critical infrastructure monitoring
Scenario
Positioning Accuracy
(95 % confidence level)
Positioning service availability
Positioning service latency
Environment of use
UE speed
UE type
Horizontal Accuracy
Vertical Accuracy
Critical Infrastructure Monitoring
[10] cm
(note 1)
[1] m
[99%]
[100]ms
(note 2)
Outdoor
stationary
Infrastructure mounted
Note 1: Assuming the UEs are installed at each story of a building with story height of 4m.
Note 2: Assuming the frequency of an earthquake is 10 Hz.
11.17 Use case on remote and automatic construction
11.17.1 Description
According to [229], the use of ICT in the construction industry has become a pressing issue. Many of the tasks are performed outdoors and usually involve many workers and organizations in various construction processes. Work planning, management, and safety are also important issues because workers operate machinery such as trucks and heavy construction equipment and handle hazardous materials like chemicals and heavy materials. Extensive use of ICT to monitor workers and construction equipment, remotely control heavy equipment, and automate site operations will enable sustainable and efficient construction operations and site safety. These ICT-related initiatives are collectively referred to as “Construction 4.0” [361].
The future construction sector is expected to introduce innovative technologies, such as advanced telecommunication, AI, robots, and cloud computing, into the construction and infrastructure domains, and to take initiatives such as on-site verification of uncrewed construction technologies, technologies to support construction site workers, and labour-saving supervision and inspection using video data. Uncrewed construction using 3GPP systems with “high speed and high capacity,” “multiple connectivity,” and “low latency” capabilities will be effective in improving productivity in the construction sector.
The possible applications of remote and automatic construction are as follows:
1. Remote installation with cooperation of experienced technical workers – On-site workers use VR technology to perform installation work with the assistance of experienced technical workers located in remote locations.
2. Remote installation by experienced technical workers - Installation by remote operation of construction machinery or robots with using haptics and VR technologies by experienced technical workers.
3. Uncrewed construction, in which construction machinery and robots carry out the construction and building process;
- Autonomous-driving vehicles carry equipment on behalf of workers,
- Automatic construction machinery carry out the installation, and
- Robots perform the installation automatically.
4. Construction harmonized physical space and cyberspace - Design is carried out in cyberspace and reflected to the actual construction. Actual construction in physical space is proceeded under the virtual construction in cyberspace. Any issues in the preparation of construction materials and in the site environment are identified while non-3GPP environmental sensors detect temperature, humidity, gas emission, etc. Appropriate measures are taken to those issues.
RTK-GNSS (Real-Time Kinematic positioning) is a technique used to enhance the precision of position data derived from GNSS by combining the measurement result of a target and that of reference station [231]. RTK-GNSS is widely used in construction sector. RTK-GNSS can provide cm-level accuracy in open-air environments but can hardly be used in NLOS situations like urban canyons. There is a risk of jamming and spoofing.
11.17.2 Pre-conditions
Construction contractor A is contracted to perform construction work at construction site B with stakeholder C. Company A's ICT construction machinery and UAVs are equipped with 3GPP communications devices, as shown in Figure 11.17.2-1. Mobile carrier D installs base stations to cover construction site B.
Figure 11.17.2-1: Remote and Automatic Construction
11.17.3 Service Flows
1. Surveying – Construction contractor A quickly and accurately assesses the situation at construction site B before construction, by using UAVs equipped with 3GPP communication devices, cameras, laser scanners, etc. 3GPP communication devices are used for communication and measurement of positioning.
2. Design – Construction contractor A creates 3D design data based on the survey data of construction site B and makes plans of facilities.
3. Construction - Construction contractor A performs construction work at construction site B using the following two types of ICT construction machinery equipped with 3GPP communication devices and non-3GPP sensors, based on real time measurement and analysis of site conditions and the 3D design data. 3GPP communication devices are used for communication and measurement of positioning.
- Machine Guidance – ICT construction machinery are operated by human operators.
- Machine Control – ICT construction machinery are operated semi-automatically.
4. Construction Management – After completion of construction, construction contractor A accurately assesses the situation of construction site B after construction, by using UAVs equipped with 3GPP communication devices, cameras, laser scanners, etc., to confirm whether the construction has been carried out as designed.
5. Delivery – Construction contractor A shares the measurement data of construction site B with stakeholder C.
11.17.4 Post-conditions
Construction contractor A completes construction work at construction site B with higher quality, fewer people, and more safety.
11.17.5 Existing features partly or fully covering the use case functionality
In 3GPP, using Positioning Reference Unit, cm-level accuracy is achieved, but it is for indoors. However, cm-level accuracy in outdoor locations is required in the use case, especially the use case that multiple ICT construction machinery work collaboratively.
11.17.6 Potential New Requirements needed to support the use case
[PR 11.17.6-1] The 6G system shall be able to support the following KPIs utilising the 3GPP and non-3GPP positioning technologies.
Table 11.17.6-1: KPIs for remote and automatic construction
Scenario
Positioning Accuracy
(95 % confidence level)
Positioning service availability
Positioning service latency
Environment of use
UE speed
UE type
Horizontal Accuracy
Vertical Accuracy
Machine Guidance, Machine Control
[2-10] cm
(note)
[10] cm
[99%]
[30] s
Outdoor
[5] km/h
Machinery mounted
Note: Assuming the similar performance as RTK-GNSS [231].
11.18 Use case on regulated services resiliency in disaster conditions
11.18.1 Description
There are three different existing features in 3GPP that could be considered together, in order to achieve additional societal gains in 6G.
1. Rel-17 Feature: MINT [14] defines a Disaster Condition and a way that MNOs can offer (inbound roaming) service for other PLMNs where other PLMNs’ RAN infrastructure cannot function.
2. Rel-18 Feature: Smart Energy Infrastructure (SEI) [64] and Network and Service Operations for Energy Utilities (NSOEU) [232] Defines a means by which, in the event of an energy outage, MNOs and energy utility Distribution Service Operators (DSOs) can coordinate recovery to reduce downtime in both the mobile telecommunication system and energy distribution system.
3. Regulatory Services – PWS [62], emergency call [58] specify services that all UEs and networks support, even in limited service state. These regulatory services provide fundamental public safety communication between government agencies and the public.
This use case considers a new functional capability for the 6G system, to provide resilient operation of regulatory services in the event of a Disaster Condition that affects energy service.
In SEI [64], further developed in NSOEU [232], (see the detailed use case in [233], clause 6.2) an energy utility obtains information from an MNO concerning their ‘energy supply ID’ associated with critical mobile network infrastructure, especially base stations.
Figure 11.18.1-1: Energy utility and telecommunication coordinated recovery of energy service
In Figure 11.18.1-1, the switch between Distribution Substation 4 and Distribution Substation 5 is connected (an operation that requires some time and care in the distribution grid), restoring energy supply to Distribution Substation 3. Then it is possible for Distribution Substation 3 to rapidly restore energy distribution to Energy Supply 2 to keep B operating without downtime.
The standard that supports this coordinated recovery process [64] benefits both the MNO (so there is no interruption of service) and also the DSO (because distribution substation remote operations can be completed in seconds or minutes, whereas sending a technician takes hours.) Thus, both the energy system and telecommunication system achieve higher availability.
11.18.2 Pre-conditions
In this use case we consider the following:
- The DSO provides energy service to all PLMNs operating in a given area using NSOEU.
The actors are those responsible for network operations at an energy utility “DSO” and mobile operators “PLMN-1” and “PLMN-2.”
11.18.3 Service Flows
PLMN-1 and PLMN-2 provide information as defined in NSOEU as defined in TS 28.318 [232] to the DSO concerning their critical infrastructure for providing service, including cell sites, data centres, etc. to the DSO. This information includes also the autonomous energy supply capacity (in minutes), the cell ID (for the case that the critical infrastructure is specific to serving a particular cell ID) and other information.
In this use case, in addition to this information, additional information is provided:
- The service area for support of regulator services, served by the critical infrastructure. For example, this service area could be one or more specific physical regions, e.g. a metropolitan area, or region of a country, bounded geolocations, etc.
Figure 11.18.3-1: Service Area for Regulated Services
In Figure 11.18.3-1 above, there is a disaster in the red region on the physical map resulting in an energy outage. The regulatory service coverage area is known for both PLMN-1 and PLMN-2. In the event of an energy outage, it is necessary that within this area there is service in at least one PLMN in all locations. It is clear from the projection of the disaster area onto the regulatory service coverage area, that in PLMN-1 the service area is served by PLMN-2 by E and PLMN1 by A and C. Due to the disaster, both A and E are without power. This means, using mechanisms defined in NSOEU either A or E need to have their service restored before autonomous energy capacity is exhausted. Unlike in SEI and NSOEU, there is the added consideration to preserve regulatory service availability.
It may be necessary to prioritize restoration of energy services to MNO infrastructure that serve DSO Distribution Substations as the first priority, so that energy service is possible at all in the region. Restoration of MNO infrastructure to support regulatory services can then be the second highest priority, for example.
11.18.4 Post-conditions
The result of this operation will be improvement of service availability of regulatory services in the event of a disaster (such as an earthquake, tsunami or other heavy storm, fire, etc.)
3GPP standards can enable this operation by enhancing the information provided as part of NSOEU to include regulatory service coverage areas associated with specific MNO infrastructure.
11.18.5 Existing features partly or fully covering the use case functionality
There are no features to support the functionality described in this use case. The requirements build upon those in TS 22.104 [64] for coordinated energy and mobile communication service recovery.
- NSOEU is enhanced to include information concerning which infrastructure elements (e.g. base stations) provide emergency services in which area (we will term this the regulatory service coverage area.)
- There is a disaster condition that affects the area, causing an energy outage that affects the ability of the DSO to provide energy distribution to some infrastructure elements.
Unlike in NSOEU, the objective is not to restore telecommunication service crucial to the support of DSO infrastructure as quickly as possible. This remains an objective, but in this use case we consider an additional consideration: prioritisation of restoration of critical infrastructure essential to provide regulatory services.
11.18.6 Potential New Requirements needed to support the use case
[PR 11.18.6-1] Subject to regulatory requirements and operator policy, the 6G system shall support a means for an MNO to improve availability for regulated services (e.g. PWS [62], emergency call [58]) despite a lack of energy supply, through exchange of information with a DSO.
11.19 Use case on network-requested execution of service functions in connected vehicles
11.19.1 Description
In this use case, an authorized vertical user (e.g. road operator, transport authority) requests the 6G system to deploy and execute a service function (e.g. tasks) in one or more connected vehicles located in a specific area. These service functions are temporary and executed only for the duration and purpose of the request. They can be deployed and executed only in vehicles that have indicated support for this capability.
For example, in the case of a severe car accident, a transport authority may request the 6G network to deploy and execute a service function in vehicles located near the accident. The purpose of this function may be to collect situational data and assist in building real time awareness of road and traffic conditions.
After the service function is deployed and executed in a vehicle, it may collect data such as:
• GPS location, speed, and heading,
• LiDAR or camera sensor metadata,
• Proximity to the accident zone or road obstructions,
• Other relevant environmental or telemetry data.
The service function in a vehicle may periodically transmit this data to the vertical user’s server through the 6G network. This data can be used by the transport authority to reconstruct the event timeline, monitor traffic congestion buildup, identify potential risks, and coordinate emergency response actions more effectively. It may also be used to support other applications such as updating navigation guidance, informing traffic broadcasts, or initiating adaptive traffic control measures in surrounding areas.
11.19.2 Pre-conditions
- The connected vehicles are registered with the 6G system and have indicated support for executing network-requested service functions.
- The vertical user is authorized to request service function execution (e.g. through a service exposure interface).
- The 6G network can determine the location of connected vehicles and assess their availability and suitability based on predefined metrics (e.g. capability, battery level, load).
11.19.3 Service Flows
A vertical user initiates a request to the 6G network to deploy and execute a service function in vehicles located in a target area.
The 6G network identifies connected vehicles in this area that are eligible and available to execute the requested function.
The 6G network sends a request to the selected vehicles to deploy and execute the service function. This request indicates also where the service function is stored and how it can be downloaded.
Each vehicle that receives (and accepts) the request downloads the service function (e.g. from a network repository) and executes it locally. User consent may be requested before a service function is accepted and executed in a vehicle.
The service function begins collecting sensor data (e.g. GPS location, speed, etc.).
The data is transmitted periodically or on event basis from the service function to a vertical user’s server via the 6G network.
The service function may be terminated in a vehicle either automatically (e.g. after a predefined period) or explicitly by the vertical user or by the 6G system.
11.19.4 Post-conditions
- The service function is terminated (and possibly removed) from each vehicle.
- The collected data has been transmitted to the vertical user’s system for further processing or action.
- The 6G network may log the participation of vehicles in the service for auditing or service tracking purposes.
11.19.5 Existing features partly or fully covering the use case functionality
Existing specifications (e.g. TS 22.261 [14] and TS 22.186 [66]) support the concept of service exposure to verticals and enhanced V2X services. They also include support for communication between vehicles and edge applications.
However, existing specifications do not fully support the dynamic, network-requested execution of temporary service functions in vehicles.
11.19.6 Potential New Requirements needed to support the use case
[PR 11.19.6-1]: Subject to operator policy, regulatory requirements and user consent, the 6G network shall provide suitable APIs to allow authorized third parties to collect data from UEs (e.g. connected vehicles) that are located in a specific area and are capable of collecting data upon network request.
[PR 11.19.6-2]: Subject to operator policy, regulatory requirements and user consent, the 6G network shall enable UEs (e.g. connected vehicles) to indicate whether they can collect data upon network request.
11.20 Use case on network managed localized communication for verticals
11.20.1 Description
One of new capability is to establish one or more local networks which are managed by one Network operator (can be PLMN or NPN), while the 3GPP UEs in the same local network can communicate with each other via localized 6G network functions which are closer to the UE, with authentication and authorization by 6G network functions which can reside in a centralized location and further away from UEs.
Support indirect communication among 3GPP UEs using 6G radio interface and local traffic breakout via local CN function can help offloading Local services to local network to reduce load for centrally located CN nodes (Higher Experienced Rate and Connection Density), and local traffic management provides better performance of latency and mobility (Stricter latency and mobility requirement).
The proposed localized communication in a local network may have a wider coverage which can be used for communication of 3GPP devices belonging to a user, a family, or used for a CPN for a factory, residence, office or shop). It may also achieve the same level of transmission latency as direct device connection due to the communication is via a 6G network while 3GPP UE need not additionally support direct device connection.
11.20.2 Pre-conditions
Factory-A wants to wirelessly connect its devices with a very low latency (e.g 10ms).
Mobile operator-B provides the local traffic transmission service, where the 3GPP devices can perform local traffic transmission within the local network coverage area via a 6G access network and core network function in the local network.
11.20.3 Service Flows
When Factory-A wants to support communication with very low latency, it applies to Mobile operator-A with their 3GPP devices for local traffic communication. Based on SLA, the certain 3GPP devices are authenticated and authorized by Mobile operator’s core network and perform the traffic transmission with ultra-low latency.
The mobile operator can provide certain QoS guarantee fr the local traffic transmission, which fully satisfied the Factory’s requirement.
11.20.4 Post-conditions
Thanks to the network managed local traffic transmission, Factory A can realize the local transmission among their devices with ultra-low latency.
11.20.5 Existing features partly or fully covering the use case functionality
In TS 22.261 clauses 3.1 and 6.38, already has the service requirement of PIN. But PIN is a much smaller scope and it’s tied to individual customers and exclusive for PLMN, such as “CPNs and PINs have in common that in general they are owned, installed and/or (at least partially) configured by a customer of a public network operator”. PIN in 6G can be extended to bigger scale as indicated in the use case.
The network managed localized communication assumes the two 3GPP UEs communicate to each other via 3GPP radi/Uu supported by 6G network
PIN supports local inter-PIN element communication using direct network connection only if the PIN elements are in the range to use PIN direct connection, as indicated in TS 22.261 [14]. clause 6.38.1 “Via a PIN Element with Gateway Capability, PIN Elements have access to the 5G network services and can communicate with PIN Elements that are not within range to use PIN Direct Connection.”
For this network managed local traffic transmission, 3GPP UE need not additionally support direct device connection.
11.20.6 Potential New Requirements needed to support the use case
[PR 11.20.6-1] Subject to operator policy and user consent, the 6G system shall support the communication between two UEs via direct network connection in a local network with fulfilling the following KPIs:
Table 11.20.6-1: Network managed localized communication for verticals
Scenario
Experienced data rate
(Note 1)
Latency
(UL/DL)
Factory
[≤ 1.5 Gbit/s]
(Note 1)
UL: [5] ms
DL: [5] ms
(Note 1)
Vehicle
[0,1 to [1] Gbit/s]
(Note 2)
UL: [5] ms
DL: [5] ms
(Note 2)
Note 1: Refer to the KPI requirement in TS 22.261 [14] Table 7.10.2-1.
Note 2: Refer to the performance of Gaming or Interactive Data Exchanging in TS 22.261 [14] Table 7.6.1-1
11.21 Use case on Industrial IoT
11.21.1 Description
Network slicing allows operators to create multiple virtual networks (slices) on top of a shared physical infrastructure. Each slice can be optimized for specific use cases or customer requirements. SLA assurance for these slices is crucial for maintaining quality of service and meeting customer expectations. Here the SLA may be defined in terms of guaranteed minimum throughput, energy efficiency, maximum latency and reliability metrics. For industrial scenario, dedicated network slices can be deployed which requirs ultra-low latency, high reliability, and guaranteed bandwidth to ensure the safety and success of the industry. The SLA of industry slice may be mapped to various parameters, such as resource type, QoS parameters and slice specific metrics. Network should be aware of the slice specific requirements and take this information into account to make resource allocation decisions for each slice.
From the perspective of third party, it is desired to monitor the slice level performance. For example, the third party should be able to detect the slice level latency, radio resource usage, and reliability. Here the slice level latency and reliability is the average latency or reliability of the user traffic belonging to a given slice. The slice level radio of the Physical Resource Block (PRB) usage denotes the ratio of PRB allocated for a given slice.
If the network performance is approaching SLA thresholds, it may automatically request additional resources from the network. On the other hand, the network performance data can be analyzed using AI model to predict potential issues. If a possible future SLA breach is predicted, it may proactively fine-tune its industry control system or requests resource adjustment. For example, some adjustment on bandwidth or computing resources can be made to maintain performance of this slice.
Based on the slice performance information exposure and slice resource adjustment, precise slice performance assurance for industrial scenarios can be ensured.
11.21.2 Pre-conditions
A seaport uses a combination of autonomous cranes, self-driving trucks, and AGVs to handle cargo. The 6G system has been utilized to enhance the seaport’s operations. The 6G system is operated by Operator A.
11.21.3 Service Flows
According to the characteristics of seaport, different network slices for different purposes are deployed. For example, URLLC slice for real time control of autonomous vehicles and cranes, mMTC slice for IoT sensors monitoring of cargo and environmental conditions, eMBB slice for high-resolution video surveillance and augmented reality applications used by human operators, sensing slice for precise positioning and collision avoidance of vehicles.
The seaport system continuously monitors the network performance of these slices.
The seaport is experiencing a surge in activity due to the arrival of several large container ships. Simultaneously, foggy weather conditions are reducing visibility which affects the operation of autonomous vehicles. Therefore, increased latency due to network congestion is detected in the URLLC slice and reduced accuracy due to foggy conditions is detected in the sensing slice. Meanwhile, higher demand on the eMBB slice is expected as human operators rely more on video feeds.
Based on the real time network performance monitoring and AI analysis, the seaport system determines to adjust the slice specific parameters/configurations, e.g. increase spectrum allocation by 25%, adjust the ratio of PRBs allocated for different slices, adjust beamforming patterns to focus on areas with autonomous vehicle activity, adjust sensor reporting intervals based on criticality and current port activity, prioritize AR data for critical operational areas, etc.
After the adjustment of slice specific parameters/configurations, URLLC latency stabilizes at 1 ms with 99.9999% reliability. Sensing slice maintains vehicle positioning accuracy despite fog. eMBB slice manages to support increased video usage without significant quality degradation.
The seaport system continuously learns from the outcomes of its adjustments, refining its decision-making processes for long term seaport operations.
11.21.4 Post-conditions
Through this dynamic network performance monitoring and adjustment process, the seaport maintains optimal performance across all its network slices despite the challenging weather conditions and increased operational demands.
11.21.5 Existing features partly or fully covering the use case functionality
In TS 22.261 [14], clause 6.1.2.2 on management include the following requirements:
The 5G system shall allow the operator to create, modify, and delete a network slice.
The 5G system shall allow the operator to define and update the set of services and capabilities supported in a network slice.
The 5G system shall allow the operator to configure the information which associates a UE to a network slice.
The 5G system shall allow the operator to configure the information which associates a service to a network slice.
Traffic and services in one network slice shall have no impact on traffic and services in other network slices in the same network.
Creation, modification, and deletion of a network slice shall have no or minimal impact on traffic and services in other network slices in the same network.
The 5G system shall support scaling of a network slice, i.e. adaptation of its capacity.
The 5G system shall enable the network operator to define a minimum available capacity for a network slice. Scaling of other network slices on the same network shall have no impact on the availability of the minimum capacity for that network slice.
The 5G system shall enable the network operator to define a maximum capacity (e.g. number of UEs, number of data sessions) for a network slice.
The 5G system shall enable the network operator to define a priority order between different network slices in case multiple network slices compete for resources on the same network.
The 5G system shall support means by which the operator can differentiate policy control, functionality and performance provided in different network slices.
In TS 22.261 [14], clause 6.10.2 on requirements for network capability exposure include the following requirements:
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party to define and update the set of services and capabilities supported in a network slice used for the third-party.
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party to configure the information which associates a service to a network slice used for the third-party.
Based on operator policy, a 5G network shall provide suitable APIs to allow a trusted third-party to create, modify, and delete network slices used for the third-party.
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party to monitor the network slice used for the third-party.
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party to scale a network slice used for the third-party, i.e. to adapt its capacity.
Based on operator policy, the 5G network shall expose a suitable API to allow an authorized third-party to monitor the resource utilisation of the network service (radio access point and the transport network (front, backhaul)) that are associated with the third-party.
Based on operator policy, the 5G network shall expose a suitable API to allow an authorized third-party to define and reconfigure the properties of the communication services offered to the third-party.
As we can see, the 5G network allows a trusted third party to monitor the resource utilization of the network service. The monitoring does not support the prediction of the network performance data (e.g. latency, reliability and resource usage).
11.21.6 Potential New Requirements needed to support the use case
[PR 11.21.6-1] Subject to operator’s policy, the 6G network shall be able to expose information related to a network slice (e.g. current or predicted latency and reliability) to the authorized third party.
11.22 Use case on spatial computing enabled dynamic material management
11.22.1 Description
In a lot of factories, it depends on large quantities of materials and parts moving between production lines. Every hour, thousands of parts are transported from the warehouses to the various assembly lines, and finished goods are transported from the end of the line to the storage areas which are in different locations including indoor and outdoor. The production pace is so fast that any material delays, path congestion, or equipment failures can bring the line to a standstill, affecting overall production capacity.
Dynamic Materials Management is a highly integrated and automated system that enables real time monitoring, optimized storage and efficient use of factory materials through precise spatial data acquisition, processing and interaction. Spatial computing combines a variety of technologies to understand and process information in physical space and could be utilized in diversified industries and domains to provide immersive experience and dynamic management.
In smart factory scenario, the spatial computing system could collect real time information about the location of each piece of equipment and material in the factory, process, display of, interact with, these information to build and update a digital twin model of the factory to support dynamic material management and factory layout optimization. By sensing, analyzing and optimizing material delivery paths in real time, and dynamically adjusting factory layouts according to production demand, factories can dramatically improve production efficiency, reduce resource wastage, and improve the flexibility of the entire supply chain.
The 6G network could help the spatial computing system to collect real time information, with 6G AI and computing capability to assist spatial computing system to build and update digital twin model of the factory. Besides these, 6G sensing capability will be used to assist AGV to secure and efficiently deliver material.
A factory's production lines may change based on customers’ order priorities, with the help of 6G system, the priority of each material is analysed in real time by the spatial computing system, then the order and path of material delivery are automatically adjusted under the collaboration between 6G network and the spatial computing system. In case the equipment failure, it can be identified in real time and the transportation path of materials could be adjusted on time either.
11.22.2 Pre-conditions
Operator TT provides “Smart Factory++” service through its 6G network which could not only provide 6G communication service but also AI and computing service for spatial computing system used by factory customer.
Operator TT has deployed and configured 6G network including multiple local Service Hosting Environments.
Manufactory MM has subscribed the “Smart Factory++” service from Operator TT to operate its dynamic material management and factory layout optimization task.
11.22.3 Service Flows
Through the 6G network, each 3GPP IoT device (e.g. 5G IoT device or 6G IoT device) on board the sensors (e.g. High-resolution camera, 3D scanner, depth sensors, AGV, etc.) in the factory continuously upload location information (with centimeters or even micrometers level accuracy) and status data of each piece of equipment and material in the factory, to the Service Hosting Environment A and Service Hosting Environment B which are near the sensors with required data processing capability.
After processing the received information, Service Hosting Environment A and Service Hosting Environment B report the results to the spatial computing system to generate a real time digital twin model of the factory.
When a batch of materials departs from a warehouse, an AGV sends the materials' current location, transportation path, and estimated time of arrival to Service Hosting Environment B over the 6G network. Multiple AGVs in the factory are operating simultaneously.
Sensors in the factory detect the movement of materials and track the environment status of transportation paths in real time and report sensing data to the Service Hosting Environment A and B in real time. Service Hosting Environment A is configured to process the sensing data from transportation paths in the west, while Service Hosting Environment B is configured to process the sensing data from transportation paths in the east. The real time position information of the AGVs are shared through 6G network while the sensing data result from Service Hosting Environment A and B are shared either, thus to avoid collisions or path conflicts at the intersections.
When a conveyor belt in the transportation path fails, the Service Hosting Environment B could identify and analyse this situation, then immediately notify the AGV via the 6G network, then the AGV adjusts the path to ensure that the materials can be delivered in time.
The Service Hosting Environment B and A report the update information of each piece of equipment and material to the spatial computing system to maintain a real time digital twin model of the factory.
If a production line is lacking key components, the spatial computing system will automatically instruct the closest AGV through 6G system to prioritize the transportation of this batch of materials. When the demand of the production line changes, the spatial computing system can adjust the priority of materials in real time through the 6G network to ensure that the most important materials can be delivered first.
The spatial computing system analyzes the material demand of the production line based on real time data and finds that the order quantity of a certain production line has increased and the material demand grows accordingly. Then, it notifies the relevant automation equipment (e.g. robots or AGVs) via the 6G network to start moving the material storage area closer to the production line, to reduce material transportation time.
During the layout adjustment process, with the help of 6G network, the space computing system monitors the position of each piece of equipment and material in real time and update the digital twin model of the factory.
11.22.4 Post-conditions
The Manufactory MM is able to real time tracking of material flow in the factory and automatically adjust the layout of a factory (e.g. relative position of storage areas, conveyors, and equipment) to optimize material supply and production efficiency based on dynamic changes in material demand, transportation paths, and production lines.
11.22.5 Existing features partly or fully covering the use case functionality
In TS 22.261 [14] clause 6.5.2, following requirements are defined:
The 5G network shall support configurations of the Service Hosting Environment in the network (e.g. access network, core network), that provide application access close to the UE's point of attachment to the access network.
Based on operator policy, the 5G network shall be able to support routing of data traffic between a UE attached to the network and an application in a Service Hosting Environment for specific services, modifying the path as needed when the UE moves during an active communication.
Based on operator policy, application needs, or both, the 5G system shall support an efficient user plane path, modifying the path as needed when the UE moves or application changes location, between a UE in an active communication and:
- an application in a Service Hosting Environment; or
- an application server located outside the operator’s network; or
- an application server located in a customer premises network or personal IoT network.
The 5G network shall be able to interact with applications in a Service Hosting Environment for efficient network resource utilization and offloading data traffic to the most suitable Service Hosting Environment, e.g. close to the UE's point of attachment to the access network or based on usage information.
NOTE: To accomplish offloading data traffic, usage information might be exposed to the Service Hosting Environment.
But, the existing service hosting environment related requirements do not consider the computing resource management for computing or data services.
11.22.6 Potential New Requirements needed to support the use case
[PR 11.22.6-1] Subject to operator’s policy and agreement with the 3rd party, the 6G system shall provide a mechanism for operators to configure, adjust and manage the computing resource in Service Hosting Environment based on e.g. UE location, computing service requirements from 3rd party.
[PR 11.22.6-2] Subject to operator’s policy and agreement with the 3rd party, the 6G system shall support to select one or more Service Hosting Environment to fulfil the computing service requirements from 3rd party, considering the UE location, computing capabilities and traffic load, etc.
11.23 Use case on independent 6G local network for factory
11.23.1 Description
In smart factories, 6G networks are critical communication infrastructures that support real time communication and collaboration among various types of devices, robots, sensors in the factory, local platform and cloud-based platforms. When the connection with the remote (non-local) network fails, the production and management of the factory may be seriously affected. In this case, 6G network in factories need to quickly build a 6G local network to ensure production continuity. The 6G local network is composed of the 6G RAN, the core network of 6G system, service hosting environment which are deployed in the factory. Usually, the core network of 6G in the factory doesn’t work. When the backhaul between the 6G RAN and remote core network of 6G system is broken, the core network of 6G system in the factory will connect with 6G RAN in the factory to continue the communication service.
The 6G local network will maintain basic operation of production processes, ensuring that automated equipment, robots, sensors, etc. can continue to work together. The capability of the 6G local network depends on the network functions of the existing 6G infrastructure in the factory. It supports local data delivery, processing and after the external 6G network is restored, it will resume either. Providing local monitoring and control functions is another demand to ensure production safety and efficiency.
11.23.2 Pre-conditions
Operator TT provides “Smart Factory network+” service for its factory customer through its 6G network which provide independent 6G local network with communication and data services when the network has some failure.
Operator TT has deployed and configured and network components e.g. the core network of 6G system, base station, multiple local data processing nodes, “Smart Factory network+” local platform etc. in the factory.
Manufactory MM has subscribed the “Smart Factory network+” service from operator TT.
11.23.3 Service Flows
The “Smart Factory network+” local platform continuously monitors the 6G network. Suddenly, there is something wrong with the 6G network, the base station in the factory can’t connect with the core network deployed remotely.
The “Smart Factory network+” local platform sends the fault alert to the factory's production scheduling system which is deployed in the factory by Manufactory MM, and whole network components in the factory.
The local network components including local service hosting environment, base stations, the core network of 6G system automatically construct a 6G local network to provide communication service for the factory's equipment, robots, and sensors, with no further communication with the remote network.
Through the new constructed 6G local network, the factory's production scheduling system demands the factory's equipment, automation devices (e.g. robotic arms, CNC equipment, AGVs, etc.), and sensors etc. continuously perform scheduled production tasks.
Since external networks cannot be connected, local service hosting environment takes over more local processing tasks. They continue to receive data from devices and sensors and assist the factory's production scheduling system to process all types of real time data, such as production progress, equipment status, material scheduling, fault detection, etc. ensuring that production efficiency is not compromised.
Due to the failure of the remote network, the local production data is unable to be uploaded to the cloud or remote servers in real time. Production data from all devices, sensors and automation systems are transmitted over the local 6G network and cached in the factory's production scheduling system. Once the 6G remote network is restored, the cached data is to ensure data automatically synchronized to the cloud consistency.
The “Smart Factory network+” local platform continuously monitors the local 6G network.
When the 6G remote network is restored, the local 6G network will re-establish connectivity with the remote network. The factory's production scheduling system will synchronize the cached data to the cloud to ensure data consistency. All devices resume communication with the cloud, enabling external data analysis and remote monitoring.
11.23.4 Post-conditions
After confirming that the remote network is operating stably, the local 6G network connects with core network in remote and continues to provide efficient communication support for the factory.
11.23.5 Existing features partly or fully covering the use case functionality
In TS 22.261 [14] clause 6.41, Providing access to local services, multiple requirements about local service are defined. For example,
Subject to regulatory requirements and localized service agreements, the 5G system shall allow a home network operator to automatically negotiate policies with the hosting network for allowing the home network’s subscribers to connect at a specific occasion, e.g., time and location, for their home network services.
Subject to the automatic localized services agreements between the hosting network operator and home network operator, for UE with only home network subscription and with authorization to access hosting networks the 5G system shall support:
- access to the hosting network and use home network services or selected localized services via the hosting network,
- seamless service continuity for home network services or selected localized services when moving between two hosting networks or a host network and the home network.
The 5G system shall support a mechanism to enable configuration of a network that provides access to localized services such that the services can be limited in terms of their spatial extent (in terms of a particular topology, for example a single cell), as specified by a service provider of localized services.
The 5G system shall support a mechanism to enable configuration of a network that provides access to localized services such that the services can be limited in terms of the resources or capacity available, to correspond to requirements that apply only to the locality of service delivery, as specified by a service provider of localized services.
The 5G system shall enable the home network to allow a UE to automatically select a hosting network for accessing localized services when specified conditions (e.g., predefined time, location) are fulfilled.
The 5G system shall be able to prevent a UE to re-access the hosting network after the localized services were terminated if the authorization for the localized services is no longer valid (e.g., can be based on certain conditions such as time or location of the user).
The 5G system shall provide mechanisms to mitigate user plane and control plane overload caused by a high number of UEs returning from a temporary local access of a hosting network to their home network in a very short period of time.
The 5G system shall provide mechanisms to minimize the impact on the UEs communication e.g., to prevent user plane and control plane outages when returning to a home network together with other high number of UEs in a very short period of time, after terminating their temporary local access to a hosting network.
But, the automatically establishing local network and detection the home network restore and switch off the local 6G network are missing.
11.23.6 Potential New Requirements needed to support the use case
[PR 11.23.6-1] Subject to operator’s policy, the 6G network shall support means to enable communication between UEs and a service hosting environment in a local 6G network, managed by an authorized 3rd party, when the connection with the remote (non-local) network fails.
NOTE 1: The local 6G network is composed of at least 6G RAN and core network of 6G which are deployed in the local area e.g. factory.
NOTE 2: The above assumes agreement with the 3rd party and control by the core network of 6G system,
11.24 Use case on utility direct transfer trip for distributed energy resources integration and protection
11.24.1 Description
Low latency communication from device to device is required for many grid protection and control functions. Rapid communication of a fault condition enables protective actions such as breaker trips to take place before equipment damage occurs. Communication is also used to prevent false tripping and unnecessary outages. Many complex factors related to the type of protection, and the electrical characteristics of the circuits being protected, affect the choice of protection schemes and the resultant communication requirements. In simple terms, the more electrical power being delivered, the faster the required response, and the higher the required reliability.
Growing numbers of interconnected DER sites (examples: solar and wind) have increased the need for protection. Large DER sites may deliver hundreds of kW to a few MW of generated load back to the utility grid. These loads can potentially destabilize the grid without fast and reliable monitoring and control, resulting in communication latency requirements in the low 10s of ms. It is difficult to reliably provide that performance with current wireless networks, so an optical fibre connection is often required. Since these DER sites are frequently rural, there is little to no existing fibre, so the installation can be prohibitively expensive. There is a need and opportunity for 6G networks to meet these requirements at a much lower cost than new fibre installations. Also, a node-to-node communication ability will be useful for low or no coverage areas of the PLMN for reliability of the Grid.
The benefits of 6G in supporting DER
The positive impact from the 6G system use case will enable the construction of renewable energy sources (solar and wind) in more locations, by avoiding costly installation and maintenance of fibre networks.
Energy resources: As explained, the 6G system can play a central role in sustainable and resilient use of energy resources via by enabling renewable energy installation in more remote locations at a much lower cost.
Emissions: The use of wireless instead of requiring the installation of new fibre reduces materials consumption and emissions from the fibre installation process.
Trustworthiness: There is an introduced risk of cyber-attacks from introducing connected devices across society-critical infrastructures such as utilities that require security measures towards various threat vectors. Also, the risk for privacy intrusion needs to be considered. The efficient secure protocols defined in 3GPP networks and deployed by operators are typically combined with end-to-end security between the devices implementing the protection system.
Infrastructure: The 6G system with appropriate and new requirements can serve the growing utility infrastructure needs for better control and protection of renewable energy generation in an efficient way. Vulnerability includes the increased risk of cyber-attacks as mentioned, something that comes with any wireless network operations.
Figure 11.24.1-1: What is this figure?
Editor’s note: Missing title and a reference to the figure
11.24.2 Pre-conditions
Company X is a utility company that wants to install a medium sized solar generation site to provide renewable electricity in a region New-Land.
The company will interconnect the solar site to a distribution feeder that runs 25 km to the nearest substation, also serving electricity to rural customers along the route.
Since power is being supplied from both ends of the distribution feeder circuit, special protection is required to handle fault conditions (overloads, downed power lines, damaged poles, etc.). In the case of a fault condition, both the solar site and the substation need to simultaneously disconnect to prevent an islanded condition that could result in voltage surges and endanger the workers. A protection scheme called DTT which requires signalling between the solar generation site and the serving substation. Messages must be reliably (99.999%) delivered in under 25 ms (device-to-device) to ensure faults are handled safely.
Company X installs the 6G wireless network and likes to meet the expected performance and reliability requirements that they can receive with Fiber installation.
Operator ABC provides the new 6G communication service for utility direct transfer trip of the solar site of utility Company X.
11.24.3 Service Flows
Company X wants to use wireless for DTT protection. This is also in the interest of the Mobile Operator ABC, who wants to provide service to the utility for this use case supported by a 6G system.
Company X provides protective relays with 6G capability to be installed at its solar site.
Operator ABC provides a differentiated service to Utility Company X with low latency and reliable service
This service is part of critical operations avoiding unnecessary power outage to a large area of utility service zone
11.24.4 Post-conditions
When the fault takes place with the solar generation end, the two ends communicated with the control message and got disconnected within the specified time in order to avoid islanding as per the DTT fault protection mechanism.
11.24.5 Existing features partly or fully covering the use case functionality
TS 22.261 [14] clause 6.28.2.2 describes Smart Grid requirements.
TS 22.104 [64] clause A.4.1.1 provides smart grid use cases on voltage protection and fault protection mechanisms using FLISR. But no use cases on DTT are listed. The DTT protects a large service area while the FLISR protects only a neighbourhood.
11.24.6 Potential New Requirements needed to support the use case
Editor’s Note: KPI values will be updated in future contributions
[PR 11.24.6-1] Subject to Operator’s policy, the 6G network shall support end-to-end, secure and reliable IoT device communication with the network following KPI’s in Table 11.24.6-1
[PR 11.24.6-2] Subject to Utility Company and Mobile Operator’s policy, the 6G network should provide alternate means to communicate between devices at both ends in the event when a UE loses its connectivity to the 6G system.
Editor’s Note: This requirement is FFS
Table 11.24.6-1: KPI for direct trip transfer (DTT) use case
Profile
Characteristic parameter
Application Bit rate down link (Mbit/s)
Application Bit rate up link (Mbit/s)
End-to-end latency: maximum (ms)
Payload Message size (kbyte)
# of UEs
connection
Communication service availability: target value
Transfer Interval
Direct Transfer Trip
TBD
TBD
< [10]
TBD
NOTE
[2]
[> 99.999]
TBD
NOTE: Typical message sizes are 500-byte IEC 61850 [356] Generic Object Oriented Substation Event messages plus any overhead due to tunnelling, security etc.
11.25 Use case on monitoring utility transmission grid assets
11.25.1 Description
In a high voltage transmission, there is an increasing need for monitoring line conditions, as transmission lines are operating at capacity limits as electrical load continues to grow. Transmission lines can handle additional capacity, if the environmental conditions support it. This is called dynamic line rating. On a cool windy day, conductors can safely carry more current without sagging, compared to a hot, still day. Dynamic line rating requires real time monitoring of conditions of the lines and environment to determine the safe current capacity. Sensors must be located on or near the lines and provide data. There is often no low-voltage mains power available, so sensors need low power operation to work with scavenged power (inductively coupled from the high voltage line) or on batteries if the line is de-energized. As the transmission lines are frequently in remote rural areas, there is a need for long range communication.
These sensors may report various types of information in addition to conductor temperature, wind, and ambient temperature for dynamic line rating. Sensors are also used to detect possible arcing in insulators, unusual swaying or other motion of the conductors, movement or vibrations in the supporting towers. When wireless network coverage and capacity allows, video or still photos can be used to detect physical security issues, animals, smoke and fire, and other conditions that could affect the transmission line.
The ability to perform an alternate communication method to store and forward messages from the sensors along the transmission line is valuable when the line is located at cell edge or out of coverage due to temporary obstruction, weather or other environmental reasons.
11.25.2 Pre-conditions
Utility company X wants to monitor its transmission line for safety of the line. On a hot day, they want to know if the voltage on the transmission line is safe for it not to sag and cause fire or short circuits. Measuring the transmission parameters at different environmental conditions (temperature, air pressure, moisture, wind flow) is useful to avoid potential outages and accidents. For example, on a suitable cool day, they may carry more electrical load to fulfil its capacity. Low power long life sensors can measure the ambient conditions and inform the central location periodically, but if the pre-set threshold is crossed, the transmission system should be able to take action (i.e. it may trigger to take a picture and send for prompt action). If the sensors are not low power enough or do not have good coverage, the transmission line monitoring becomes faulty and as a result, wildfire, accidents can easily take place.
11.25.3 Service Flows
Utility transmission line uses MNO provided PLMN wide area network for monitoring,
Sensors are used to monitor the environmental conditions and send the data periodically to a nearby Utility office for human monitoring. The data may be stored before sending due to configuration of time interval period,
If the measurement data exceeds the pre-set threshold points for safety, the sensor must send the data immediately. Sensors may also trigger a camera to take a picture of the situation if the infrastructure allows,
The monitoring centr[[SUGGESTION_START]]e[[SUGGESTION_END]] sets an alarm for immediate attention and reduces the load of the transmission line or any other action to fix the problem from becoming worse,
If for some reason, the transmission line at the edge of a MNO cell does not have a good coverage to communicate effectively, it tries other means of communications such as sending to a nearby node which has better connectivity (if the infrastructure allows that capability) or via NTN interface.
11.25.4 Post-conditions
Transmission line monitoring results are used for making sure that the transmission line has the right load and it does not cause any fire or short circuit related problems remotely. A helper can be sent if the problem cannot be corrected automatically by adjusting the voltage or load on the transmission line.
11.25.5 Existing features partly or fully covering the use case functionality
3GPP Massive IoT communication via sensor UEs with NB-IoT or LTE-M RAT on 4G mostly.
For 5G system:
In TS 22.261 [14] clause 6.4 Resource efficiency states some general requirements for IoT devices.
In TS 22.261 [14] clause 8 Security, security requirement for IoT devices is listed.
In TS 22.261 [14] clause 6.28.2.2 describes Smart Grid requirements
TS 22.104 [64] Appendix A.4 also contains a number of Utility grid use cases.
However, the transmission line monitoring is a new area of measurement which can be possible with a variety of new generation of 6G IoT devices.
11.25.6 Potential New Requirements needed to support the use case
[PR 11.25.6-1] The 6G System shall ensure that IoT communication services shall minimize impact on other services (e.g. regular voice, video and data).
[PR 11.25.6-2] Subject to the Operator’s policies and control, the 6G system shall provide means to support extended coverage.
[PR 11.25.6-3] The 6G System shall support diverse device types with long lifetime (e.g. 10 to 20 years).
NOTE: 3GPP should consider support of device lifetimes longer than the lifetime of the supporting 6G core network.
[PR 11.25.6-4] Subject to the Utility Operator’s and MNO policies, the 6G system shall support the 6G IoT communication services with the following KPI values:
Table 11.25.6-1: KPI for monitoring Utility transmission grid assets
Profile
Characteristic parameters
Application Bit rate down link (kbit/s)
Application Bit rate up link (kbit/s)
End-to-end latency: maximum (ms)
Devices per km of transmission line
Reliability of the connection: target value
Communication service availability: target value
Battery lifetime
Monitoring Utility transmission grid assets
TBD
TBD
TBD
[5-15]
[99.999]
[99.999]
10-20 yrs
Editor’s Note: Parameters of this table are FFS
11.26 Use case on 6G-enabled decentralized grid power contract
11.26.1 Description
Production and consumption of electricity in a distribution grid is becoming more and more complex. The behaviour of the customer connected to the grid could vary from importing electricity from the grid or producing electricity to the grid, depending on time of the day and external conditions (market value, weather etc.)
There is also a local behaviour, a way to share local production between neighbours in the same LV grid or in a slightly larger aera. Currently, these local communities are energy based, the behaviour of the community is not working in real time and the settlement is done during the billing time. An example of this type of community can be find in France as “collective self-consumption”.
The actors of the energy system are more and more concern by the real time balance of the grid. It will be a good value for the electricity system if some actor could act in predictable way, following a predictable contracted load, whether it is consumption or production.
This use case shows a need for the 6G to handle a local peer to peer communication between electricity meters, cyber secured and resilient, so the contract of power impact to the energy system could be respected.
Table 11.26.1-1: Potential sustainability impacts of the Decentralized Local Grid Power Contract use case
(the UN SDGs/GDC matching goals of each aspect within 3GPP context)
Potential benefits of the use case (added value)
Potential areas of attention of the use case (risks to be mitigated)
Environmental sustainability aspects
(UN SDGs 12, 13, 14, 15 and indirectly 6, 7 & 11.
UN GDC “Develop principles for environmental sustainability of digital technologies”)
Energy resources
(UN SDG 7, 11, 12)
Improved usage of local energy production
Material resources
(UN SDG 11, 12)
Avoid the need to build fast frequency response equipment since the behaviour is predictable by the electricity system
Emissions
(UN SDG 6, 7, 11, 12, 13, 14, 15)
Improve the integration of local renewable system that can avoid building high CO² emission generation based on coal, oil, gas.
Socio-economic sustainability aspects
(UN SDGs 2, 3, 4, 5, 8, 9, 10, 11, 16 & 17 and indirectly 12.
UN GDC “Closing Digital Divides and Accelerating SDG Progress”
&
“Expanding Digital Economy Inclusion”
&
“Fostering an Inclusive, Safe Digital Space”)
Trustworthiness
(UN SDGs 11 and indirectly 3 & 17)
Avoid the unpredictability of the power consumption of the community
Work & income
(UN SDG 8 and indirectly 12)
Optimal behaviour of the community provides them a better price for electricity
Infrastructure
(UN SDG 9)
Improving stability of the electric grid
TCO reduction
(UN SDGs 8, 9 and 12)
Avoid oversize dimensioning of production or battery in a local distribution grid
11.26.2 Pre-conditions
- A community of local users of the grid decide to act coordinate and propose a targeted electricity power value interval to an electricity system actor for a period using the 6G system.
- Through the 6G system, the electricity system actor provided the power limit and timeframe for this community.
11.26.3 Service Flows
During one electricity period (50 Hz – 20 ms or 60 Hz – 17 ms), the meter of each community user calculates his power impact on the grid and send this information to the other meters of the community using 6G network.
Using the information transmitted by their peers, the meter with his onboarded algorithm (could be classical multi agent or AI) modify the impact on the grid of the premises, using the controlled appliances located inside the premises (water heater, photovoltaic inverter, electric vehicle, heat pump, pool pump, static batteries…). The communication between the meter and the appliances is not part of this document.
Each meter repeats bullet 1 till the end of the contract period.
11.26.4 Post-conditions
The local community manages to behave the way they have contracted with the electricity system actor.
11.26.5 Existing features partly or fully covering the use case functionality
TS 22.104 [64]: Service requirements for URLLC.
TS 22.261 [14]: Service requirements for the 5G system; includes general support for IoT, URLLC, and industrial use case
11.26.6 Potential New Requirements needed to support the use case
[PR 11.26.6-1] The 6G system shall be able to support the following KPIs associated:
Table 11.26.6-1: Communication KPI for Decentralized Local Grid Power Contract use case
Use case #
Characteristic parameters
Influence quantity
Communication service availability: target value [%]
Communication service reliability: mean time between failures
End-to-end latency: maximum [ms]
Service bitrate: user experienced data rate [ kbps]
Message size [byte]
Transfer interval: target value
Survival time
UE speed
# of UEs
Service area
1
99.999
–
< 1
< 100
< 100
n/a
(note)
–
stationary
<200
2 km radius
NOTE: event-triggered in 50 Hz or 60 Hz
Editor's Note: Values are FFS
W Other Use Cases
W.1 Use case on computing service for XR gaming acceleration
W.1.1 Description
With the rapid advancement of XR technology and the gaming industry, there is an increasing demand from users for more realistic and immersive experiences. Providing such experiences often involves intricate graphics rendering, extensive data processing, and real time interaction, which necessitates a tremendous amount of computing power.
Rendering technologies, such as local rendering, remote rendering, and split rendering, have been studied to enable high-quality VR content on terminal devices as mentioned in [234]. In split rendering, a client (which can be a lightweight client) renders the most time-sensitive or low-fidelity objects that require less computing resources, while some delay tolerant or high-fidelity objects that demand powerful computing resources are rendered at the server (which can be a powerful device) as mentioned in [235]. As shown in Figure W.1.1-1, the server encodes the rendered data (e.g. surface caches) within an intermediate representation according to [236], and the client then decodes, renders and reconstructs locally. In addition, an example of requirements for split rendering on the client and on the server are shown in Table W.1.1.-1.
Figure W.1.1-1: Split rendering
Table W.1.1-1: Example requirements for split rendering
Split rendering
Rendering on the server
Rendering on the client
Rendering description
illumination, spheres or grids;
volumetric clouds;
environment modelling;
…
peripheral pixels;
graph synthesis;
…
Hardware requirements
Generic CPU/GPU can support.
Generic mobile phone can support.
Split rendering is also advantageous for reducing end-to-end latency and effectively distributing the rendering load between the server and low-powered device. Thus, in order to enable users with various 6G terminal devices to enjoy high-quality experiences at a fraction of the cost, the implementation of split rendering technology is highly recommended.
When offering rendering services to accelerate XR games for users, operators can consider the following three categories of business models as shown in Figure W.1.1-2:
• Category 1: Service anchor at a third-party AF. This is a classic business model. In this category, the AS operates outside the 6G network. When UEs requests rendering services through specific applications, the AS negotiates with the 6G network to acquire information about computing resources. The computing process are executed and completed in the cloud or edge. Throughout the process, the service anchor remains with the third-party AF, as the final computing results are reassembled and organized at the AF before being sent to the UE. The primary role of the 6G network in this model is to facilitate the transfer of data and information between the UE and AF.
• Category 2: Service anchor at a third-party AF with partial computing offloading to 6G network. This is a variation of the classic business model. When UEs request rendering services, the AS might offload part of the rendering process to the 6G network based on the SLA/negotiation. When the AS is deployed by operator, then AS negotiates with 6G network and it can be interpreted into the UE interacts/negotiates with 6G network. If the AS is deployed by 3rd party, then AS interacts with 6G network based on SLA. One or multiple computing nodes within the 6G network handle the offloaded work. After processing, the final result is still reassembled and organized at the AS before being sent to the UE. In this model, the 6G network provides services beyond data transfer, which also include computing services.
• Category 3: Service anchor within 6G network. IMS is one instance of this category. In this category, the AS is deployed inside the 6G network. It can be interpreted into the UE interacts/negotiates with 6G network. When UEs request rendering services, the AS invokes one or multiple computing nodes within the network to either individually or collaboratively complete the rendering process. After the processes are completed, the results may be reassembled and organized at the AS before being transmitted to the UE. The 6G network provides services beyond data transfer, which includes both communication services and computing services.
Figure W.1.1-2: Business models to provide computing services
The existing mobile network is capable of meeting communication demands, while the convergence of mobile network and computing power can offer a wide range of converged computing and communication services. In the future, 6G networks will provide significant resources, including communication resources, computing resource, data resource, and AI resource, inspiring new solutions for game acceleration services (e.g. VR game rendering).
W.1.2 Pre-conditions
The 6G Operator-C has deployed computing nodes for handling offloaded workload from UE. The 6G Operator-C can provide UE with a variety of converged computing services (e.g. XR rendering, environment reconstruction, image classification, language processing). Meanwhile, they can also expose the information of computing resources deployed in the core network or in the edge compute domain to an authorized 3rd party or a UE to help consumer to select their service appropriately.
One game company Wukong and the 6G operator-C have an SLA to enable the 6G system to provide computing services to accelerate XR game for the players of VR game "Wukong".
The game company Wukong performs some preliminary processing for the game "Wukong" on its own server. Then, the splitting rendering can be utilized to divide the game rendering into: a) most time-sensitive or low-fidelity object parts that require less computing resource, and b) some delay tolerant or high-fidelity objects parts that demand powerful computing resources. The former can be achieved by a generic game device, while the latter can be offloaded to the network of 6G Operator-C.
Alice and Bob are big fans of the game "Wukong", they both purchased expensive game devices (e.g. VR headsets) to play the game "Wukong" and subscribed to 6G Operator-C’s mobile service to ensure that they can enjoy the game anywhere anytime.
They all enjoyed their game until the game company Wukong releases a new version of game. The game experience has been greatly upgraded, but the hardware requirements for game devices are also getting higher and higher. The old game device is unable to guarantee the upgraded game experience due to hardware limitations. Alice and Bob now face two choices: Option a) to buy the new version of game devices with advanced hardware, and of course, it will cost a lot of money. Option b) to buy the computing service from the 6G Operator-C to accelerate the game "Wukong".
W.1.3 Service Flows
Figure W.1.3-1: Computing service for VR game rendering
The service flows of Computing service for VR game rendering are depicted in Figure W.1.3-1.
Alice and Bob play the online VR game "Wukong" together. To enhance their gaming experience and address game issues caused by low computing power, Alice chooses to buy an expensive new game device with advanced hardware. The business model for Alice can be interpreted as category 1 mentioned in description part. For Bob, replacing the equipment would be costly, so he decides to subscribe to the computing service (e.g. VR rendering) from the 6G Operator-C for game "Wukong" to accelerate the game. The business model for Bob can be interpreted as category 2 mentioned in description part.
In the subscribed computing services, the UE may firstly negotiate with the 6G network to interact information about computing service, for example, computing task (e.g. rendering, environment reconstruction, image classification, language processing) supported by network, latency required by the UE, how to report the status of the computing task and so on. 6G Operator-C then evaluates the computing task and schedules some appropriate computing resources for the specific computing service. Besides, the network is capable of providing the network-side rendering for some delay tolerant or high-fidelity objects part of VR game. Subsequently, the rendered data are encoded and transmitted to the game device to carry out the terminal-side rendering for the part of the most time-sensitive or low-fidelity objects.
NOTE: The algorithm used for split rendering can be pre-configured on the computing node, or uploaded by the UE or downloaded from the authorized 3rd party before the rendering service starts.
2. The 6G network receives the compressed data from the game company Wukong, which has been preliminarily processed on the game server.
Upon receiving the compressed data from game company "Wukong" for Bob, the 6G Operator-C identifies that Bob has subscribed to the computing service for game rendering. Consequently, the network allocates communication resource and computing resources to provide appropriate computing services by performing network-side rendering, which helps accelerate the VR game interaction on Bob’s game device.
The network also enforces the policy and QoS control for Bob to guarantee both the communication and computing requirements to render and transmit the game data, such as rendered graphics data. Additionally, the network also manages and provides the computing service related information (e.g. computing status of the task) to Bob’s UE.
Upon receiving the compressed data from game company "Wukong" for Alice, the 6G Operator-C identifies that Alice has not subscribed to the computing service. Therefore all game data is transmitted to Alice's game devices and the game rendering is locally performed by Alice’s devices.
The network also enforces the policy and QoS control for Alice to guarantee the communication requirements to ensure data transmission.
3. In accordance with the SLA between the operator (i.e. 6G Operator-C) and 3rd party (i.e. game company Wukong), the network consistently improves the performance of game Wukong rendering and acceleration. When Bob engages in interactive operations in VR games, such as moving, turning, and triggering actions, the VR game rendering provided by computing service on the network provides superior game experiences, such as smooth scene switching, light and shadow changes, object movements, and more. This ensures Bob can enjoy the same game experience with Alice.
W.1.4 Post-conditions
Alice spends a lot of money to buy the new costly XR devices (e.g. VR headsets) to enjoy high-quality, real time game experiences.
Bob can enjoy the same experiences with Alice without upgrading his game devices. Instead, Bob only needs to invest a small amount of money in the computing service provided by 6G Operator-C.
W.1.5 Existing features partly or fully covering the use case functionality
The existing 3GPP system can only provide communication services, the existing 3GPP system does not provide computing service to subscribers.
W.1.6 Potential New Requirements needed to support the use case
[PR W.1.6-1] Subject to operator’s policy, the 6G network shall be able to fulfil the user-requested performance requirement (e.g. latency) for the 6G Computing Service.
[PR W.1.6-2] The 6G network shall be able to inform UE of the information related to 6G Computing Service in the Service Hosting Environment (e.g. guaranteed computing capabilities) to assist an UE to invoke a computing service.
W.2 Use case on computing service enabling personal AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent
W.2.1 Description
AI, in particular GenAI, is set to revolutionize future applications and become a mainstream technology, especially on mobile devices. Leveraging the power of GenAI, personal AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents are becoming essential tools for users and are fast becoming increasingly powerful.
One could imagine AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents available with AR glasses and using information (e.g. location, gyro data, video scene, audio prompt etc.) to generate real time video content that could be displayed on these glasses, superimposed onto the real scene, and also associated audio content. Other AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents could be available on smartphones, e.g. as a new user interface combining audio prompts and other information to not only interact with the device itself but also to generate content on the fly depending on the user’s actions. These examples raise issues of device form-factor limitations, latency, compute load and privacy. These agents may also make extensive use of RAG technology to fetch relevant information based on the user's personal knowledge. An augmented prompt with context after RAG is then used to generate a response through a pre-trained AI model, such as a LLM. Such RAG processing should take place on local devices to ensure user privacy and confidentiality.
Currently, LLM models are predominantly hosted on cloud servers to provide responses. However, this reliance on a cloud-based approach introduces several notable challenges:
- Latency: latency can impede real time interactions, as the delay in communication with a centralized server can be significant.
- Privacy: data processed on a centralized server might be more vulnerable to security breaches, and data private to the user may not be approved for sharing with the service provider.
- Congestion: the network can experience congestion as data moves through the infrastructure, affecting E2E Compute Service performance.
- Power consumption: substantial energy consumption is required both for data transmission and cloud server operations.
- Scalability: expanding the capacity of centralized servers demands extensive resources and infrastructure.
These issues highlight the need for more efficient and secure alternatives to the traditional cloud-based approach.
As personal AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gents become increasingly essential to the user experience, providing computational services by the 6G system can not only address the issues identified above (latency, compute load, privacy, power consumption, congestion) but in doing so also generate new value for operators.
For mobile users employing AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent services, offloading the computationally intensive tasks to the 3GPP system's resources leverages the network's low-latency and high-bandwidth capabilities, which are proximate to the user in both physical distance and network hops. This approach allows RAG computations to be processed locally on the device itself or device edge (i.e. other devices in vicinity), enhancing privacy. Similarly, LLM inference tasks can split into personalized LLM inference vs centralized LLM inference. The personalized LLM inference can still run on the device or at the device edge whereas centralized LLM inference that demands substantial computational power is transferred to a more capable compute node, e.g. in the network. The LLM models used in the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent service are typically provided by third-party service providers that leverage mobile network operator’s compute resources. This system processes the data efficiently and returns the response promptly to the user's device. Such device edge computing framework will ensure a seamless and continuous user experience while maintaining privacy through processing within the trusted MNO device edge domain.
W.2.2 Pre-conditions
During her daily commute, Eva effortlessly employs AI services, including personal AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent, through her array of personal devices—mobile phone, smart AR glasses and smartwatch. This personal AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent requires computational tasks such as RAG and LLM inference, where RAG or personalized LLM inference is processed on her devices (or other devices in vicinity) while the more demanding centralized LLM inference is offloaded to other compute nodes for enhanced performance.
MNO A offers compute services through its own resources to handle compute-intensive tasks offloaded by user devices like Eva’s. The MNO A 6G system has computing resources distributed across cloud server, network, or at device edge (e.g. a CPE). Eva has a subscription to the 6G Computing Service from MNO A.
To access the most advanced and largest models for high-quality responses, Eva also subscribes to an LLM inference service provided by Service Provider B. Service Provider B itself has an SLA with MNO A to use their 6G Computing Service to run their customers’ queries through their sophisticated LLM models for inference. This synergistic arrangement allows Eva to benefit from both MNO A’s robust compute infrastructure and Service Provider B’s state-of-the-art AI models.
W.2.3 Service Flows
1. Eva uses personal AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent applications which encompass RAG and LLM inference tasks.
- Eva has recently moved to Japan as part of an intra-company transfer. While traveling in Japan with local SIM, she encountered many signs and product descriptions in Japanese. The RAG task accesses her personal preferences and previous search history stored on her device to provide relevant translations. When she stopped at a local tea shop, her AR glasses recognized her interest in organic products and highlighted organic tea varieties in the translations. The RAG-enhanced translations via LLM inference were seamlessly integrated into her view, overlaying the original Japanese text with English translations in real time.
- During lunch, Eva decided to catch up on some Japanese reading. Her residential flat had installed CPE, which provided not only a connection to MNO A’s system but also additional computing resources. She used her AR glasses to access an e-book, and the preliminary image processing (e.g. narrowing the field of view to only see the page) was offloaded to the CPE. This extended the battery life of her mobile phone and glasses, and reduced communication costs for offloading. Offload to the CPE also avoids sharing the background image of her apartment outside the premises. The LLM provided real time translations and summaries of complex passages, making her reading experience smooth and enjoyable.
2. When Eva chooses personal operation mode, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent application on her device would either run a local LLM in standalone fashion or split the task between a local LLM (for in-device personal data) and a centralized LLM (for complex LLM layers) which would be offloaded directly to MNO A’s system through her subscribed compute service. This approach allows Eva to leverage her local model and maintain privacy of her personal data while benefiting from operator A’s robust computational resources.
3. When Eva opts for advanced operation mode, the AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent application on her device sends a request to Service Provider B. In turn, Service Provider B invokes APIs from MNO A to execute the intensive LLM inference task using SLA between them. This arrangement enables Service Provider B via SLA with MNO A to offer efficient inference services to customers like Eva.
4. As part of operation in Steps 2 and 3, MNO A can distribute the task to different computing resources it has. Also, when there are trusted devices nearby, such as a CPE, MNO A in coordination with AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent on Eva’s device can offload the computationally intensive LLM inference tasks to them. This not only extends the battery life of her mobile phone and glasses but also reduces communication costs associated with offloading as compared to computing at a centralized network location.
5. By subscribing to the compute service offered by MNO A and leveraging LLM inference service provided by Service Provider B, Eva ensures her AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent applications run smoothly, with quick and reliable responses even during peak network usage.
W.2.4 Post-conditions
Eva’s AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent applications can run smoothly while she is moving.
MNO A increases its revenue: by providing compute services directly to subscribed users, and by offering compute resources to service providers.
W.2.5 Existing features partly or fully covering the use case functionality
5G EDGE as described in TS 23.548 [137].
W.2.6 Potential New Requirements needed to support the use case
[PR W.2.6-1] Subject to operator’s policy and user consent, the 6G system shall support a mechanism for providing 6G Computing Service to user (via UE).
[PR W.2.6-2] Subject to operator’s policy, the 6G system shall support mechanisms to ensure security and privacy when providing 6G Computing Service.
[PR W.2.6-3] Subject to operator policy and user consent, the 6G system shall support translating the 6G Computing Service requirements of a 3rd party service provider (e.g. latency) into compute and communication resources of Service Hosting Environment for providing the subscribed 6G Computing Service.
W.3 Use case on computing service in operator managed data network
W.3.1 Description
As we transition towards IMT-2030 and 6G, computing services are crucial for enhancing network capabilities. According to ITU-R M.2160 [27], ubiquitous use of computing resources is projected to expand significantly within IMT-2030. This expansion brings computing capabilities closer to the origin of data, facilitating real time responses and data transport efficiency. The Roadmap to 6G [206] introduces a goal where communications and connectivity services will seamlessly integrate with unified computing, scaling across devices, network resources, and data centres.
In this fast-evolving landscape, Computing Services refer to the strategic utilization of network resources to provide infrastructure, platform, and software functionalities. These services optimize resource use, ensuring efficient application delivery. For example, compute offloading allows third-party applications to enhance processing through operator-managed data network. This is particularly beneficial for devices with limited local processing power, resulting in improved performance and efficiency.
AI-based analytics can further enhance these mechanisms by delivering real time insights, predictive optimisation, and automated decision making. Such smart resource management mechanisms enable real time reservation and dynamic allocation of computing resources, ensuring availability and enhancing application reliability and performance. Additionally, continuously monitoring of resource utilization, AI-driven systems can detect usage patterns, forecast demand fluctuations, and dynamically adjust allocations - preventing both overprovisioning and underutilization. This proactive approach enables operators to maximize efficiency while maintaining optimal service performance.
W.3.2 Precondition
John, a manager in a logistics company as an authorised 3rd party, inputs a delivery order which triggers initialization of computation processes on the operator-managed network, assigning tasks to autonomous delivery drones equipped with real time data processing capabilities.
W.3.3 Service Flows
1. Before the beginning of the business hour, trusted 3rd party blocks/preserves computing resources in advance within the operator managed data network.
2. Once an order is received, the trusted 3rd party offloads the computing workload to the computing resources in the operator-managed data network. By advance reservation, resource allocation is fast and guaranteed.
3. The trusted 3rd party delivers the initial plan to drones and far edge computations for the localized fleet management.
4. The computational workloads on far edge computations further generates initial detail route plans for drones in the assigned zone that would come to the zone. The assigned drone collects the parcel and leaves the warehouse for the delivery location.
5. The drone continuously collects data from its sensors during flight. For instance, it monitors battery voltage, temperature, and motor RPM and sends a report to the far edge computation in the 6G network.
6. AI-based analytics within the network continuously monitor resource allocation data to ensure optimal usage. When overutilization is detected, the system dynamically reallocates workloads to underutilized resources.
7. The drone repeats step 2, step 3, step 4 and step 5 and completes delivery,
8. At each step, usage patterns of the computing service can be exposed to trusted 3rd party so that the logistics company can utilise the information for their business improvements.
W.3.4 Post-condition
John is notified of the successful delivery of the order.
W.3.5 Existing features partly or fully covering the use case functionality
Not applicable
W.3.6 Potential New Requirements needed to support the use case
[PR W.3.6-1] Subject to operator’s policy, 6G network shall support mechanism to allow sharing of information related to 6G Computing Service within the Service Hosting Environment (e.g. to predict overprovisioning and underutilization of computing resources).
[PR W.3.6-2] Subject to operator’s policy and user’s consent, 6G network shall provide mechanism to expose information related to 6G Computing Service to an authorised 3rd party (e.g. to track usage patterns of the computing service for offline AI analysis).
W.4 Use case on network offering information-as-a-service
W.4.1 Description
Consider a city’s planning authority aiming to deploy city tour buses for tourists. To determine the appropriate number of buses, the authority must first estimate how many tourists visit the city. These visitors may arrive via flights, buses, trains, cars, ships, or other modes of transport. The key question is: How can the planning authority accurately gather data on the total number of incoming tourists? Consider another example of disaster management. During any natural disaster, it is important to plan the evacuation and maintain the supplies to survivors. To plan rescue activities, the disaster management team needs to know the number of people in the affected area. How can the team get a good estimate of the number of people in the affected regions?
Moreover, it is not just the event authority that requires such data. Several other municipal departments, such as the police, public health, tourism, and public works, also rely on up-to-date city-level information to plan and execute their functions effectively.
Interestingly, cellular networks inherently possess valuable insights that can help address these needs. Through routine operations, mobile networks continuously collect service logs, location updates, mobility patterns, and traffic statistics from users' devices. This data, if appropriately processed and aggregated (this includes anonymization), could provide accurate and timely estimates of the number of people arriving in specific locations, possibly their modes of travel, and movement trends across the city.
The data available in the network may not readily reveal the requested information. It may require processing of historical data. Additionally, the processing requires provisioning of computational and storage resources. For example, to estimate the number of tourists visiting a city for an annual event, all the historical and current roaming data need to be processed to extract the numbers.
In this context, this use case presents the requirement for supporting “information as a service” in the 6G network which includes extraction, exposure and computing of the data to derive relevant information.
W.4.2 Pre-conditions
Operator A provides information service to third party applications like city planning authority X. This service is provided to users via an operator portal. The third party X is authorised with the Operator A and registered for this information service with the network.
W.4.3 Service Flows
Figure W.4.3-1: Service flows and examples for “Information as a service”
Figure W.4.3-1 shows the service flows and examples for “Information as a service” support in the 6G network. Following is the description for service flows:
Third party city planning authority X uses the operator portal to submit a query—such as, “How many people have arrived in the city?”
This query is received by the Network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. The network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent converts it into queries that the other relevant 6G network functions can understand.
The network then retrieves large volume of relevant data. It processes this data to extract meaningful insights—for example, the total number of users currently visiting (roaming users) the city. And sends this information to the Network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent.
The Network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent converts this information into simple response format that third party can understand and send it to the third party as response to the input query.
W.4.4 Post-conditions
The planning authority organizes and deploys the appropriate number of buses.
W.4.5 Existing features partly or fully covering the use case functionality
None
W.4.6 Potential New Requirements needed to support the use case
[PR W.4.6-1] Subject to regulation and operator(s) policy, the 6G network shall support a mechanism to expose information derived/processed from the 6G System Data based on the requirement of a third-party application (e.g. user intent).
X Other Considerations
Editor's Note: e.g. charging, etc. if any
X.1 Considerations on Lawful Interception
In addition to the LI requirements specified in TS 22.261 [14] the 6G system shall support Lawful Interception
- Where the 6G system is operated or where the services are provided,
- When (some) network functions are 3rd party provided or operated by different operators,
- In all supported interworking and interoperating scenarios, including roaming (in visited networks)
NOTE: These requirements apply to all services which must comply with the 3GPP SA3-LI specifications, subject to national or regional regulations.
The 6G system shall support the retention of data to meet national or regional regulatory requirements, [314], [315], [313], and [316].
Y Consolidated Potential Requirements
Editor's Note: It needs to be decided whether / how consolidation would be performed.
Z Conclusion and Recommendations
Annex A:
Additional Use Cases
Editor's Note: If there are any use cases that does not fall into any sections in this TR, these use cases could be placed in the Annex, subject to further discussion. How the Annex will be used is FFS.
A.1 Use Case #X
A.2 Use Case #Y
A.3 Use Case #Z
Annex <X>:
Change history
Change history
Date
Meeting
TDoc
CR
Rev
Cat
Subject/Comment
New version
2024-08
SA1#107
First version
0.0.0
2024-11
SA1#108
Output of approved pCRs from SA1 #108. Inclusion from pCRs in S1-244670,S1-244819, S1-244823, S1-244838, S1-244843, S1-244847,S1-244868, S1-244869, S1-244870, S1-244875,S1-344876,S1-244895,S1-244896, S1-244897, S1-244898. S1-244900, S1-244901, S1-244902, S1-244903, S1-244905, S1-244911,S1-344913,S1-244914, S1-244915, S1-244918, S1-244919, S1-244921
0.1.0
2024-12
-
-
“Deep editorial” clean-up (MCC, ETSI, Rapporteurs)
0.1.1
2025-02
SA1#109
Output of approved pCRs from SA1 #109: S1-250500 S1-250511 S1-250514 S1-250538 S1-250541 S1-250550 S1-250553 S1-250554 S1-250559 S1-250563 S1-250566S1-250745S1-250771S1-250774S1-250787S1-250801 S1-250815 S1-250831 S1-250837 S1-250865S1-250946S1-250992S1-250993S1-250994S1-250996S1-250997S1-250999S1-250669S1-250682S1-250686S1-250917S1-250948S1-250949S1-250950S1-250951S1-250991S1-250956S1-250968 S1-250970 S1-250971 S1-250972 S1-250973 S1-250975 S1-250978 S1-250977 S1-250979S1-250989S1-251001S1-251002S1-251003S1-251004S1-251005S1-251006S1-251007S1-251008S1-251010S1-251011S1-251012S1-251013S1-251014 S1-251015
0.2.0
MCC + editors’ clean-up
0.2.1
2025-05
SA1 #110
S1-252934
Output of approved pCRs from SA1 #110: S1-252018S1-252073S1-252156S1-252314S1-252334S1-252349S1-252399S1-252432S1-252436S1-252444S1-252445S1-252463S1-252464S1-252487S1-252503S1-252508S1-252516S1-252524S1-252528S1-252529S1-252530S1-252533S1-252534S1-252535S1-252537S1-252538S1-252540S1-252542S1-252543S1-252551S1-252564S1-252566S1-252567S1-252568S1-252570S1-252573S1-252574S1-252576S1-252577S1-252580S1-252581S1-252582S1-252583S1-252584S1-252585S1-252587S1-252588S1-252589S1-252590S1-252601S1-252602S1-252613S1-252618S1-252628S1-252629S1-252630S1-252632S1-252657S1-252664S1-252677S1-252680S1-252682S1-252683S1-252702S1-252709S1-252715S1-252735S1-252736S1-252745S1-252750S1-252755S1-252769S1-252830S1-252832S1-252841S1-252842S1-252843S1-252847S1-252848S1-252855S1-252856S1-252857S1-252858S1-252860S1-252862S1-252863S1-252864S1-252869S1-252872S1-252873S1-252876S1-252878S1-252880S1-252883S1-252885S1-252887S1-252889S1-252891S1-252892S1-252893S1-252894S1-252895S1-252896S1-252897S1-252898S1-252899S1-252915S1-252921S1-252924S1-252928S1-252936S1-252937S1-252938S1-252941S1-252946S1-252947S1-252948S1-252949S1-252950S1-252951S1-252952S1-252953S1-252956S1-252957S1-252959S1-252960S1-252963S1-252964S1-252965S1-252966S1-252967S1-252968S1-252969S1-252970S1-252971S1-252972
0.3.0
2025-06
-
Fix errors including:
6.6.6-1, 6.11.2, 8.1.1-1, 8.6.5, 8.8.6, 9.1.6-1, 9.5.6, 9.7.1, 11.8.6-1.
Editorial changes including:
3.1, 5.5.5, 5.7.2.1
Reduce File size (reduce pictures quality, fonts not embedded, etc)
0.3.1
2025-09
Output of approved pCRs from SA1 # 111:
S1-253089-S1-253090-S1-253367-S1-253400-S1-253401-
S1-253402-S1-253403-S1-253404-S1-253406-S1-253407-
S1-253408-S1-253662-S1-253410-S1-253411-S1-253412-
S1-253413-S1-253414-S1-253417-S1-253418-S1-253420-
S1-253421-S1-253422-S1-253423-S1-253424-S1-253425-
S1-253426-S1-253427-S1-253450-S1-253451-S1-253452-
S1-253453-S1-253454-S1-253455-S1-253500-S1-253501-
S1-253502-S1-253503-S1-253505-S1-253506-S1-253507-
S1-253509-S1-253510-S1-253514-S1-253515-S1-253517-
S1-253518-S1-253519-S1-253520-S1-253521-S1-253522-
S1-253523-S1-253524-S1-253525-S1-253526-S1-253528-
S1-253529-S1-253530-S1-253531-S1-253532-S1-253533-
S1-253534-S1-253545-S1-253565-S1-253567-S1-253568-
S1-253569-S1-253570-S1-253572-S1-253629-S1-253631-
S1-253648-S1-253649-S1-253650-S1-253651
S1-253053-S1-253122-S1-253190-S1-253382-S1-253384-
S1-253456-S1-253457-S1-253458-S1-253459-S1-253460-
S1-253535-S1-253536-S1-253537-S1-253538-S1-253539-
S1-253540-S1-253541-S1-253542-S1-253544-S1-253546-
S1-253550-S1-253554-S1-253574-S1-253575-S1-253576-
S1-253580-S1-253581-S1-253582-S1-253583-S1-253585-
S1-253586-S1-253587-S1-253634-S1-253635-S1-253636-
S1-253637-S1-253638-S1-253639-S1-253640-S1-253652-
S1-253589-S1-253641-S1-253552-S1-253553-S1-253592-
S1-253593-S1-253594-S1-253595-S1-253642-S1-253602-
S1-253603-S1-253653-S1-253644-S1-253607-S1-253608-
S1-253645-S1-253654-S1-253419-S1-253612-S1-253613-
S1-253614-S1-253616-S1-253647-S1-253626
0.4.0
2025-10
Inclusion of all editorial changes as listed in S1-254009
0.4.1
|
S1-254052.zip
|
2026-01-05 15:48:12
|
S1-254077
|
SA1
|
TSGS1_112_Dallas
|
pCR
|
revised
|
General
|
3GPP TSG-SA WG1 Meeting #112 S1-254077
Dallas, USA, 17-21 November 2025 (revision of S1-25xxxx)
Source: Nokia, Orange, EDF
pCR Title: Update to sustainability overview
Draft Spec: 3GPP TR 22.870 v.0.4.1
Agenda item: 8.1.1
Document for: Approval
Contact: Laurent-Walter Goix
Abstract: This contribution proposes to add some introduction to the sustainability overview.
1. Introduction
Scope of this contribution is to provide an introduction to the various aspects related to sustainability which are already mentioned in various use cases of the TR, i.e. emissions, energy resources, material resources, TCO reduction, trustworthiness, inclusion & equality, health & well-being, education & culture, work & income, infrastructure, food.
2. Reason for Change
Several use cases mention various sustainability-related aspects/topics using the same terms. However these terms are not particularly introduced in the document, which may lead to misunderstandings to the reader. However there is no intention to provide a prescriptive formal definition of these terms.
This contribution aims at providing a breakdown of the overall “sustainability” into several underlying “aspects”; which are already referred to in the document. It also intends to better map with the UN SDGs as a target of 6G.
3. Conclusions
<Conclusion part (optional)>
4. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870 v0.4.1
FIRST CHANGE
4.1 Sustainability
According to the United Nations, “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” [29]
Many related target areas and actions are identified in the United Nations 17 Sustainable Development Goals (UN SDGs) [87], which are categorized into environmental, social and economic goals.
ITU-R has identified “the motivation for the development of IMT-2030 is to continue to build an inclusive information society towards contributing to support the United Nations Sustainable Development Goals (SDGs)." [27] "Sustainability is a foundational aspiration of future IMT systems. IMT-2030 is expected to help address the need for increased environmental, social, and economic sustainability”. [27]
[[SUGGESTION_START]]6G [[SUGGESTION_END]][[SUGGESTION_START]]as envisioned in this document [[SUGGESTION_END]][[SUGGESTION_START]]considers sustainability aspects both in its own technological lifecycle (“Sustainable 6G”) and for its application across industries (“6G for sustainability”). In that sense, 6G can contribute to nearly all the 17[[SUGGESTION_END]] [[SUGGESTION_START]]United Nations’ Sustainable Development Goals[[SUGGESTION_END]] [[SUGGESTION_START]](UN SDGs), directly or indirectly, and benefit people, planet and the economy[[SUGGESTION_END]][[SUGGESTION_START]], as depicted in Figure 4.1-1[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]Throughout this document, [[SUGGESTION_END]][[SUGGESTION_START]]various use cases have been analysed [[SUGGESTION_END]][[SUGGESTION_START]]in relation[[SUGGESTION_END]][[SUGGESTION_START]] to their impact [[SUGGESTION_END]][[SUGGESTION_START]]regarding[[SUGGESTION_END]][[SUGGESTION_START]] sustainability. [[SUGGESTION_END]][[SUGGESTION_START]]Potential impact can relate to benefits, or risks, introduced by the 6G features and services [[SUGGESTION_END]][[SUGGESTION_START]]invo[[SUGGESTION_END]][[SUGGESTION_START]]lv[[SUGGESTION_END]][[SUGGESTION_START]]ed in the use case[[SUGGESTION_END]][[SUGGESTION_START]], which could be mitigated by 3GPP enablers. [[SUGGESTION_END]][[SUGGESTION_START]]Some [[SUGGESTION_END]][[SUGGESTION_START]]common [[SUGGESTION_END]][[SUGGESTION_START]]terms have been used for th[[SUGGESTION_END]][[SUGGESTION_START]]ese analyses[[SUGGESTION_END]][[SUGGESTION_START]] in the various clauses of this document[[SUGGESTION_END]][[SUGGESTION_START]], which[[SUGGESTION_END]][[SUGGESTION_START]] correspond to[[SUGGESTION_END]] [[SUGGESTION_START]]11 sustainability aspects[[SUGGESTION_END]] [[SUGGESTION_START]]mentioned[[SUGGESTION_END]] [[SUGGESTION_START]]in Figure 4.1-1[[SUGGESTION_END]][[SUGGESTION_START]], further indicatively mapped to the [[SUGGESTION_END]][[SUGGESTION_START]]related UN SDGs[[SUGGESTION_END]][[SUGGESTION_START]]. [[SUGGESTION_END]]
[[SUGGESTION_START]]Figure 4.1-1: Sustainability aspects for 6G targeting UN SDGs[[SUGGESTION_END]]
[[SUGGESTION_START]]Each [[SUGGESTION_END]][[SUGGESTION_START]]of the 11 [[SUGGESTION_END]][[SUGGESTION_START]]sustainability aspect[[SUGGESTION_END]][[SUGGESTION_START]]s referenced in this document[[SUGGESTION_END]] [[SUGGESTION_START]]is described [[SUGGESTION_END]][[SUGGESTION_START]]in Tab[[SUGGESTION_END]][[SUGGESTION_START]]le 4.1-1.[[SUGGESTION_END]]
[[SUGGESTION_START]]Table 4.1-1: [[SUGGESTION_END]][[SUGGESTION_START]]Description of s[[SUGGESTION_END]][[SUGGESTION_START]]ustainability aspects[[SUGGESTION_END]]
[[SUGGESTION_START]]Sustainability [[SUGGESTION_END]][[SUGGESTION_START]]a[[SUGGESTION_END]][[SUGGESTION_START]]spect[[SUGGESTION_END]]
[[SUGGESTION_START]]Description[[SUGGESTION_END]]
[[SUGGESTION_START]]Sustainable 6G[[SUGGESTION_END]]
[[SUGGESTION_START]]Emissions[[SUGGESTION_END]]
[[SUGGESTION_START]]to [[SUGGESTION_END]][[SUGGESTION_START]]w[[SUGGESTION_END]][[SUGGESTION_START]]ater, [[SUGGESTION_END]][[SUGGESTION_START]]a[[SUGGESTION_END]][[SUGGESTION_START]]ir, [[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]]oil – including GHG/CO2e emissions, waste and other pollution[[SUGGESTION_END]]
[[SUGGESTION_START]]Energy resources[[SUGGESTION_END]]
[[SUGGESTION_START]]Material resources[[SUGGESTION_END]]
[[SUGGESTION_START]]including use of land, water[[SUGGESTION_END]]
[[SUGGESTION_START]]TCO reduction[[SUGGESTION_END]]
[[SUGGESTION_START]]Trustworthiness[[SUGGESTION_END]]
[[SUGGESTION_START]]including [[SUGGESTION_END]][[SUGGESTION_START]]r[[SUGGESTION_END]][[SUGGESTION_START]]esilience, [[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]]ecurity, [[SUGGESTION_END]][[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]rivacy, [[SUGGESTION_END]][[SUGGESTION_START]]i[[SUGGESTION_END]][[SUGGESTION_START]]ntegrity[[SUGGESTION_END]]
[[SUGGESTION_START]]Inclusion & [[SUGGESTION_END]][[SUGGESTION_START]]e[[SUGGESTION_END]][[SUGGESTION_START]]quality[[SUGGESTION_END]]
[[SUGGESTION_START]]including [[SUGGESTION_END]][[SUGGESTION_START]]a[[SUGGESTION_END]][[SUGGESTION_START]]ffordability, [[SUGGESTION_END]][[SUGGESTION_START]]a[[SUGGESTION_END]][[SUGGESTION_START]]ccessibility[[SUGGESTION_END]]
[[SUGGESTION_START]]6G for [[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]]ustainability[[SUGGESTION_END]]
[[SUGGESTION_START]]Health & [[SUGGESTION_END]][[SUGGESTION_START]]w[[SUGGESTION_END]][[SUGGESTION_START]]ell-being[[SUGGESTION_END]]
[[SUGGESTION_START]]i[[SUGGESTION_END]][[SUGGESTION_START]]ncluding physical & mental [[SUGGESTION_END]][[SUGGESTION_START]]w[[SUGGESTION_END]][[SUGGESTION_START]]ell-[[SUGGESTION_END]][[SUGGESTION_START]]b[[SUGGESTION_END]][[SUGGESTION_START]]eing at individual and social/community level[[SUGGESTION_END]]
[[SUGGESTION_START]]Education & culture[[SUGGESTION_END]]
[[SUGGESTION_START]]Work & income[[SUGGESTION_END]]
[[SUGGESTION_START]]Infrastructure[[SUGGESTION_END]]
[[SUGGESTION_START]]including [[SUGGESTION_END]][[SUGGESTION_START]]h[[SUGGESTION_END]][[SUGGESTION_START]]ousing, [[SUGGESTION_END]][[SUGGESTION_START]]t[[SUGGESTION_END]][[SUGGESTION_START]]ransport, [[SUGGESTION_END]][[SUGGESTION_START]]c[[SUGGESTION_END]][[SUGGESTION_START]]onnectivity, [[SUGGESTION_END]][[SUGGESTION_START]]e[[SUGGESTION_END]][[SUGGESTION_START]]nergy, [[SUGGESTION_END]][[SUGGESTION_START]]w[[SUGGESTION_END]][[SUGGESTION_START]]ater, [[SUGGESTION_END]][[SUGGESTION_START]]w[[SUGGESTION_END]][[SUGGESTION_START]]aste management[[SUGGESTION_END]]
[[SUGGESTION_START]]Food[[SUGGESTION_END]]
Editor's Note: this sub-clause on sustainability may be moved as another 4.x sub-clause or as normal text of the Overview clause.
END OF CHANGES
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S1-254077.zip
|
2026-01-05 15:48:35
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S1-254083
|
SA1
|
TSGS1_112_Dallas
|
pCR
|
revised
|
General
|
3GPP TSG SA WG 1 Meeting #112 S1-254083
17-21 November 2025, Dallas, Texas, USA (revision of S1-25xxxx)
Source: 6G Study Rapporteurs
pCR Title: Proposed Text for Clause 4 (Overview)
Draft Spec: 3GPP TR 22.870v04.1
Agenda item: 8.1.1
Document for: Approval
Contact: Xiaonan Shi (shixiaonan@chinamobile.com) and Jean Trakinat (jean.trakinat1@t-mobile.com)
Abstract: Proposes initial overview text for Clause 4.
1. Introduction
Draft TR 22.870 has no overview text and the plan is to send to SA for information out of this meeting. This pCR provides backgroung from Recommendation ITU-R M.2160-0 that identifies usage scenarios and design guidelines. It is provided as initial/baseline.
This pCR is based on S1-253091, which was presented at SA1 #111 but noted without much discussion.
2. Reason for Change
This pCR proposes
New text for the Overview (Clause 4)
All ENs in this clause are removed.
Clause 4.1heading ( “Sustainability”) is deleted and the existing text is left as part of the overview.
3. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870v0.4.1.
* * * First Change * * * *
4 Overview
[[SUGGESTION_START]]The motivations and hopes for the 6th Generation of the 3GPP begin with societal expectations. Mobile communications have become critical to every-day living, and this dependency will not abate. If anything, the appetite for “anywhere” connectivity is expected to become “everywhere” connectivity, connecting the (previously) unconnected. Additionally, the 6G system is expected, not only to continue to connect people and machines, but to ultimately connect them in immersive and in multi-sensory ways.[[SUGGESTION_END]]
[[SUGGESTION_START]]The various use cases in this study provide a broad range of capabilities and services to identify potential drivers in the development of the 6G System. Some leverage and enhance capabilities and services from previous generations of 3GPP systems while others introduce newer technologies to enhance potential service offerings. Some use cases explicitly address societal needs and capture commercial aspects, while others focus on internal system improvements that seek to increase network capacity and improve network performance.[[SUGGESTION_END]]
[[SUGGESTION_START]]The ITU-R in [27] identified six usage scenarios, to be addressed in 6G[[SUGGESTION_END]]
[[SUGGESTION_START]]Immersive Communication, which expands the capabilities of enhanced Mobile Broadband (eMBBB),[[SUGGESTION_END]]
[[SUGGESTION_START]]Hyper Reliable and Low-Latency Communication, which is an expansion of Ultra-Reliable and Low-Latency Communication (URLCC),[[SUGGESTION_END]]
[[SUGGESTION_START]]Massive Communication, which extends massive Machine Type Communication (mMTC), [[SUGGESTION_END]]
[[SUGGESTION_START]]Ubiquitous Connectivity, to enhance coverage of uncovered or scarcely covered areas (e.g., rural, remote, sparsely populated areas, indoors),[[SUGGESTION_END]]
[[SUGGESTION_START]]Artificial Intelligence (AI) and Communication, to support distributed computing and AI applications, and[[SUGGESTION_END]]
[[SUGGESTION_START]]Integrated Sensing and Communications (ISAC), to offer wide area multi-dimensional sensing and provide spatial information about connected and unconnected objects, devices, their movements, and surroundings.[[SUGGESTION_END]]
[[SUGGESTION_START]]In addition, the ITU-R also identified in [27] the overarching design principles of sustainability, security and resilience, connecting the unconnected for providing universal and affordable access to all users independent of the location, and ubiquitous intelligence for improving overall system performance. The use cases in this study attempt to address the usage scenarios and the design principles to provide meaningful potential requirements for the 6G System.[[SUGGESTION_END]]
According to the United Nations, “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” [29]
Many related target areas and actions are identified in the United Nations 17 Sustainable Development Goals (UN SDGs) [87], which are categorized into environmental, social and economic goals.
ITU-R has identified “the motivation for the development of IMT-2030 is to continue to build an inclusive information society towards contributing to support the United Nations Sustainable Development Goals (SDGs)." [27] "Sustainability is a foundational aspiration of future IMT systems. IMT-2030 is expected to help address the need for increased environmental, social, and economic sustainability”. [27]
[[SUGGESTION_START]]Based on the above [[SUGGESTION_END]][[SUGGESTION_START]]background[[SUGGESTION_END]][[SUGGESTION_START]], 6G scenarios and requirements are studied in this report, including system and operational aspect, AI, integrated sensing and communication, ubiquitous [[SUGGESTION_END]][[SUGGESTION_START]]connectivity[[SUGGESTION_END]][[SUGGESTION_START]], immersive communication, massive communication, [[SUGGESTION_END]][[SUGGESTION_START]]f[[SUGGESTION_END]][[SUGGESTION_START]]urther [[SUGGESTION_END]][[SUGGESTION_START]]u[[SUGGESTION_END]][[SUGGESTION_START]]se [[SUGGESTION_END]][[SUGGESTION_START]]c[[SUGGESTION_END]][[SUGGESTION_START]]ases on [[SUGGESTION_END]][[SUGGESTION_START]]i[[SUGGESTION_END]][[SUGGESTION_START]]ndustry and [[SUGGESTION_END]][[SUGGESTION_START]]v[[SUGGESTION_END]][[SUGGESTION_START]]erticals[[SUGGESTION_END]][[SUGGESTION_START]], and other use cases.[[SUGGESTION_END]]
* * * End of Changes * * * *
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S1-254083.zip
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2026-01-05 15:48:54
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S1-254214
|
SA1
|
TSGS1_112_Dallas
|
pCR
|
revised
|
General
|
3GPP TSG SA WG 1 Meeting #112 S1-254214
17-21 November 2025, Dallas, Texas, USA
Source: Novamint, Thales, TNO, ESA
pCR Title: Pseudo-CR on Annex related to satellite/NTN
Draft Spec: 3GPP TR 22.870 V0.4.1
Agenda item: 8.1.1
Document for: Approval
Contact: Thierry Bérisot (tberisot@novamint.com)
Abstract: This contribution proposes an annex related to satellite/NTN
1. Introduction
The 6G system supports satellite natively as well as new associated requirements. An explaination on what are the characteritics of satellite/NTN is missing.
2. Reason for Change
Introduce an informative annex related to satellite/NTN
3. Conclusions
None
4. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870 v0.4.1
* * * First Change * * * *
Annex X (Informative):
Non-Terrestrial Network (NTN)
X.1 What is NTN
Since Release 17, 3GPP introduced 3GPP NTN (Non-Terrestrial Network) to support satellite or HAPS based radio access network as part of 3GPP with 2 defined radio access technologies that have been enhanced with NTN capabilities:
IoT-NTN as part of the Massive Internet of Things
NR-NTN as part of the 5G NR radio interface family
X.2 Main characteristics of satellite access networks
X.2.1 Class of orbit
There are several classes of orbits that are expected to be supported by a system using satellite access:
Geostationary/Geosynchronous orbiting:
GEO (Geostationary Earth Orbit): Circular orbit 35.786 km above the Earth's equator (Note: Due to gravitational forces a GEO satellite is still moving within a range of a few km around its nominal orbital position).
GSO (Geosynchronous Orbit): GSO differs from GEO by a freedom to orbit around the 0° latitude. This allows to optimize the satellite lifetime by optimizing the necessary fuel for station keeping.
In the study, the terms GEO (geostationary) and GSO (geosynchronous) are equivalent, unless indicated otherwise
Non-Geostationary Orbiting (NGSO) satellites: NGSO satellites do not stand still with respect to Earth. Should service continuity be required over time, several satellites (a constellation) is required to meet this requirement, the lower the altitude the higher the number of satellites. Different classes of NGSO satellites are listed below:
VLEO (Very Low Earth Orbit): Circular orbit in altitudes of typ. 180-600km (reduced latency due to their proximity to the surface however the lower the altitude, the higher the number of satellites is required to ensure service continuity and the higher the atmospheric drag which would need constant propulsion and re-boosting and hence reduced life duration).
LEO (Low Earth Orbit): Circular orbit in altitudes of typ. 500-2.000km (lower delay and better link budget but larger number of satellites needed for coverage)
MEO (Medium Earth Orbit): Circular orbit in altitudes of typ. 8.000-20.000km
HEO (Highly Elliptical Orbiting): Elliptical orbit around the earth.
An illustration of some of those orbits is provided with the following figure:
Figure X.2.1-1: Illustration of the classes of orbits of satellites
Each of these satellite orbit types can have different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) to provide efficient and multiple services.
X.2.2 Constellation
A satellite constellation is a set of satellites working together as a system or network. A satellite constellation is composed of satellites in the same orbit types.
Typical sizing of constellation according to the orbit:
GSO constellation may be based on one to 3 satellites depending on the targeted coverage
NGSO constellations providing real time services are sized to ensure at least one satellite in visibility of all UEs (above the min elevation angle) in the targeted coverage
NGSO constellations providing non-real-time services such as messaging shall be made of at least one satellite. A given UE can connect to the satellite from time to time depending on the satellite revisit time.
A large satellite constellation composed of hundreds, even thousands of satellites is called a Mega constellation.
A set of NTN constellations in different orbits (e.g. GSO, NGSOs) working together as a system or network is called Multi-orbit (or Multi-Layer) NTN.
X.2.3 Inter-Satellite Link
Inter-Satellite Link (ISL) refers to the means to provide communication between satellites without the need to use a ground station. The support of ISLs is needed between two satellites of the same constellation (intra-/inter- orbital plane), between two satellites/NTN of different constellations/orbits (e.g., between NGSO and GSO satellites). ISL allows to provide service in areas where there are no NTN-gateways and to provide coverage of remote areas like ocean.
* * * End of Change * * * *
|
S1-254214.zip
|
2026-01-05 15:49:15
|
S1-254280
|
SA1
|
TSGS1_112_Dallas
|
pCR
|
revised
|
General
|
3GPP TSG-SA WG1 Meeting #112 S1-254280
Dallas, TX, USA, 17-21 November 2025
Source: Novamint, Thales, TNO, ESA
pCR Title: Pseudo-CR on Annex related to satellite/NTN
Draft Spec: 3GPP TR 22.870 V0.4.1
Agenda item: 8.1.1
Document for: Approval
Contact: Thierry Bérisot (tberisot@novamint.com)
Abstract: This contribution proposes an annex related to satellite/NTN
1. Introduction
The 6G system supports satellite natively as well as new associated requirements. An explaination on what are the characteritics of satellite/NTN is missing.
2. Reason for Change
Introduce an informative annex related to satellite/NTN
3. Conclusions
None
4. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870 v0.4.1
* * * First Change * * * *
Annex X (Informative):
Non-Terrestrial Network (NTN)
X.1 What is NTN
Since Release 17, 3GPP introduced 3GPP NTN (Non-Terrestrial Network) to support satellite or HAPS based radio access network as part of 3GPP with 2 defined radio access technologies that have been enhanced with NTN capabilities:
IoT-NTN as part of the Massive Internet of Things
NR-NTN as part of the 5G NR radio interface family
X.2 Main characteristics of satellite access networks
X.2.1 Class of orbit
There are several classes of orbits that are expected to be supported by a system using satellite access:
Geostationary/Geosynchronous orbiting:
GEO (Geostationary Earth Orbit): Circular orbit 35.786 km above the Earth's equator (Note: Due to gravitational forces a GEO satellite is still moving within a range of a few km around its nominal orbital position).
GSO (Geosynchronous Orbit): GSO differs from GEO by a freedom to orbit around the 0° latitude. This allows to optimize the satellite lifetime by optimizing the necessary fuel for station keeping.
In the study, the terms GEO (geostationary) and GSO (geosynchronous) are equivalent, unless indicated otherwise
Non-Geostationary Orbiting (NGSO) satellites: NGSO satellites do not stand still with respect to Earth. Should service continuity be required over time, several satellites (a constellation) is required to meet this requirement, the lower the altitude the higher the number of satellites. Different classes of NGSO satellites are listed below:
VLEO (Very Low Earth Orbit): Circular orbit in altitudes of typ. 180-600km (reduced latency due to their proximity to the surface however the lower the altitude, the higher the number of satellites is required to ensure service continuity and the higher the atmospheric drag which would need constant propulsion and re-boosting and hence reduced life duration).
LEO (Low Earth Orbit): Circular orbit in altitudes of typ. 500-2.000km (lower delay and better link budget but larger number of satellites needed for coverage)
MEO (Medium Earth Orbit): Circular orbit in altitudes of typ. 8.000-20.000km
HEO (Highly Elliptical Orbiting): Elliptical orbit around the earth.
An illustration of some of those orbits is provided with the following figure:
Figure X.2.1-1: Illustration of the classes of orbits of satellites
Each of these satellite orbit types can have different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) to provide efficient and multiple services.
X.2.2 Constellation
A satellite constellation is a set of satellites working together as a system or network. A satellite constellation is composed of satellites in the same orbit types.
Typical sizing of constellation according to the orbit:
GSO constellation may be based on one to 3 satellites depending on the targeted coverage
NGSO constellations providing real time services are sized to ensure at least one satellite in visibility of all UEs (above the min elevation angle) in the targeted coverage
NGSO constellations providing non-real-time services such as messaging shall be made of at least one satellite. A given UE can connect to the satellite from time to time depending on the satellite revisit time.
A large satellite constellation composed of hundreds, even thousands of satellites is called a Mega constellation.
A set of NTN constellations in different orbits (e.g. GSO, NGSOs) working together as a system or network is called Multi-orbit (or Multi-Layer) NTN.
X.2.3 Inter-Satellite Link
Inter-Satellite Link (ISL) refers to the means to provide communication between satellites without the need to use a ground station. The support of ISLs is needed between two satellites of the same constellation (intra-/inter- orbital plane), between two satellites/NTN of different constellations/orbits (e.g., between NGSO and GSO satellites). ISL allows to provide service in areas where there are no NTN-gateways and to provide coverage of remote areas like ocean.
* * * End of Change * * * *
|
S1-254280.zip
|
2026-01-05 15:49:36
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S1-254434
|
SA1
|
TSGS1_112_Dallas
|
pCR
|
revised
|
General
|
3GPP TSG SA WG 1 Meeting #112 S1-254[[SUGGESTION_START]]434[[SUGGESTION_END]]
17-21 November 2025, Dallas, Texas, USA (revision of S1-254[[SUGGESTION_START]]349[[SUGGESTION_END]]_was_S1-253163r5)
Source: Verizon, Samsung, AT&T, T-Mobile USA, Deutsche Telekom, China Mobile, NTT DOCOMO[[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] Nokia[[SUGGESTION_END]]
pCR Title: Use case on of an Autonomous Network Management– a request to study
Draft Spec: 3GPP TR 22.870-V0.4.1
Agenda item: 8.1.2.2
Document for: Approval
Contact: samita.chakrabarti@verizon.com
Abstract: An autonomous management of the network, based on real-time/dynamic conditions is considered to use the standardized management [[SUGGESTION_START]]system[[SUGGESTION_END]]. The proposal requests a feasibility study that may not be possible to do at the stage1 level.
1. Introduction
This revision [[SUGGESTION_START]]addresses comments receievd d[[SUGGESTION_END]][[SUGGESTION_START]]uring the meeting[[SUGGESTION_END]].
2. Reason for Change
Editorial changes, clarification of the intent of this proposal to 3GPP and SA1 audience.
[[SUGGESTION_START]]Other changes [[SUGGESTION_END]][[SUGGESTION_START]]address [[SUGGESTION_END]][[SUGGESTION_START]]from[[SUGGESTION_END]][[SUGGESTION_START]] version r1 to r2:[[SUGGESTION_END]]
[[SUGGESTION_START]]Walter[[SUGGESTION_END]] [[SUGGESTION_START]](Nokia)[[SUGGESTION_END]][[SUGGESTION_START]]– for consistency, [[SUGGESTION_END]][[SUGGESTION_START]]change [[SUGGESTION_END]][[SUGGESTION_START]]AI-enabled to “autonomous” whenever possible in the proposal[[SUGGESTION_END]] [[SUGGESTION_START]]+ add as a supporter[[SUGGESTION_END]]
[[SUGGESTION_START]]Amanda and Shuang – PR3[[SUGGESTION_END]][[SUGGESTION_START]] – needs clarification[[SUGGESTION_END]][[SUGGESTION_START]] – done ( Thanks to Jean for the text)[[SUGGESTION_END]]
[[SUGGESTION_START]]Shuang – PR1 ( go back to the original [[SUGGESTION_END]][[SUGGESTION_START]]253163r5 PR1)[[SUGGESTION_END]] [[SUGGESTION_START]]–[[SUGGESTION_END]] done
[[SUGGESTION_START]]Shuang – [[SUGGESTION_END]][[SUGGESTION_START]]Simplify or remove[[SUGGESTION_END]][[SUGGESTION_START]] th[[SUGGESTION_END]][[SUGGESTION_START]]e diagram( as it may be confusing)[[SUGGESTION_END]][[SUGGESTION_START]] – [[SUGGESTION_END]]removed
[[SUGGESTION_START]]Re[[SUGGESTION_END]][[SUGGESTION_START]]moved – reference to SA2, SA5 ( from previous comments[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]Lola- PR1 – replace AIML capability with Autonomous capability[[SUGGESTION_END]][[SUGGESTION_START]] + NOTE1[[SUGGESTION_END]]
3. Conclusions
The key goal of the outcome is to aim for a clear understanding of what autonomous management means & how it relates to the centralized OAM that exists already,.
4. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870
2 References
[77] 3GPP TR 28.915: "Study on management aspects of Network Digital Twin".
[147] 3GPP TS 28.312: "Management and orchestration; Intent driven management services for mobile networks
* * * First Change * * * *
5.x.1 Use case on Autonomous Network Management
5.x.1.1 Description
6G network will support new types of applications and services requiring the network to deliver on diverse and often guaranteed requirements, it is critical for Network Operators to be able to manage, assure and optimize the wireless network in real time with increased autonomy. A standard defined set of management servicesare required between the [[SUGGESTION_START]]A[[SUGGESTION_END]]utonomous Network Management system and the 3GPP 6G System Network Functions[[SUGGESTION_START]].[[SUGGESTION_END]] [[SUGGESTION_START]]The support of new mechanisms of communication between the two entities are expected to realize autonomous configuration and control of the networks which may use new generation applications, or digital twining of the network (see clause 5.9.3). [[SUGGESTION_END]] The issue is, the current[[SUGGESTION_START]] Network[[SUGGESTION_END]] [[SUGGESTION_START]]M[[SUGGESTION_END]]anagement[[SUGGESTION_START]] System[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]] are designed for pre-configured static operations and such [[SUGGESTION_START]]mechanis[[SUGGESTION_END]][[SUGGESTION_START]]ms[[SUGGESTION_END]] are not easy to be utilized directly from the [[SUGGESTION_START]]A[[SUGGESTION_END]][[SUGGESTION_START]]utonomous [[SUGGESTION_END]]network management (e.g. [[SUGGESTION_START]] AI-enabled, intent-based, Agents etc[[SUGGESTION_END]]). To realize autonomous network management across multiple vendors/domains, Network Functions (NF) need to be equipped with new standardized management functionalities that can be easily utilized by the AI-enabled[[SUGGESTION_START]]/intent-based[[SUGGESTION_END]] network management. Thus, there is a gap [[SUGGESTION_START]]in capabilities[[SUGGESTION_END]] [[SUGGESTION_START]]of [[SUGGESTION_END]][[SUGGESTION_START]]Network Management System [[SUGGESTION_END]]toward the 6G [[SUGGESTION_START]]Networks[[SUGGESTION_END]]. This gap can be addressed by enhancing the interfaces for supporting the new generation of use cases involving dynamic real-time changes to network.
A few examples of the interface usages follow:
Imperative type of interfacing: The management services request services via exact instructions for configurations of the 6G Network system.
The declarative type of interfacing: The management services allow request to be made to the 6G network functions to achieve given objectives (e,g. intent-based requests [147]) within optionally provided [[SUGGESTION_START]]Operator specific [[SUGGESTION_END]]guidelines by the exposure interface. This type of services is largely considered declarative.
Due to the nature of dynamically generated applications and intent based services, a set of new Network Management exposure functions and interfaces are required between the [[SUGGESTION_START]]6G[[SUGGESTION_END]]Network and the network management system for network visibility and real-time network configuration adjustment for the trusted and authorized applications.
These interfaces between[[SUGGESTION_START]] Network Management Systems[[SUGGESTION_END]] and 6G network are requested to be studied by the appropriate downstream groups in order to allow autonomous control of network behavior in response to changing network conditions at different levels of granularity (e.g. sector-carrier level, UE level, session level). The network management system may also leverage standard defined performance measurement counters and KPIs, as well as inferred data such as analytics as input/observation data[[SUGGESTION_START]]. [[SUGGESTION_END]]
5.x.1.2 Pre-conditions
[[SUGGESTION_START]]Example: [[SUGGESTION_END]]A larger than anticipated influx of crowd is predicted or observed at an event venue, where the 6G network provider servicing the venue, designed and configured it based on the originally anticipated crowd and traffic volume. Similarly, less than anticipated crowd and traffic volume may be possible to due various reasons, in those cases, proper network resource usage is useful for efficiency of the Operators Network
5.x.1.3 Service Flows
The management system obtains observation or prediction of larger than expected influx of crowd at an event venue based on the automatic input from AIML functions, sensing, sensor data, Radio performance data etc.
The management system invokes management service exposed by the 6G network to adjust resource allocation for serving the venue based on new information on current or predicted device numbers and traffic volume.
The 6G network increases resource allocation of the venue based on provided information (device numbers, traffic volume, coverage etc.) and updates the internal policies (which may be initially provided, and now updated by the management system).
5.x.1.4 Post-conditions
The venue 6G network continues to successfully deliver services meeting all service requirements and KPIs with appropriately adjusted network resources, to the additional authorized venue participants
5.x.1.5 Existing features partly or fully covering the use case functionality
Today, there is no standardized interface defined between the AI-enabled Network Management system framework, and the [[SUGGESTION_START]]3GPP 6G [[SUGGESTION_END]]Network which can support real-time network changes when needed. Current [[SUGGESTION_START]]mechanisms[[SUGGESTION_END]] are able to handle pre-planned configurations and change-requests to the 3GPP Networks.
5.x.1.6 Potential New Requirements needed to support the use case
[[SUGGESTION_START]][PR 5.x.6-1] Based on the operator policy, the 6G network shall support Network Management system with A[[SUGGESTION_END]][[SUGGESTION_START]]utonom[[SUGGESTION_END]][[SUGGESTION_START]]o[[SUGGESTION_END]][[SUGGESTION_START]]us[[SUGGESTION_END]] [[SUGGESTION_START]]capability[[SUGGESTION_END]][[SUGGESTION_START]] ( e,g AI-enabled[[SUGGESTION_END]][[SUGGESTION_START]], Rule based etc.)[[SUGGESTION_END]]
[[SUGGESTION_START]][PR 5.x.6-[[SUGGESTION_END]][[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]]] The 6G network shall be able to support both autonomous network management and legacy network management capabilities.[[SUGGESTION_END]]
NOTE1: [[SUGGESTION_START]]An autonomous [[SUGGESTION_END]] Network Management Systems [[SUGGESTION_START]]can potentially [[SUGGESTION_END]]use [[SUGGESTION_START]]mechanisms[[SUGGESTION_END]] (e.g, [[SUGGESTION_START]] AI-agents, rule-based or intent-based frameworks, Netconf, etc[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]] ) in order to directly communicate with 6G network functions, or Application functions to communicate the intent, parameters, KPI, new data models to support the dynamic network changes based on the use-cases in clause 6 and clause 5.9 [[SUGGESTION_START]] in the TR 22.870 document. A 3GPP study is requested to understand the feasibility of dynamic changes of network configuration, policy, QoS etc. and therefore identifying the suitable recommendations for [[SUGGESTION_END]][[SUGGESTION_START]]mechanisms ([[SUGGESTION_END]][[SUGGESTION_START]]e,g, [[SUGGESTION_END]][[SUGGESTION_START]]protocols, AI methods, interfaces[[SUGGESTION_END]][[SUGGESTION_START]] etc.)[[SUGGESTION_END]][[SUGGESTION_START]] to achieve the autonomous management. Note, this study is not feasible to perform at stage1.[[SUGGESTION_END]]
NOTE2: The intent of these requirements is to provide input to the[[SUGGESTION_START]] 3GPP[[SUGGESTION_END]] 6G[[SUGGESTION_START]] studies[[SUGGESTION_END]] as a priority for standardized use-cases in clause 6 and clause 5.9 in the [[SUGGESTION_START]]TR 22.870 [[SUGGESTION_END]] study document[[SUGGESTION_START]].[[SUGGESTION_END]]
|
S1-254434.zip
|
2026-01-05 15:49:59
|
S1-254433
|
SA1
|
TSGS1_112_Dallas
|
pCR
|
approved
|
General
|
3GPP TSG-SA WG1 Meeting #112 S1-254433
Dallas, TX, USA, 17-21 November 2025
Source: Novamint, Thales, TNO, ESA, SoftBank Corp.
pCR Title: Pseudo-CR on Annex related to satellite/NTN
Draft Spec: 3GPP TR 22.870 V0.4.1
Agenda item: 8.1.1
Document for: Approval
Contact: Thierry Bérisot (tberisot@novamint.com)
Abstract: This contribution proposes an annex related to satellite/NTN
1. Introduction
The 6G system supports satellite natively as well as new associated requirements. An explaination on what are the characteritics of satellite/NTN is missing.
2. Reason for Change
Introduce an informative annex related to satellite/NTN
Merged S1-254175 into this contribution as an additional subclause to the NTN annex.
3. Conclusions
None
4. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870 v0.4.1
* * * First Change * * * *
Annex TBD (Informative):
Non-Terrestrial Network (NTN)
X.1 What is NTN
Since Release 17, 3GPP introduced 3GPP NTN (Non-Terrestrial Network) to support satellite or HAPS based radio access network as part of 3GPP with 2 defined radio access technologies that have been enhanced with NTN capabilities:
IoT-NTN as part of the Massive Internet of Things
NR-NTN as part of the 5G NR radio interface family
X.2 Main characteristics of satellite access networks
X.2.1 Class of orbit
There are several classes of orbits that are expected to be supported by a system using satellite access:
Geostationary/Geosynchronous orbiting:
GEO (Geostationary Earth Orbit): Circular orbit 35.786 km above the Earth's equator (Note: Due to gravitational forces a GEO satellite is still moving within a range of a few km around its nominal orbital position).
GSO (Geosynchronous Orbit): GSO differs from GEO by a freedom to orbit around the 0° latitude. This allows to optimize the satellite lifetime by optimizing the necessary fuel for station keeping.
In the study, the terms GEO (geostationary) and GSO (geosynchronous) are equivalent, unless indicated otherwise
Non-Geostationary Orbiting (NGSO) satellites: NGSO satellites do not stand still with respect to Earth. Should service continuity be required over time, several satellites (a constellation) is required to meet this requirement, the lower the altitude the higher the number of satellites. Different classes of NGSO satellites are listed below:
VLEO (Very Low Earth Orbit): Circular orbit in altitudes of typ. 180-600km (reduced latency due to their proximity to the surface however the lower the altitude, the higher the number of satellites is required to ensure service continuity and the higher the atmospheric drag which would need constant propulsion and re-boosting and hence reduced life duration).
LEO (Low Earth Orbit): Circular orbit in altitudes of typ. 500-2.000km (lower delay and better link budget but larger number of satellites needed for coverage)
MEO (Medium Earth Orbit): Circular orbit in altitudes of typ. 8.000-20.000km
HEO (Highly Elliptical Orbiting): Elliptical orbit around the earth.
An illustration of some of those orbits is provided with the following figure:
Figure X.2.1-1: Illustration of the classes of orbits of satellites
Each of these satellite orbit types can have different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) to provide efficient and multiple services.
X.2.2 Constellation
A satellite constellation is a set of satellites working together as a system or network. A satellite constellation is composed of satellites in the same orbit types.
Typical sizing of constellation according to the orbit:
GSO constellation may be based on one to 3 satellites depending on the targeted coverage
NGSO constellations providing real time services are sized to ensure at least one satellite in visibility of all UEs (above the min elevation angle) in the targeted coverage
NGSO constellations providing non-real-time services such as messaging shall be made of at least one satellite. A given UE can connect to the satellite from time to time depending on the satellite revisit time.
A large satellite constellation composed of hundreds, even thousands of satellites is called a Mega constellation.
When a constellation is composed of a minimum number of satellites allowing to operate without having a dense deployment, such constellation is called a sparse constellation.
A set of NTN constellations in different orbits (e.g. GSO, NGSOs) working together as a system or network is called Multi-orbit (or Multi-Layer) NTN.
X.2.3 Inter-Satellite Link
Inter-Satellite Link (ISL) refers to the means to provide communication between satellites without the need to use a ground station. The support of ISLs is needed between two satellites of the same constellation (intra-/inter- orbital plane), between two satellites/NTN of different constellations/orbits (e.g., between NGSO and GSO satellites). ISL allows to provide service in areas where there are no NTN-gateways and to provide coverage of remote areas like ocean.
X.3 High Altitude Platform Station (HAPS)
X.3.1 HAPS Fundamentals
High Altitude Platform Station (HAPS) is defined by the International Telecommunications Union (ITU) as "a station on an object at an altitude of 20 to 50 km and at a specified, nominal, fixed point relative to the Earth." This operational altitude, within the stratosphere, allows a single platform to provide wide-area coverage, making it an ideal solution to complement traditional terrestrial networks, especially in remote and rural areas where people still lack reliable internet.
HAPS platforms are not a single technology but a system of systems. These systems are broadly divided into (a) Aviation Systems, which cover the flight vehicle, energy, and navigation, and (b) Service Systems, which include the communications payload and its connection to the core network. The most relevant aspects lie within the service systems, where familiar architectural principles are adapted for a stratospheric environment.
X.3.2 HAPS: characteristics with respect to satellites
HAPS will be operating in the stratosphere at approximately 20 km altitude, that is much lower than satellites such as Low Earth Orbit (LEO) at 500+ km and Geostationary (GEO) at over 35,000 km. The fundamental difference is the propagation distance. A HAPS service link of 20 km is comparable to long-distance links in many terrestrial mobile networks. This proximity to Earth, combined with the favourable propagation conditions of an earth-sky link (free from ground clutter), is the key to its performance.
This translates mobile network performance compared to satellite-based solutions as summarized in Table TBD.2-1:
Table TBD.2-1: Relative characteristics of GEO, MEO, LEO, VLEO and HAPS
GEO
MEO
LEO
VLEO
HAPS
Altitude (km)
35.786
8.000 – 20.000
600 – 1.500
300 - 600
≈ 20
Typical RTT (ms)
485 - 550
100 - 400
9 - 57
TBD
< 10
* * * End of Change * * * *
|
S1-254433.zip
|
2026-01-05 16:00:25
|
S1-254431
|
SA1
|
TSGS1_112_Dallas
|
pCR
|
approved
|
General
|
3GPP TSG SA WG 1 Meeting #112 S1-254431
17-21 November 2025, Dallas, Texas, USA (revision of S1-254083, S1-254083r1)
Source: 6G Study Rapporteurs
pCR Title: Proposed Text for Clause 4 (Overview)
Draft Spec: 3GPP TR 22.870v04.1
Agenda item: 8.1.1
Document for: Approval
Contact: Xiaonan Shi (shixiaonan@chinamobile.com) and Jean Trakinat (jean.trakinat1@t-mobile.com)
Abstract: Proposes initial overview text for Clause 4.
1. Introduction
Draft TR 22.870 has no overview text and the plan is to send to SA for information out of this meeting. This pCR provides backgroung from Recommendation ITU-R M.2160-0 that identifies usage scenarios and design guidelines. It is provided as initial/baseline.
This pCR is based on S1-253091, which was presented at SA1 #111 but noted without much discussion.
2. Reason for Change
This pCR proposes
New text for the Overview (Clause 4)
All ENs in this clause are removed.
Clause 4.1 heading ( “Sustainability”) is deleted and the existing text is left as part of the overview.
[[SUGGESTION_START]]R1: based on discussions in the “General” drafting session,[[SUGGESTION_END]][[SUGGESTION_START]] the rapporteurs were asked[[SUGGESTION_END]][[SUGGESTION_START]] to address the following comments: [[SUGGESTION_END]]
[[SUGGESTION_START]]Some linkage is needed before " The ITU-R in [27] identified six usage scenarios, to be addressed in 6G" [Samsung][[SUGGESTION_END]]
[[SUGGESTION_START]]Limit the scope of the change to the overview.[[SUGGESTION_END]]
[[SUGGESTION_START]]Changes made:[[SUGGESTION_END]]
[[SUGGESTION_START]]Inserted a new Clause 4.1 “General” to address hanging paragraph and renumbered 4.1 Sustainability to 4.2[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]Editor’s Note: the clause header[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]] may be removed if the sustainability text is moved to another clause.[[SUGGESTION_END]]
[[SUGGESTION_START]]Added l[[SUGGESTION_END]][[SUGGESTION_START]]ead in/[[SUGGESTION_END]][[SUGGESTION_START]]transition text added to third paragraph (based on off-line proposals and the SID text[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]eMBBB is corrected to eMBB (first bullet)[[SUGGESTION_END]][[SUGGESTION_START]] and [[SUGGESTION_END]][[SUGGESTION_START]]URLCC to [[SUGGESTION_END]][[SUGGESTION_START]]URLLC (in 2nd bullet)[[SUGGESTION_END]]
[[SUGGESTION_START]]Moved the last paragraph to the end of clause 4.1 (General).[[SUGGESTION_END]]
3. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870v0.4.1.
* * * First Change * * * *
4 Overview
[[SUGGESTION_START]]4.1 General[[SUGGESTION_END]]
[[SUGGESTION_START]]The motivations and hopes for the 6th Generation of the 3GPP begin with societal expectations. Mobile communications have become critical to every-day living, and this dependency will not abate. If anything, the appetite for “anywhere” connectivity is expected to become “everywhere” connectivity, connecting the (previously) unconnected. Additionally, the 6G system is expected, not only to continue to connect people and machines, but to ultimately connect them in immersive and in multi-sensory ways.[[SUGGESTION_END]]
[[SUGGESTION_START]]The various use cases in this study provide a broad range of capabilities and services to identify potential drivers in the development of the 6G System. Some leverage and enhance capabilities and services from previous generations of 3GPP systems while others introduce newer technologies to enhance potential service offerings. Some use cases explicitly address societal needs and capture commercial aspects, while others focus on internal system improvements that seek to increase network capacity and improve network performance.[[SUGGESTION_END]]
[[SUGGESTION_START]]6G aims to support societal advancements and to bring value to society in the 2030s and beyond in secure, resilient, environmentally and economically sustainable ways. In addition to new 6G services, other considerations are needed, e.g. CAPEX/OPEX reduction, improvement of overall 3GPP system performance, and migration from and interworking with 5G aspects.[[SUGGESTION_END]][[SUGGESTION_START]] The study [[SUGGESTION_END]][[SUGGESTION_START]]is [[SUGGESTION_END]][[SUGGESTION_START]]structured along the lines of the six usage scenarios, to be addressed in 6G, as identified by t[[SUGGESTION_END]][[SUGGESTION_START]]he ITU-R in [27][[SUGGESTION_END]][[SUGGESTION_START]]:[[SUGGESTION_END]]
[[SUGGESTION_START]]Immersive Communication, which expands the capabilities of enhanced Mobile Broadband (eMBB),[[SUGGESTION_END]]
[[SUGGESTION_START]]Hyper Reliable and Low-Latency Communication, which is an expansion of Ultra-Reliable and Low-Latency Communication (UR[[SUGGESTION_END]][[SUGGESTION_START]]LL[[SUGGESTION_END]][[SUGGESTION_START]]C),[[SUGGESTION_END]]
[[SUGGESTION_START]]Massive Communication, which extends massive Machine Type Communication (mMTC), [[SUGGESTION_END]]
[[SUGGESTION_START]]Ubiquitous Connectivity, to enhance coverage of uncovered or scarcely covered areas (e.g. rural, remote, sparsely populated areas, indoors),[[SUGGESTION_END]]
[[SUGGESTION_START]]Artificial Intelligence (AI) and Communication, to support distributed computing and AI applications, and[[SUGGESTION_END]]
[[SUGGESTION_START]]Integrated Sensing and Communications (ISAC), to offer wide area multi-dimensional sensing and provide spatial information about connected and unconnected objects, devices, their movements, and surroundings.[[SUGGESTION_END]]
[[SUGGESTION_START]]In addition, the ITU-R also identified in [27] the overarching design principles of sustainability, security and resilience, connecting the unconnected for providing universal and affordable access to all users independent of the location, and ubiquitous intelligence for improving overall system performance. The use cases in this study attempt to address the usage scenarios and the design principles to provide meaningful potential requirements for the 6G System.[[SUGGESTION_END]]
Based on the above background, 6G scenarios and requirements are studied in this report, including system and operational aspect, AI, integrated sensing and communication, ubiquitous connectivity, immersive communication, massive communication, further use cases on industry and verticals, and other use cases.
4.[[SUGGESTION_START]]2[[SUGGESTION_END]] Sustainability
According to the United Nations, “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” [29]
Many related target areas and actions are identified in the United Nations 17 Sustainable Development Goals (UN SDGs) [87], which are categorized into environmental, social and economic goals.
ITU-R has identified “the motivation for the development of IMT-2030 is to continue to build an inclusive information society towards contributing to support the United Nations Sustainable Development Goals (SDGs)." [27] "Sustainability is a foundational aspiration of future IMT systems. IMT-2030 is expected to help address the need for increased environmental, social, and economic sustainability”. [27]
Editor's Note: this sub-clause on sustainability may be moved as another 4.x sub-clause or as normal text of the Overview clause.
[[SUGGESTION_START]]Based on the above [[SUGGESTION_END]][[SUGGESTION_START]]background[[SUGGESTION_END]][[SUGGESTION_START]], 6G scenarios and requirements are studied in this report, including system and operational aspect, AI, integrated sensing and communication, ubiquitous [[SUGGESTION_END]][[SUGGESTION_START]]connectivity[[SUGGESTION_END]][[SUGGESTION_START]], immersive communication, massive communication, [[SUGGESTION_END]][[SUGGESTION_START]]f[[SUGGESTION_END]][[SUGGESTION_START]]urther [[SUGGESTION_END]][[SUGGESTION_START]]u[[SUGGESTION_END]][[SUGGESTION_START]]se [[SUGGESTION_END]][[SUGGESTION_START]]c[[SUGGESTION_END]][[SUGGESTION_START]]ases on [[SUGGESTION_END]][[SUGGESTION_START]]i[[SUGGESTION_END]][[SUGGESTION_START]]ndustry and [[SUGGESTION_END]][[SUGGESTION_START]]v[[SUGGESTION_END]][[SUGGESTION_START]]erticals[[SUGGESTION_END]][[SUGGESTION_START]], and other use cases.[[SUGGESTION_END]]
* * * End of Changes * * * *
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S1-254431.zip
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2026-01-05 16:06:36
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S1-254330
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SA1
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TSGS1_112_Dallas
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pCR
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approved
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Next meetings (calendar)
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3GPP TSG-SA WG1 Meeting #112 S1-254330
Dallas, USA, 17-21 November 2025 (revision of S1-254207, S1-253658)
Source: Nokia
pCR Title: Fix EN on diverse UE types 5.10.1
Draft TS/TR: 3GPP TR 22.870 v0.4.1
Agenda Item: 8.1.2
Document for: Approval
Contact: Laurent-Walter Goix
Abstract: This contribution proposes to solve EN #15.
[[SUGGESTION_START]]Rev 4330[[SUGGESTION_END]]
[[SUGGESTION_START]]PR1 u[[SUGGESTION_END]][[SUGGESTION_START]]pdated as per agreed edited text during the drafting session[[SUGGESTION_END]]
[[SUGGESTION_START]]R_SA1#112[[SUGGESTION_END]]
[[SUGGESTION_START]]Clarified the distinction between charac[[SUGGESTION_END]][[SUGGESTION_START]]teristics and needs[[SUGGESTION_END]]
[[SUGGESTION_START]]Added reference to RAN studies[[SUGGESTION_END]]
R2
[[SUGGESTION_START]]Updated PR2 as requested with wording related to RAN study[[SUGGESTION_END]]
[[SUGGESTION_START]]Added Qualcomm as cosigner[[SUGGESTION_END]]
[[SUGGESTION_START]]Removed changes on changes[[SUGGESTION_END]]
R1
Updated examples of UEs: changed M-IoT to IoT [Huawei], changed glasses to XR glasses [China Unicom], added wearables [NTT DoCoMo]
Updated PR2 wording: “support UEs with…” [Qualcomm], removed “low-power” [Huawei]
---------- Use Case template ----------
* * * First Change * * * *
5.10.1 Continued support for diverse UE types
5.10.1.1 Description
It is envisioned that a next generation system will continue to support a population of UEs with varying capabilities that would support [[SUGGESTION_START]]a range of [[SUGGESTION_END]]use cases. For example, simple UEs with limited capabilities would be supported by the 6G network, alongside more sophisticated UEs [[SUGGESTION_START]]offering[[SUGGESTION_END]] more advanced features and capabilities.
To illustrate this point further, simple UEs could have lower throughput, lower bandwidth, lower power consumption and lower processing power capabilities. Other more sophisticated UEs could have higher throughput, higher bandwidth, higher power consumption and higher processing power capabilities.
To better support the various UE types that represent different market segments, the 6G system needs flexibility to optimally support these different UE types.
While different UE types already exist in 5G, it is essential that this requirement is included from the first release of 6G.
It is also important to highlight that while there is expected diversity in the types of UEs, each UE type should have at its core a common set of basic capabilities, to maximise the commonalities and thus reduce market fragmentation.[[SUGGESTION_START]] In that respect, 6G is expected [[SUGGESTION_END]][[SUGGESTION_START]]to provide benefits by [[SUGGESTION_END]][[SUGGESTION_START]]be[[SUGGESTION_END]][[SUGGESTION_START]]ing[[SUGGESTION_END]] [[SUGGESTION_START]]commonly [[SUGGESTION_END]][[SUGGESTION_START]]accessible by [[SUGGESTION_END]][[SUGGESTION_START]]various[[SUGGESTION_END]][[SUGGESTION_START]] type[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]] of UE.[[SUGGESTION_END]]
[[SUGGESTION_START]]Furthermore, it is expected that a single UE [[SUGGESTION_END]][[SUGGESTION_START]]may[[SUGGESTION_END]] [[SUGGESTION_START]]be[[SUGGESTION_END]] [[SUGGESTION_START]]hav[[SUGGESTION_END]][[SUGGESTION_START]]ing[[SUGGESTION_END]][[SUGGESTION_START]] different communication needs[[SUGGESTION_END]][[SUGGESTION_START]] over time[[SUGGESTION_END]][[SUGGESTION_START]], within its capabilities[[SUGGESTION_END]][[SUGGESTION_START]]; that is,[[SUGGESTION_END]] [[SUGGESTION_START]]it could temporarily[[SUGGESTION_END]] [[SUGGESTION_START]]vary[[SUGGESTION_END]][[SUGGESTION_START]] from [[SUGGESTION_END]][[SUGGESTION_START]]its usual[[SUGGESTION_END]][[SUGGESTION_START]] communication [[SUGGESTION_END]][[SUGGESTION_START]]needs[[SUGGESTION_END]][[SUGGESTION_START]]. A[[SUGGESTION_END]][[SUGGESTION_START]]s an example, a[[SUGGESTION_END]][[SUGGESTION_START]] device actively engage[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]][[SUGGESTION_START]] in high-demand communication [[SUGGESTION_END]][[SUGGESTION_START]]by default [[SUGGESTION_END]][[SUGGESTION_START]]could also [[SUGGESTION_END]][[SUGGESTION_START]]request to [[SUGGESTION_END]][[SUGGESTION_START]]transition to limited communication, e.g. to conserve energy.[[SUGGESTION_END]][[SUGGESTION_START]] Yet in another examp[[SUGGESTION_END]][[SUGGESTION_START]]le[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] a stationary device used to limited communication could [[SUGGESTION_END]][[SUGGESTION_START]]seldom [[SUGGESTION_END]][[SUGGESTION_START]]request[[SUGGESTION_END]] [[SUGGESTION_START]]high-demand [[SUGGESTION_END]][[SUGGESTION_START]]communicat[[SUGGESTION_END]][[SUGGESTION_START]]ion[[SUGGESTION_END]][[SUGGESTION_START]], e.g. based on some impeding need[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
5.10.1.2 Existing features partly or fully covering the use case functionality
TS 22.261 [14] indicates in various clauses, the intent of a 5G system to support "diverse UEs and services" in the informative text, for example:
Introduction clause:
The need to support different kinds of UEs (e.g. for the Internet of Things (IoT)), …
Clause 6.2.1:
A key feature of 5G is support for UEs with different mobility management needs. 5G will support UEs with a range of mobility management needs …
Clause 6.4.1:
5G introduces the opportunity to design a system to be optimized for supporting diverse UEs and services.
Also, a number of requirements in TS 22.261 [14] mention "UE capabilities":
The 5G system shall support a mechanism for a UE to select and access network slice(s) based on UE capability, ongoing application, radio resources assigned to the slice, and policy (e.g., application preference).
The 5G system shall allow the operator to assign a UE to a network slice, to move a UE from one network slice to another, and to remove a UE from a network slice based on subscription, UE capabilities, the access technology being used by the UE, operator's policies and services provided by the network slice.
The 5G system shall support UEs with multiple radio and single radio capabilities.
There are features already defined in other WGs in 5G that reflect the diverse device support in 5G. However, there are no requirements defined in Stage 1 in 5G. Therefore, it is proposed that a requirement to enable the network to support different device types / capabilities is defined.
5.10.1.3 Potential New Requirements
[PR 5.10.1.3-1] The 6G system shall support UEs with different characteristics [[SUGGESTION_START]]and different [[SUGGESTION_END]][[SUGGESTION_START]]service [[SUGGESTION_END]][[SUGGESTION_START]]needs [[SUGGESTION_END]]such as data rate, latency, [[SUGGESTION_START]]reliability[[SUGGESTION_END]].
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S1-254330.zip
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2026-01-06 10:28:13
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S1-254470
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SA1
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TSGS1_112_Dallas
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pCR
|
approved
|
Next meetings (calendar)
|
3GPP TSG-SA WG1 Meeting #112 S1-254470
17-21 November 2025, Dallas, USA
(revision of S1-254050r1)
(revision of S1-254050)
(revision of S1-253154)
Title: Use Case on smart manufacturing enabled by diverse autonomous robots
Agenda item: 8.1.8.2
Source: Orange
Contact: Philippe Lottin (philippe.lottin (@) orange.com)
Abstract: This contribution provides a new use case (re-submission) of smart manufacturing enabled by diverse autonomous robots.
1. Introduction
“Further Use Cases on Industry and Verticals” has been identified as one of key areas for 6G study. This contribution proposes a new use case on smart manufacturing.
2. Reason for Change
The growth in the use of diverse autonomous robots will support the future industrial scenarios such as manufacturing. There could be various types of robots including automated robots, semi-autonomous robots, fully autonomous robots. All these can be categorized as AI agents deployed in the end device. These robots can be used to complement and even replace human labour. This use case will show how 3GPP system can help in communication and cooperation between robots to enable the smart manufacturing in the future.
3. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870.
* * * First Change (New clause) * * * *
11.x Use case on smart manufacture enabled by autonomous robots
11.x.1 Description
The growth in the use of diverse robots (e.g. automated robots, semi-autonomous robots, fully autonomous robots) will support the future industrial scenarios such as manufacturing. All these robots can also be regarded as AI agents deployed in the end device. These robots can be used to complement and even replace human labour to ensure safe and efficient operation. One usage scenario, as described in HEXA-X-II Deliverable D1.2 [9.4x], is smart manufacturing where different types of autonomous robots work in unison to fulfil a complex task. There could be transportation robots, assembling robots, inspection robots, calibration robots, patrol robots, etc. It is foreseeable that many robots will need to collaborate in the future to accomplish a task via group communication for the information exchange between robots in a robot group.
Typically, a factory can have hundreds or thousands of autonomous robots from different manufactures; these robots support different robot/AI agents protocols with various capabilities (e.g., different operating system, different media processing capabilities). It is critical to enable efficient communication and collaboration among these autonomous robots, which can be very challenging especially for individual users or small-medium enterprises who need the services provided by diverse robots but cannot maintain a dedicated server for the interworking issue. The 3GPP system is expected to enable seamless/hassle-free communication between these diverse robots.
Table 11.x.1-1: Potential sustainability impacts of the use case
Sustainability Handprints
(benefits)
Sustainability Footprints
(costs)
Environmental
Resource efficiency: Functionalities may be provided by machines with less materials, energy, and waste generated. Industrial automation can offer more efficient use of resources and energy usage (exactly when and how much is necessary) by improving precision, material handling, task coordination, and flawless operations. The precise service understanding and network resource allocation will reduce resource idleness and optimize the network resource efficiency.
The manufacturing, including material extraction and industrial processes, and transportation of robots generate GHG emissions
The disposal of machines and devices results in increased electronic waste
Social
Accessibility: help people perform tasks beyond human capabilities
Safer work environment.
Increased trustworthiness of on-time delivery of the expected outcomes
Rendering roles obsolete: may eliminate job roles involving manual, linear, and repetitive tasks
Robots may require human operators to obtain new skills w.r.t. their method of use and maintenance (IT/robots literacy)
Economic
Increased productivity and enhanced competitiveness
New business opportunities may emerge from technology leadership in autonomous robotics
New business opportunities for individuals: Cf. “Accessibility” in “Social benefits”
New job opportunities
OPEX reduction through self-reflection of the network.
Extra R&D investments.
Initial investments to purchase, install, and set up autonomous machines may be a barrier for smaller businesses or those in developing countries.
Increased reliance on robots/cobots can pose a risk in case of failures or cyber-attacks
11.x.2 Pre-conditions
Each factory has its own autonomous production robots, e.g. transportation robots, assembling robots, inspection robots, calibration robots, patrol robots, etc. These autonomous robots are produced by different manufacturers and of different capabilities. There are many kinds of products being produced in these factories.
The 3GPP network can manage the registration of all autonomous robots and can also enable the communication in different robot groups.
There is network AI agent in 3GPP network that leverages LLMs and the network's capabilities (e.g., communication, sensing, AI, computing, and others) to provide service for all autonomous robots.
The network AI agent in 3GPP network can monitor the robot’s network status (latency, jitter, disturbances, errors, faults, etc.) and make sure the service requirement (e.g., QoS) requested by the robots can be satisfied.
11.x.3 Service Flows
1. The owner of factory #1 activate all the robots and make them initially register to the network. During the registration, the 3GPP network will allocate each robot an identity and credential if applicable. The network will be based on these identities and credentials to route the communications between robots and also authorize the service requests. The same procedures also apply to factory #2.
2. The 3GPP network will also create a communication group #1 for these robots based on their request to ensure the security and efficiency of common information exchanges between these robots. Afterwards, they will perform auto-discovery procedure including the discovery of identities, services and capabilities. The same procedures also apply to factory #2.
3. Based on the product/task type, different robots in factory #1 will be further divided into logical communication domains so that they are not disturbed by product/task-irrelevant robots in other domains and the data flows are not flooded across domains if not needed. This can facilitate the efficient and secure collaboration between the robots. For example, transportation robot #1, assembling robot#1, inspection robot#1, calibration robot#1 are responsible for production of product #1 while transportation robot #1, assembling robot#2, inspection robot#2, calibration robot#2 are responsible for production of product #2. All the patrol robots are responsible for safeguard. It should be noted that these logical communication domains will be created and released frequently with the lifecycle of the product/task.
4. After long-term monitoring, the 3GPP network stores the robot's activity patterns which can be used to provide proactive service to these robots. For example, reserve computing resources along the route of the transportation robot to offload computing task to ensure that the robot has sufficient power outside. In case the transportation robot encounters a traffic jam, the robot or the network AI agent finds out that the network is congested. After receiving feedback, the network AI agent adjusts the policy to generate a dedicated network or a higher QoS policy for the robots, thereby providing a better and more stable service for the robots. At the same time, the network AI agent can also perform self-reflection based on the feedback (e.g., memory updating, fine-tuning the model, etc.).
5. There may also be collaborations between robots from different factories. For example, when the transportation robot #1 is overloaded, the factory #1 can lend transportation robot#2 from factory#2 for a while. When transportation robot#2 arrives at the factory #1, it will establish a communication group with the assembling robots in factory#1 for collaboration.
11.x.4 Post-conditions
The 3GPP system enables efficient communication and collaboration among these autonomous robots with enhanced security and controllability to provide efficient manufacturing.
11.x.5 Existing features partly or fully covering the use case functionality
3GPP TR 22.916 [136] captures the outcome of Study on Network of Service Robots with Ambient Intelligence, which includes various use cases with challenges and potential gaps and other consideration points related to efficient communications service and cooperative operation for a group of service robots. It provides a good basis for further enhancements of communications for the network of service robots. The aspects of highly intelligent robots (e.g., semi-autonomous robots, fully autonomous robots) are yet to be addressed, especially regarding the efficient communication and collaboration among such robots in a robot group.
To enable 3GPP system to support the efficient communication and collaboration among highly intelligent robots, firstly it is crucial for the 3GPP system to identify such robots. The relevant service requirements can be found in 3GPP TS 22.101 [58] clause 26a:
The 3GPP system shall support secure provisioning of credentials to a non-3GPP device connected via a gateway UE, whose User Identifier has been linked with the 3GPP subscription of the gateway UE, to enable the non-3GPP device to access the network and its services according to the linked 3GPP subscription when connected via non-3GPP access.
As to the efficient communication and collaboration in a robot group, it can be partially covered by 5G LAN-type service, as defined in 3GPP TS 22.261 [7.6x]. 5G system offers private communication using IP and/or non-IP between two UEs or a group of UE; the group is generally defined by subscription and managed by operators or 3rd authorized party. However, the communication among highly intelligent robots (e.g., semi-autonomous robots, fully autonomous robots) in a robot group is more dynamic and requires certain level of flexibility and autonomy. For example, all the autonomous robots in a factory may form a 5G LAN VN but some autonomous robots may need to form sub-groups to exchange specific types of data (e.g., safety guard data) without the involvement of all the robots in the LAN VN. In addition, the 3GPP system is expected to enable seamless communication between these diverse autonomous robots supporting different protocols with various capabilities.
11.x.6 Potential New Requirements needed to support the use case
[PR 11.x.6-1] The 6G system shall provide means to support efficient and secure communication between UEs (e.g. autonomous robots collaborating in a robot group) considering the diversity of protocols and capabilities supported by them.
* * * End of Change * * * *
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S1-254470.zip
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2026-01-06 10:28:27
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S1-254346
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SA1
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TSGS1_112_Dallas
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pCR
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noted
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Work Item/Study Item status update
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3GPP TSG-SA WG1 Meeting #112 S1-254346
17-21 November 2025, Dallas, Texas, USA (revision of S1-254223r2)
Source: Huawei, HiSilicon[[SUGGESTION_START]], China Mobile[[SUGGESTION_END]]
New use case title: New use case on 6G system assisted physical AI training data generation
Draft TS/TR: 3GPP TR 22.870 v0.4.1
Agenda item: 8.1.3
Document for: Approval
Contact: Shuang ZHANG, zhang.shuang2 AT Huawei DOT com
Abstract: This use case proposes a novel 6G service highly desirable for physical AI-embodied device/AI robot companies for enabling their products’ market adoption. To tackle the training data shortage problem in developing/deploying physical AI devices/robots targeted at various fields/sectors (e.g. agricultural robots, mining/construction robots, autonomous vehicles) by resorting to 3GPP services (e.g. 3GPP sensing, 6G AI Service, communication service) presents a unique opportunity for 3GPP operators to enter the realm of an exciting domain of physical AI-embodied device/robot.
Using 3GPP sensing data as direct 3D spatial-temporal data (no need to convert from 2D visual data), complemented with positioning and/or additional 6G/3GPP network/system data, and any external data (e.g. image, video) wherever available, the 6G system is expected to have the advantage of generating high-quality 3D spatial-temporal data applicable for areas where physical AI robots are expected to operate. Combined with 6G AI Service or 6G Computing Service (depending on the business models), the corresponding AI models can be trained and deployed in the 6G network (e.g. SHE).
[[SUGGESTION_START]]r[[SUGGESTION_END]][[SUGGESTION_START]]1 addresses:[[SUGGESTION_END]]
[[SUGGESTION_START]]1.(Nokia): [[SUGGESTION_END]][[SUGGESTION_START]]clarified [[SUGGESTION_END]][[SUGGESTION_START]]physical AI devices/robots are UEs;[[SUGGESTION_END]]
[[SUGGESTION_START]]2.[[SUGGESTION_END]][[SUGGESTION_START]](ZTE): PR2 changes [[SUGGESTION_END]][[SUGGESTION_START]]“[[SUGGESTION_END]][[SUGGESTION_START]]expose[[SUGGESTION_END]][[SUGGESTION_START]]” to “[[SUGGESTION_END]][[SUGGESTION_START]]provide[[SUGGESTION_END]][[SUGGESTION_START]]”;[[SUGGESTION_END]]
[[SUGGESTION_START]]3.(QC): [[SUGGESTION_END]][[SUGGESTION_START]]in PR1, [[SUGGESTION_END]][[SUGGESTION_START]]clarified [[SUGGESTION_END]][[SUGGESTION_START]]by ad[[SUGGESTION_END]][[SUGGESTION_START]]ding note2 to clarify it is the 3rd party (consumer of the 6G Physical AI Training Data Service) that trains (e.g. how to train is irrelevant here) the AI models for the physical AI devices’/robots’ AI models, and this can be in [[SUGGESTION_END]][[SUGGESTION_START]]3[[SUGGESTION_END]][[SUGGESTION_START]]rd[[SUGGESTION_END]][[SUGGESTION_START]] party cloud, etc.[[SUGGESTION_END]]
[[SUGGESTION_START]]r2 addresses:[[SUGGESTION_END]]
[[SUGGESTION_START]]1.(QC): [[SUGGESTION_END]][[SUGGESTION_START]]generalize PRs by not limiting to physical AI[[SUGGESTION_END]][[SUGGESTION_START]] devices/robots[[SUGGESTION_END]][[SUGGESTION_START]], so now the “6G Physical AI Training Data Service” is an example[[SUGGESTION_END]][[SUGGESTION_START]];[[SUGGESTION_END]][[SUGGESTION_START]] use the term “[[SUGGESTION_END]][[SUGGESTION_START]]3rd party AI application (e.g. AI agent application)[[SUGGESTION_END]][[SUGGESTION_START]]”[[SUGGESTION_END]][[SUGGESTION_START]] -> changes in PR1[[SUGGESTION_END]]
[[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]].(Nokia) [[SUGGESTION_END]][[SUGGESTION_START]]expand to incl. data collected from UE[[SUGGESTION_END]][[SUGGESTION_START]] for [[SUGGESTION_END]][[SUGGESTION_START]]such training[[SUGGESTION_END]][[SUGGESTION_START]] -> changes in PR1[[SUGGESTION_END]]
* * * First Change * * * *
2 References
The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or nonspecific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
[1] 3GPP TR 21.905: "Vocabulary for 3GPP Specifications".
<Skip>
[257] 3GPP TS 28.538: “Management and orchestration; Edge Computing Management”.
[[SUGGESTION_START]][1x] Occworld: Learning a 3d occupancy world model for autonomous driving, T[[SUGGESTION_END]][[SUGGESTION_START]]singhua[[SUGGESTION_END]][[SUGGESTION_START]] U[[SUGGESTION_END]][[SUGGESTION_START]]niversity[[SUGGESTION_END]][[SUGGESTION_START]], ECCV 2024.[[SUGGESTION_END]]
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]2x] CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer, T[[SUGGESTION_END]][[SUGGESTION_START]]singhua[[SUGGESTION_END]][[SUGGESTION_START]] U[[SUGGESTION_END]][[SUGGESTION_START]]niversity[[SUGGESTION_END]][[SUGGESTION_START]], Zhipu AI, ICLR 2025.[[SUGGESTION_END]]
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]3x] Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control, https://arxiv.org/html/2503.14492v1, 2025.[[SUGGESTION_END]]
[[SUGGESTION_START]][4x] Beamforming Design for RIS-aided ISAC: Maximizing Weighted Sum of SCNR and SINR, Southeast University, GLOBECOM 2024.[[SUGGESTION_END]]
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]5x] Cosmos World Foundation Model Platform for Physical AI[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] https://arxiv.org/pdf/2501.03575, 2025.[[SUGGESTION_END]]
[[SUGGESTION_START]][6x] Do generative video models understand physical principles?[[SUGGESTION_END]][[SUGGESTION_START]], [[SUGGESTION_END]][[SUGGESTION_START]]https://arxiv.org/pdf/2501.09038, 2025.[[SUGGESTION_END]]
[[SUGGESTION_START]][7x] VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness, https://arxiv.org/pdf/2503.21755, 2025.[[SUGGESTION_END]]
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]8x] OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] CVPR 2023.[[SUGGESTION_END]]
[[SUGGESTION_START]][9x][[SUGGESTION_END]] [[SUGGESTION_START]]Renny Sari Dewi, et al.[[SUGGESTION_END]][[SUGGESTION_START]]A Systematic Review of Physical Artificial Intelligence, Journal of Artificial Intelligence and Engineering Applications in 2025.[[SUGGESTION_END]]
[[SUGGESTION_START]][10x] [[SUGGESTION_END]][[SUGGESTION_START]]John Smith et al, Strategies for overcoming data scarcity, imbalance, and feature selection: an ML-based approach, Scientific Reports 2024[[SUGGESTION_END]]
[[SUGGESTION_START]][11x] Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots , [[SUGGESTION_END]][[SUGGESTION_START]]https://arxiv.org/abs/2310.13724[[SUGGESTION_END]][[SUGGESTION_START]]. 2023.[[SUGGESTION_END]]
* * * Second Change * * * *
3 Definitions, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the terms and definitions given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
[[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]hysical AI[[SUGGESTION_END]][[SUGGESTION_START]]: artificial intelligence systems that are embodied in robots, self-driving cars, and other autonomous machines that perceive, understand, and interact with the physical world. Unlike purely digital AI, physical AI systems use sensors like cameras, radar, and lidar to gather data from their environment and act upon it in real time. Examples include robots that can perform complex tasks, like navigating a factory floor, a humanoid robot that can walk and balance, or a self-driving car.[[SUGGESTION_END]]
[[SUGGESTION_START]]6G Physical AI [[SUGGESTION_END]][[SUGGESTION_START]]Training [[SUGGESTION_END]][[SUGGESTION_START]]Data Service[[SUGGESTION_END]][[SUGGESTION_START]]:[[SUGGESTION_END]][[SUGGESTION_START]] a service providing physical AI [[SUGGESTION_END]][[SUGGESTION_START]](i.e. [[SUGGESTION_END]][[SUGGESTION_START]]artificial intelligence systems that are embodied in robots, self-driving cars, and other [[SUGGESTION_END]][[SUGGESTION_START]]autonomous machines that perceive, understand, and interact with the physical world[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]][[SUGGESTION_START]] , via the 6G network (in conjunction with SHE), [[SUGGESTION_END]][[SUGGESTION_START]] with data for training the underpinning AI models, which utilizes a combination of 3GPP services and capabilities (e.g. sensing service, 6G AI Service, positioning data, and any other 6G System Data). [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE: To be applicable for [[SUGGESTION_END]][[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]hysical AI[[SUGGESTION_END]] [[SUGGESTION_START]]systems [[SUGGESTION_END]][[SUGGESTION_START]](e.g. [[SUGGESTION_END]][[SUGGESTION_START]]humanoid [[SUGGESTION_END]][[SUGGESTION_START]]robots, autonomous vehicles), the [[SUGGESTION_END]][[SUGGESTION_START]]training[[SUGGESTION_END]][[SUGGESTION_START]] data[[SUGGESTION_END]][[SUGGESTION_START]] (format)[[SUGGESTION_END]][[SUGGESTION_START]] is expected to [[SUGGESTION_END]][[SUGGESTION_START]]be[[SUGGESTION_END]] [[SUGGESTION_START]]suitable for presenting the physical world laws and motions, e.g. videos, point clouds[[SUGGESTION_END]] [[SUGGESTION_START]]if [[SUGGESTION_END]][[SUGGESTION_START]]with necessary specialized processing[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]3[[SUGGESTION_END]][[SUGGESTION_START]]D Consistency[[SUGGESTION_END]][[SUGGESTION_START]]:[[SUGGESTION_END]] [[SUGGESTION_START]]refers to [[SUGGESTION_END]][[SUGGESTION_START]]the degree to which different videos of the same object or scene align and match each other in terms of their geometric, spatial and visual properties when viewed from various perspectives. It can be measured by the success rate of performing 3D reconstruction (i.e. point cloud generation) using the video [5[[SUGGESTION_END]][[SUGGESTION_START]]x[[SUGGESTION_END]][[SUGGESTION_START]]].[[SUGGESTION_END]]
[[SUGGESTION_START]]P[[SUGGESTION_END]][[SUGGESTION_START]]hysical Compliance[[SUGGESTION_END]][[SUGGESTION_START]]:[[SUGGESTION_END]] [[SUGGESTION_START]]refers to the adherence to physical principles and constraints, ensures that the behaviour and interactions of objects or elements within a scene are consistent with the laws of physics, it can be measured by the similarity of motion changes between the generated video and a given ground truth video which conforms to the physics laws [6][[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]Fréchet Inception Distance[[SUGGESTION_END]][[SUGGESTION_START]]: [[SUGGESTION_END]][[SUGGESTION_START]]Fréchet Inception Distance[[SUGGESTION_END]][[SUGGESTION_START]] indicators[[SUGGESTION_END]][[SUGGESTION_START]] [7[[SUGGESTION_END]][[SUGGESTION_START]]x[[SUGGESTION_END]][[SUGGESTION_START]]] [[SUGGESTION_END]][[SUGGESTION_START]]c[[SUGGESTION_END]][[SUGGESTION_START]]an be measure by the similarity between a set of generated videos and a set of benchmark videos which indicate potential application performance requirements. [[SUGGESTION_END]]
* * * Third Change (new text) * * * *
6.x Use case on System assisted physical AI training data generation
6.x.1 Description
Physical AI refers to artificial intelligence systems that are embodied in robots, self-driving cars, and other autonomous machines that perceive, understand, and interact with the physical world. Unlike purely digital AI, physical AI systems use sensors like cameras, radar, and lidar to gather data from their environment and act upon it in real time. Examples include robots that can perform complex tasks, like agricultural robots, mining/construction robots, autonomous vehicles, humanoid service robots for cleaning, navigating a factory floor.
The learning process of physical AI usually needs to combine detailed physics-based simulations (e.g. to model real-world laws of physics and constraints) with real-world adaptations to bridge the gap between virtual training environments and physical reality [9x]. One of the obstacles to the large-scale development and adoption of physical AI is the lack of high-quality training data for different deployment needs, as well as the costly and time-consuming process of real-world data collection. For the latter, collecting physical world data in certain situations can even be impractical due to logistic challenges [10x]. To tackle this problem, generative AI-based data generation methods have been proposed and widely adopted, i.e. data generation by AI models. It produces data (3D spatial-temporal data or visual data) aligned with real-world laws for training physical AI. The training data generation models use massive, wide-area 3D spatial-temporal data collected from the real world. The 3D spatial-temporal data is often represented as temporal 3D occupancy (Occ, is a series of continuous and uniform cubic units of the 3D space. Each unit can indicate whether its location is occupied by points of a point cloud [8x]), which can always be obtained from the point clouds that can be directly collected from radar and 3GPP base stations (The 6G network has similar sensing capabilities to the radar. Therefore, 6G network can perform this data collection.), or indirectly converted through 3D reconstruction from videos captured by cameras [1x].
NOTE: Occ in this context stands for semantic occupancy perception (shorthand for occupancy perception), which models not just where objects are, but also their semantic identity in 3D space, facilitating better scene understanding for applications, e.g. agricultural robots, service robots for cleaning, mining robots, autonomous vehicles.
Figure 6.x.1-1: Illustration of physical AI training data generation model principle, drawn based on information in [1x], [2x], and [3x]
For such kind of physical AI training data generation models, apart from 3GPP sensing data, additionally using available real-world prior information (e.g. positioning information, terrain, etc.) will make higher the quality of the generated training data [2x]. In addition, incorporating visual priors as optional information in some approaches [3x] can also enhance the quality of the generated data.
With the ubiquitous connectivity and wide-area deployment of 6G network, MNOs have advantages in providing such kind of physical AI training data-generation capability thanks to 3GPP sensing, which provides the capabilities to generate 3D Occ from the sensing data (e.g. point clouds) for training physical AI data generation models. Depending on the business models, the physical AI training data generation model can be fully managed by the operators, e.g. to be trained as well as deployed in SHE. After deployment, such models are then used for generating training data needed by third-parties to further train physical AI models. Obtaining 3D spatial data from 6G network-based sensing is efficient and effective compared with the following non-3GPP approaches:
3D spatial data via visual sensors like camera: the conversion of video to 3D spatial data requires 3D reconstruction algorithms, which may introduce errors and redundant operations, the option based on direct collection of sensors (i.e. point cloud sensing data within 3GPP network) offers better reliability and efficiency.
3D spatial data via sensors like radar by 3rd party: usually challenging logistics and manual collection of training data (for physical AI robot), including vehicle-roll for collecting autonomous vehicle training data. In contrast, the widely deployed 6G network infrastructure offers a more cost-effective method in terms of collecting the sensing data.
Priori conditional information: 6G system has the potential to provide more abundant information (e.g. positioning of objects inside 3GPP coverage, or other data inside 6G system such as 6G System Data) of or reflecting the real world, serving as an effective prior for the data generation model construction. Such data largely complement 3D spatial information from 3GPP sensing (point cloud data) [4x]).
Non-3GPP data (e.g. visual) collection: compared with any 3rd party who relies on communication service provided by others for transferring collected data, 6G system has the innate advantage to provide highly efficient transfer for e.g. high throughput visual data, regardless of where the data is collected as it benefits from deployed wide-area coverage/connectivity of the 6G network
More high-resolution 3D spatial data collected for training the data generation model will improve the model’s capability to generate high-quality training data for the physical AI devices. However, it often demands higher-level of privacy protection, particularly the data collected from real-world for training the data generation model. 6G network as a secure system operated by operators (who comply with various regulatory frameworks and exercise their legal obligations to employ data privacy and protection measures) is more suitable for providing such data while complying with data privacy protection requirements (note that the generated data can be non-real, i.e. synthetic data).
Therefore, the use of 6G system will be advantageous for providing such physical AI training data generation service (e.g. a 3rd party service provider in collaboration with an operator), an illustration is shown in the Figure 6.x.1-2. First, an operator or a 3rd party company interested in providing the physical AI training data generation service can train and deploy the data generation model in 6G network (e.g. SHE). Training the data generation model using the 3GPP sensing and various data can be supported by 6G Computing Service. After training, the model can be deployed in 6G network (e.g. using 6G AI Service in SHE) to generate physical AI training data for other 3rd parties such as physical AI robot companies. As the consumer of physical AI training data generation service, they can request via the 6G network (e.g. AI training data generation service deployed in SHE) for physical AI-embodied robot training data suitable for the respective usage scenarios (i.e. various target physical environments robots are to operate in). Based on the request, the 6G system will collect sensing results/data, positioning information (e.g. UE’s altitude, speed, direction, horizontal coordinates), network data (e.g. 6G system data, and any meta-data in the 3GPP network, including the configuration data of the deployed 3GPP services and 3GPP network, subscriber data, 3GPP network analytics, network management related data) and optionally, gather 2D visual data from non-3GPP data sources. Then, the data generation model in 6G network (e.g. hosted in SHE) performs inference to produce physical AI training data, which is finally exposed via 6G network to the 3rd parties. When needed, the 6G system may leverage among others the 3GPP sensing, network data to re-train the data generation model (e.g. using 6G Computing Service in SHE), before performing AI inference with it to produce the requested physical AI training data.
Figure 6.x.1-2: Illustration of service flow on 6G system assisted physical AI training data generation
6.x.2 Pre-conditions
6G system can collect sensing results, positioning data, and depending on the situation obtaining 2D visual data e.g. videos, from the Internet.
6G network can be used to train (e.g. using 6G Computing Service) the physical AI training data generation model using 3GPP network-based sensing capability, positioning, and additional visual data such as videos. For example, the 3D spatial data used for training the physical AI training data generation models can be obtained through the sensing capabilities of 6G systems.
6G network can be used to perform AI inference by deploying the physical AI training data generation model using 6G AI Service. The inference input can include sensing results/data, positioning data and optionally available 2D visual data such as videos.
6G network can be used to expose the physical AI training data to the authorized 3rd parties (e.g. application server for training physical AI models).
Depending on the business model, the physical AI training data generation service can be provided directly by a 6G MNO, or by a 3rd party service provider who has proper business agreement with a 6G MNO. The difference could be who would have the ownership of the physical AI training data generation model, the control over its use, operation, and fulfilment of commercial obligations of the service.
A 3rd party physical AI-embodied robot company has service agreement with the AI training data generation service provider. The robot company’s physical AI embodied robots are expected to operate outdoors within the network coverage where sensing is available from the operator.
The operator’s data generation model has been pre-trained to provide the service in the target area.
6.x.3 Service Flows
The customer, a 3rd party physical AI embodied robot company requests the service provider of physical AI training data generation service (e.g. provided directly by a 6G MNO, or by a 3rd party who collaborates with a 6G MNO) for the physical AI training data of a visual physical AI embodied robot for express delivery on/around street A. To ensure the training quality of the physical AI embodied robot, the company may request the service provider for training data for navigation module and grab module, respectively. The process of determining the data quality requirements of the two requests are as follows:
Training data request for the navigation task: as the company wants to train visual physical AI robots, the data type should be video. According to the robot’s working location, the site environing street A is identified. Since the navigation task is sensitive to stationary objects and (architectural) structures in real world environments, the 3D consistency indicator is essential, e.g. its value being more than 90% is determined. Considering there is no special requirement for physical compliance [11x], the common value (current State-of-the-Art Physics-IQ evaluation results as baseline) greater than 30% can be determined e.g. page 5 upper-left table of [6x].
Indicator
Value
Data type
Video
Location
Street A
3D Consistency
>90%
Physical Compliance
>30%
…
Training data request for the grab task: to train visual physical AI robots, the data type should be video. According to the robot’s working location, the site environing street A is identified. Since the grab task (to grab/grasp/seize a moving target) requires utilizing the motion trajectory of the target, it’s critical for the task to track the moving target’s movement pattern/trajectory [6x]. Therefore, the requirement for physical compliance indicator is crucial (which is always used to measure the alignment of moving object trajectories in generated videos with real-world patterns) - a strict threshold more than 90% is determined. Considering the grab task has no special requirement for 3D consistency, a common value (e.g. COSMOS as baseline) being greater than 60% can be determined e.g. page 37 table in [5x].
Indicator
Value
Data type
Video
Location
Street A
3D Consistency
>60%
Physical Compliance
>90%
…
NOTE 1: 3D Consistency [5x] can be measured by the success rate of performing a 3D reconstruction task (i.e. point cloud generation) using the video.
NOTE 2: Physical Compliance [6x] can be measured by the similarity of motion changes between the generated video and a given ground truth video (real-world video footages) which conforms to the physics laws, e.g. Vbench in [7x]. Because the content described in these benchmark videos encompasses as many real-world scenes and physical laws as possible, comparisons can effectively reflect physical compliance.
The 6G system determines whether there is a physical AI training data generation model related to the target area of street A, and whether the model is expected to generate data whose quality meets the customer request.
If not, the 6G system can be additionally used to retrain the data generation model (6G system collecting/utilizing necessary information/data) that will be used later for physical AI training data generation.
The 6G system collects the data (e.g. sensing results, related positioning data related to environment objects on/around street A), and collects the videos related with the street A from accessible data sources.
6G system utilizes the collected data e.g. same as the data types used for data generation model training) to perform inference with the data generation model to generate physical AI training data.
6G system evaluates/determines whether the 3D consistency indicator of generated physical AI training data is greater than 60% and whether the physical compliance indicator is greater than 30%.
If these requirements are not met, 6G system optimizes the generated physical AI training data, making it meet the requirements as much as possible and test the 3D consistency and physical compliance indicators of the resulting physical AI training data.
6G system exposes to the service consumer (physical AI-embodied robot company) the final resulting physical AI training data with required quality (e.g. the 3D consistency and physical compliance indicator values) to the 3rd party physical AI embodied robot company.
The physical AI embodied robot company trains their physical AI models using the training data. When the training is complete, the trained model will be deployed to the physical AI robots (or their “brain” in the operator’s SHE or cloud).
6.x.4 Post-conditions
The 6G system continuously delivers data to the user/subscriber (e.g. the 3rd party physical AI embodied robot company, who trains the physical AI models) until the training is completed.
6.x.5 Existing features partly or fully covering the use case functionality
The following includes some examples:
The 5G system supports the network exposure to an authorized third party (e.g., TS 23.501 [140], TS 23.502 [30], and TS 23.503 [141]).
The 5G system supports the assistance to AI/ML Operations in the Application Layer (e.g., TS 23.501 [140], TS 23.502 [30], and TS 23.503 [141]).
There are sensing related service requirements for 5G system specified in TS 22.137 [6].
6.x.6 Potential New Requirements needed to support the use case
[PR 6.x.6-1] Based on operator policy, application needs and regulatory requirements, the 6G network shall support to provide [[SUGGESTION_START]]training data[[SUGGESTION_END]][[SUGGESTION_START]] retaining certain physical environment characteristics[[SUGGESTION_END]][[SUGGESTION_START]] (e.g. when needed via[[SUGGESTION_END]] 6G Physical AI Training Data Service[[SUGGESTION_START]])[[SUGGESTION_END]] to service consumers for training [[SUGGESTION_START]]t[[SUGGESTION_END]][[SUGGESTION_START]]heir[[SUGGESTION_END]] AI models[[SUGGESTION_START]] ([[SUGGESTION_END]][[SUGGESTION_START]]e.g. [[SUGGESTION_END]][[SUGGESTION_START]]used by[[SUGGESTION_END]] [[SUGGESTION_START]]3rd party AI application (e.g. AI agent application) [[SUGGESTION_END]][[SUGGESTION_START]]running on[[SUGGESTION_END]] [[SUGGESTION_START]]physical AI devices/robots)[[SUGGESTION_END]], using multiple 3GPP services and capabilities (e.g. sensing service/data, 6G AI Service, positioning data, 6G System Data [[SUGGESTION_START]]incl. data collected from UE[[SUGGESTION_END]]).
NOTE 1: The physical AI training data can be the temporal 3D Occ, videos, etc. It is good to know 3D Occ ("3D Occupancy) refers to spatial data comprising three-dimensional longitude, latitude, and altitude coordinates [1x], [8x].
[[SUGGESTION_START]]NOTE 2: it is the 3[[SUGGESTION_END]][[SUGGESTION_START]]rd[[SUGGESTION_END]][[SUGGESTION_START]] party ([[SUGGESTION_END]][[SUGGESTION_START]]e.g.[[SUGGESTION_END]] [[SUGGESTION_START]]con[[SUGGESTION_END]][[SUGGESTION_START]]sumer of the [[SUGGESTION_END]][[SUGGESTION_START]]6G Physical AI Training Data Service[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]][[SUGGESTION_START]] that trains (e.g. how[[SUGGESTION_END]][[SUGGESTION_START]] to train is irrelevant here[[SUGGESTION_END]][[SUGGESTION_START]]) the AI models[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[PR 6.x.6-2] Based on operator policy, application needs and regulatory requirements, the 6G network shall provide a mechanism to [[SUGGESTION_START]]provide [[SUGGESTION_END]]the generated physical AI training data to an authorized third-party.
[PR 6.x.6-3] Based on operator policy, application needs and regulatory requirements, the 6G network shall support 6G Physical AI Training Data Service provider (e.g. MNOs, or a 3rd party) to ensure the generated physical AI training data meet the data quality requirements (e.g. regarding 3D Consistency, Physical Compliance, Fréchet Inception Distance) of the 3rd party, if applicable.
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S1-254346.zip
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2026-01-06 10:53:19
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S1-254415
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SA1
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TSGS1_112_Dallas
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pCR
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noted
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Work Item/Study Item status update
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3GPP TSG SA WG 1 Meeting #112 S1-254415
17-21 November 2025, Dallas, Texas, USA (revision of S1-254303)
Source: Huawei, HiSilicon[[SUGGESTION_START]], Rakuten, OPPO[[SUGGESTION_END]][[SUGGESTION_START]], NEC[[SUGGESTION_END]]
pCR Title: Pseudo-CR on updating definition of SHE (Service Hosting Environment)
Draft Spec: 3GPP TR 22.870 v0.4.1
Agenda item: 8.1.3.2
Document for: Approval
Contact: Shuang ZHANG, zhang.shuang2 AT Huawei DOT com
Abstract: SHE definition update.
1. Introduction
The current definitionn of Service Hosting Environment (a.k.a. “SHE”) was agreed quite a few meetings ago and adequately captures the essence for the related the 6G services (example below), because SHE is where services are hosted, including operator-provided services as well as 3rd party’s services.
//////////////////////////////////////// exerpts from TR 22.870 v.0.4.1 ////////////////////////////////////////
6G Computing Service: a service provided by 6G network utilizing computing resources in Service Hosting Environment, which can be used by a subscriber (via UE)/3rd party.
NOTE 1: The computing resources can refer to hardware and/or software that provides the required processing, storage capability etc. to perform computational tasks (e.g. XR rendering).
Service Hosting Environment: the environment, located inside of 6G network and fully controlled by the operator, where Hosted Services are offered from.
//////////////////////////////////////// end of exerpts ////////////////////////////////////////
For 6G services and requirements already captured in the TR 22.870 v.0.4.1 so far, there are performance requirements on the 6G network to support e.g. XR rendering for immersive media content production with KPIs on Max allowed end-to-end latency being around 100ms, and user-experienced data rates.
2. Reason for Change
For 5G, some stage2 work (e.g. EC work in SA2, SA6) was done previously. However, the services and related control functions (e.g. EdgeApps) are generally not (fully) controlled by operators. This is mainly due to the management and deployment responsibilities are not fully within the remit of an operator, in fact they are divided among multiple stakeholders (e.g. PLMN operator, Edge Computing Service Providers/ECSPs, ASPs).
For the 6G use cases, it’s imporant to recognize the necessity for operator to be able to fully control and guarantee the performance-sensitive services (e.g. 6G Compute Service), therefore the 6G CN that continues to route user traffic and perform corresponding policy control is requred to also have means to guarantee the performance of identified beyond-connectivity services, particularly the processing latency in SHE required by the service (e.g. 6G Compute Service, 6G immersive media content production that replies on SHE’s compute resource for processing).
For this, it’s within SA1’s responsibility to provide clarity on those aspects.
3. Conclusions
Propose to update the deinition of Service Hosting Environment.
4. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870 v041.
* * * First Change * * * *
3 Definitions of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the terms given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
//// skip ///
Service Hosting Environment: the environment, located inside of 6G network[[SUGGESTION_START]] (excluding RAN)[[SUGGESTION_END]] and fully controlled by the operator, where Hosted Services are offered from.
//// skip ///
* * * End of Change * * * *
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S1-254415.zip
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2026-01-06 10:53:33
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S1-254506
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SA1
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TSGS1_112_Dallas
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pCR
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revised
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Quality improvement contributions
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3GPP TSG SA WG 1 Meeting #112 S1-254506
17-21 November 2025, Dallas, Texas, USA (revision of S1-254492)
Source: Huawei, HiSilicon
pCR Title: Pseudo-CR on updating clause 6.44 on customized service provisioning based on AI Agents with results sharing
Draft Spec: 3GPP TR 22.870 v0.4.1
Agenda item: 8.1.3.2
Document for: Approval
Contact: Shuang ZHANG, zhang.shuang2 AT Huawei DOT com
Abstract: This pCR proposes update for clause 6.44.
1. Introduction
This pCR proposes update based on the existing content in clause 6.44.
2. Reason for Change
1) After providing services to user, it will be good to have the possibility to interact with the user regarding the user-experienced service quality. This will enable the operator to ask for user feedback to enhance its future service.
2) Intent will be translated into different 3GPP services’ requirements. It’s necessary for the operator to double check whether the translated services are the ones requested by user. Therefore, it’s important for the network to be able to double check with the exact intention of intended services with users.
3. Conclusions
It’s concluded to propose new requirements on:
network support a mechanism to interact with the user about the user’s experience of the provided service requested by/based on received user intent.
network support a mechanism to interact/doublecheck with the user about the exact intention/objectives of the received Intent.
4. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870 v0.4.1.
[[SUGGESTION_START]]changes in r1:[[SUGGESTION_END]]
[[SUGGESTION_START]]1. [[SUGGESTION_END]][[SUGGESTION_START]](Apple) [[SUGGESTION_END]][[SUGGESTION_START]]new PR1:[[SUGGESTION_END]][[SUGGESTION_START]] 6G network changed to 6G system[[SUGGESTION_END]][[SUGGESTION_START]], as such interaction would involve the 6G system.[[SUGGESTION_END]]
[[SUGGESTION_START]]2. (Ericsson) [[SUGGESTION_END]][[SUGGESTION_START]]improved new PR2, and [[SUGGESTION_END]][[SUGGESTION_START]]added NOTE1 to[[SUGGESTION_END]] [[SUGGESTION_START]]c[[SUGGESTION_END]][[SUGGESTION_START]]larif[[SUGGESTION_END]][[SUGGESTION_START]]ied[[SUGGESTION_END]] [[SUGGESTION_START]]what [[SUGGESTION_END]][[SUGGESTION_START]]the [[SUGGESTION_END]][[SUGGESTION_START]]user [[SUGGESTION_END]][[SUGGESTION_START]]feedback[[SUGGESTION_END]][[SUGGESTION_START]] would be[[SUGGESTION_END]][[SUGGESTION_START]], e.g. the user-experienced service quality, which will enable the operator to enhance its future service[[SUGGESTION_END]][[SUGGESTION_START]]s.[[SUGGESTION_END]]
[[SUGGESTION_START]]3. (Xiaomi) helpful suggestion, improved wording of PR5.[[SUGGESTION_END]]
Changes in 4506:
1.(Sabine) PR5, add “subject to operator policy”;[[SUGGESTION_START]] -> but then PR5 deleted (suggested by QC)[[SUGGESTION_END]]
2. (QC) PR2, after “interaction with user”, add “e.g.”
Clarified why network need to tell user their experience.
3.(Vivo): update “user intent” -> “received intent”. PR2: use “based on intent” to avoid “user intent”
* * * First Change * * * *
6.44.3 Service Flows
1.Bob plans to depart Beijing at approximately 9 am tomorrow for a business trip to Chengdu. During his train journey, he will need to attend an online meeting to share work updates at around 2 pm.
2.Bob hopes that the 6G network can ensure the network quality during his meeting on the train. Therefore, he informs the network that he will attend an online meeting on the Beijing-Chengdu train at approximately 2 pm tomorrow and needs high-quality network support during the meeting.
3.Bob’s intent is transferred to the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent. Based on his intent and the network-internal information, the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent could evaluate the received intent is clear enough to give the service recommendation.
4. If not, the network [[SUGGESTION_START]]AI [[SUGGESTION_END]]Agent could collect additional necessary information (such as detailed train schedules information) from the trusted third-party data sources .
5.The AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent first identifies multiple available train routes within the specified time and travel range, and then analyzes which routes Bob will possibly take and how long the meeting would last. Subsequently, it identifies the wireless base stations that will cover these possible train routes within the estimated time range, and predicts the Quality of Experience (QoE) for Bob’s online meeting scheduled for tomorrow.
6.Finally, the [[SUGGESTION_START]]AI A[[SUGGESTION_END]]gent generates several assurance policies along with their corresponding fees and pushes them back to Bob as recommended assurance packages including different train routes and different meeting durations.
7.Bob selects one from the recommended assurance packages as feedback to the network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent.
8.The network AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent pre-configures the network and will execute the corresponding assurance policies during Bob’s trip.
[[SUGGESTION_START]]9. After the trip, the network AI [[SUGGESTION_END]][[SUGGESTION_START]]A[[SUGGESTION_END]][[SUGGESTION_START]]gent shares the user experience information of the meeting to Bob. Bob is happy with it and decide to use this service again next time.[[SUGGESTION_END]]
* * * Next Change * * * *
6.44.6 Potential New Requirements needed to support the use case
NOTE: The mention of AI capabilities such as AI [[SUGGESTION_START]]A[[SUGGESTION_END]]gent doesn’t imply or preclude any architecture assumption or solutions.
[PR 6.44.6-1] The 6G network shall support mechanisms (e.g. Al capabilities such as Al [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) to authorize the received intent(s) from user.
[[SUGGESTION_START]][PR [[SUGGESTION_END]][[SUGGESTION_START]]6.44.6[[SUGGESTION_END]][[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]]] The 6G [[SUGGESTION_END]][[SUGGESTION_START]]system [[SUGGESTION_END]][[SUGGESTION_START]]shall support mechanisms (e.g. Al capabilities such as Al [[SUGGESTION_END]][[SUGGESTION_START]]A[[SUGGESTION_END]][[SUGGESTION_START]]gent) to enable the interaction [[SUGGESTION_END]][[SUGGESTION_START]]with[[SUGGESTION_END]][[SUGGESTION_START]] user, [[SUGGESTION_END]][[SUGGESTION_START]]e.g. [[SUGGESTION_END]][[SUGGESTION_START]]negotiate the [[SUGGESTION_END]][[SUGGESTION_START]]service-related [[SUGGESTION_END]][[SUGGESTION_START]]aspects[[SUGGESTION_END]][[SUGGESTION_START]], to guarantee the [[SUGGESTION_END]][[SUGGESTION_START]]provided ser[[SUGGESTION_END]][[SUGGESTION_START]]vices meets the user expectation[[SUGGESTION_END]][[SUGGESTION_START]]. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE1: the user feedback would be[[SUGGESTION_END]][[SUGGESTION_START]] e.g.[[SUGGESTION_END]] [[SUGGESTION_START]]additional info. regarding performance of requested 3[[SUGGESTION_END]][[SUGGESTION_START]]GPP [[SUGGESTION_END]][[SUGGESTION_START]]services[[SUGGESTION_END]][[SUGGESTION_START]], purpose[[SUGGESTION_END]][[SUGGESTION_START]]/intention[[SUGGESTION_END]][[SUGGESTION_START]] of [[SUGGESTION_END]][[SUGGESTION_START]]user [[SUGGESTION_END]][[SUGGESTION_START]]request[[SUGGESTION_END]][[SUGGESTION_START]]ing[[SUGGESTION_END]][[SUGGESTION_START]] the services[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[PR 6.44.6-[[SUGGESTION_START]]3[[SUGGESTION_END]]] Based on operator policy and user consent, the 6G network shall support mechanisms (e.g. AI capabilities such as Al [[SUGGESTION_START]]A[[SUGGESTION_END]]gent) to provide 3GPP services on demand based on the received intent(s) from user by taking into account of network-related information and information from trusted third-party.
[PR 6.44.6-[[SUGGESTION_START]]4[[SUGGESTION_END]]] Based on operator policy and user consent, the 6G network shall support mechanism to collect charging information for customized service based on received intent(s) from user.
* * * End of Change * * * *
|
S1-254506.zip
|
2026-01-13T14:59:12.202041
|
S1-252100
|
SA1
|
TSGS1_110_Fukuoka
|
CR
|
revised
|
EnergyServ_Ph2 – Normative
|
3GPP TSG SA WG 1 Meeting #110 S1-252100
Fukuoka, Japan, 19 - 23 May 2025 (revision of S1-25xxxx)
CR-Form-v12.3
CHANGE REQUEST
22.261
CR
rev
-
Current version:
20..0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
x
Radio Access Network
x
Core Network
x
Title:
Further consolidation of requirements on service adjustments based on energy-related characteristics
Source to WG:
Orange
Source to TSG:
SA1
Work item code:
EnergyServ_Ph2-REQ
Date:
2025-05-07
Category:
C
Release:
Rel-20
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
Two requirements of clause 6.15a.2.2 correspond to similar functionality and would better be merged into a single requirement. In addition, these requirements should apply to the whole 5G system, not just the 5G network.
Summary of change:
Merge two paragraphs of clause 6.15a.2.2 and replace “5G network” by “5G system” in the merged paragraph.
Consequences if not approved:
Unnecessary complexity in the specification.
Unnecessary restriction of the solution space.
Clauses affected:
6.15a.2.2
Y
N
Other specs
x
Other core specifications
TS/TR ... CR ...
affected:
x
Test specifications
TS/TR ... CR ...
(show related CRs)
x
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
FIRST CHANGE
6.15a.2.2 Requirements
NOTE: The following requirements are expected to produce net energy saving in the 5G network i.e. the energy used to operate the network and deliver services based on these requirements is less that the energy saved.
The following requirements are subject to regulatory requirements, in particular to keep delivering critical services such as emergency calls, PWS, MPS and MCS to the extent possible.
Subject to operator’s policy, the 5G system shall support subscription policies that define a maximum energy credit limit for services without QoS criteria.
Subject to operator’s policy, the 5G system shall support a means to associate energy consumption information with charging information based on subscription policies for services without QoS criteria.
Subject to operator’s policy, the 5G system shall support a mechanism to perform energy consumption credit limit control for services without QoS criteria.
NOTE 1: The result of the credit control is not specified by this requirement.
NOTE 2: Credit control [49] compares against a credit control limit. It is assumed charging events are assigned a corresponding energy consumption and this is compared against a policy of energy credit limit. It is assumed there can be a new policy to limit energy consumption allowed.
Subject to operator’s policy, the 5G system shall support a means to define subscription policies and means to enforce the policy that define a maximum energy consumption (i.e. quantity of energy for a specified period of time) for services without QoS criteria.
NOTE 3: The granularity of the subscription policies can either apply to the subscriber (all services), or to particular services.
The 5G system shall provide a mechanism to include Energy related information as part of charging information.
Subject to operator policy and agreement with 3rd party, the 5G system shall provide a mechanism to support the selection of an application server based on energy related information associated with a set of application servers.
Subject to user consent and operator policy, 5G system shall be able to provide means to modify a communication service based on energy related information criteria based on subscription policies.
Subject to user consent, operator policy and regulatory requirements, the 5G system shall be able to provide means to operate part or the whole network according to energy consumption requirements, which may be based on subscription policies or requested by an authorized 3rd party.
Subject to operator’s policy, regulatory requirements and user consent, the 5G network shall enable the operator to provide means to degrade service performance (e.g. QoS, bitrate) to meet energy rationing constraints.
NOTE 4: This assumes that the degradation in service performance will reduce energy consumption in the 5G network to meet energy rationing constraints.
Subject to operator’s policy, regulatory requirements and user consent, the 5G network shall support subscription policies that include alternative (i.e. degraded) service performance (e.g. QoS parameters, maximum bitrate) of services with QoS criteria for energy saving reasons.
NOTE 5: This requirement implies that the policies could disallow some network energy saving action to be performed for some service with QoS criteria e.g. based on applicability conditions (e.g. slice, application).
Subject to operator’s policy, the 5G network shall be able to support a means to target per UE energy saving actions, based on subscription policies.
Subject to operators’ policy, regulatory requirements, [[SUGGESTION_START]]and user consent, the [[SUGGESTION_END]]5G [[SUGGESTION_START]]system [[SUGGESTION_END]]shall provide mechanisms to adjust communication service (e.g. user plane path, suitable Service Hosting Environment, defer background traffic delivery) [[SUGGESTION_START]]and control the access of UEs to the network (e.g. block traffic, disable [[SUGGESTION_END]][[SUGGESTION_START]]or defer [[SUGGESTION_END]][[SUGGESTION_START]]specific application[[SUGGESTION_END]][[SUGGESTION_START]] traffic[[SUGGESTION_END]][[SUGGESTION_START]]) [[SUGGESTION_END]]considering the change of energy supply mix of the network as one of the factors[[SUGGESTION_START]] or based on the energy-related characteristics of the network[[SUGGESTION_END]].
NOTE 6: It is assumed that 5G network can obtain energy supply mix information. How to obtain this information is out of scope of this requirement. The adjustment is not assumed to be real-time. It is up to the operators’ policy to define the time difference between obtaining the change on energy supply mix information and the actual adjustment of the communication service.
NOTE 7: Degradation of service experience resulting from the adjustment is subject to subscription-based user consent.
Subject to operator’s policy and regulatory requirements, the 5G network shall be able to trigger charging events corresponding to an impacted UE when degrading performance of services with QoS criteria (e.g. to a lower bitrate) in order to achieve energy saving.
Subject to user consent, operator policy and regulatory requirements, the 5G network shall be able to assist an authorized 3rd party to identify a set of target UEs for whom to adjust the provided application service, considering criteria such as the current and future (e.g. predicted) energy-related characteristics of their serving network.
NOTE 8: Future (e.g. predicted) information can refer to the next hour(s) or day(s), to the remaining serving time before shutdown/shortage etc.
END OF CHANGES
|
S1-252100.zip
|
2026-01-13T15:08:13.562979
|
S1-252450
|
SA1
|
TSGS1_110_Fukuoka
|
CR
|
agreed
|
EnergyServ_Ph2 – Normative
|
3GPP TSG SA WG 1 Meeting #110 S1-252450
Fukuoka, Japan, 19 - 23 May 2025 (revision of S1-252100)
CR-Form-v12.3
CHANGE REQUEST
22.261
CR
0836
rev
1
Current version:
20.2.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
x
Radio Access Network
x
Core Network
x
Title:
Further consolidation of requirements on service adjustments based on energy-related characteristics
Source to WG:
Orange
Source to TSG:
SA1
Work item code:
EnergyServ_Ph2-REQ
Date:
2025-05-07
Category:
C
Release:
Rel-20
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
Two requirements of clause 6.15a.2.2 correspond to similar functionality and would better be merged into a single requirement. In addition, these requirements should apply to the whole 5G system, not just the 5G network.
Summary of change:
Merge two paragraphs of clause 6.15a.2.2 and replace “5G network” by “5G system” in the merged paragraph.
Consequences if not approved:
Unnecessary complexity in the specification.
Unnecessary restriction of the solution space.
Clauses affected:
6.15a.2.2
Y
N
Other specs
x
Other core specifications
TS/TR ... CR ...
affected:
x
Test specifications
TS/TR ... CR ...
(show related CRs)
x
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
FIRST CHANGE
6.15a.2.2 Requirements
NOTE: The following requirements are expected to produce net energy saving in the 5G network i.e. the energy used to operate the network and deliver services based on these requirements is less that the energy saved.
The following requirements are subject to regulatory requirements, in particular to keep delivering critical services such as emergency calls, PWS, MPS and MCS to the extent possible.
Subject to operator’s policy, the 5G system shall support subscription policies that define a maximum energy credit limit for services without QoS criteria.
Subject to operator’s policy, the 5G system shall support a means to associate energy consumption information with charging information based on subscription policies for services without QoS criteria.
Subject to operator’s policy, the 5G system shall support a mechanism to perform energy consumption credit limit control for services without QoS criteria.
NOTE 1: The result of the credit control is not specified by this requirement.
NOTE 2: Credit control [49] compares against a credit control limit. It is assumed charging events are assigned a corresponding energy consumption and this is compared against a policy of energy credit limit. It is assumed there can be a new policy to limit energy consumption allowed.
Subject to operator’s policy, the 5G system shall support a means to define subscription policies and means to enforce the policy that define a maximum energy consumption (i.e. quantity of energy for a specified period of time) for services without QoS criteria.
NOTE 3: The granularity of the subscription policies can either apply to the subscriber (all services), or to particular services.
The 5G system shall provide a mechanism to include Energy related information as part of charging information.
Subject to operator policy and agreement with 3rd party, the 5G system shall provide a mechanism to support the selection of an application server based on energy related information associated with a set of application servers.
Subject to user consent and operator policy, 5G system shall be able to provide means to modify a communication service based on energy related information criteria based on subscription policies.
Subject to user consent, operator policy and regulatory requirements, the 5G system shall be able to provide means to operate part or the whole network according to energy consumption requirements, which may be based on subscription policies or requested by an authorized 3rd party.
Subject to operator’s policy, regulatory requirements and user consent, the 5G network shall enable the operator to provide means to degrade service performance (e.g. QoS, bitrate) to meet energy rationing constraints.
NOTE 4: This assumes that the degradation in service performance will reduce energy consumption in the 5G network to meet energy rationing constraints.
Subject to operator’s policy, regulatory requirements and user consent, the 5G network shall support subscription policies that include alternative (i.e. degraded) service performance (e.g. QoS parameters, maximum bitrate) of services with QoS criteria for energy saving reasons.
NOTE 5: This requirement implies that the policies could disallow some network energy saving action to be performed for some service with QoS criteria e.g. based on applicability conditions (e.g. slice, application).
Subject to operator’s policy, the 5G network shall be able to support a means to target per UE energy saving actions, based on subscription policies.
Subject to operators’ policy, regulatory requirements, [[SUGGESTION_START]]and user consent, the [[SUGGESTION_END]]5G [[SUGGESTION_START]]system [[SUGGESTION_END]]shall provide mechanisms to adjust communication service (e.g. user plane path, suitable Service Hosting Environment, defer background traffic delivery) [[SUGGESTION_START]]and control the access of UEs to the network (e.g. block traffic, disable [[SUGGESTION_END]][[SUGGESTION_START]]or defer [[SUGGESTION_END]][[SUGGESTION_START]]specific application[[SUGGESTION_END]][[SUGGESTION_START]] traffic[[SUGGESTION_END]][[SUGGESTION_START]]) [[SUGGESTION_END]]considering the change of energy supply mix of the network as one of the factors[[SUGGESTION_START]] or based on the energy-related characteristics of the network[[SUGGESTION_END]].
NOTE 6: It is assumed that 5G network can obtain energy supply mix information. How to obtain this information is out of scope of this requirement. The adjustment is not assumed to be real-time. It is up to the operators’ policy to define the time difference between obtaining the change on energy supply mix information and the actual adjustment of the communication service.
NOTE 7: Degradation of service experience resulting from the adjustment is subject to subscription-based user consent.
Subject to operator’s policy and regulatory requirements, the 5G network shall be able to trigger charging events corresponding to an impacted UE when degrading performance of services with QoS criteria (e.g. to a lower bitrate) in order to achieve energy saving.
Subject to user consent, operator policy and regulatory requirements, the 5G network shall be able to assist an authorized 3rd party to identify a set of target UEs for whom to adjust the provided application service, considering criteria such as the current and future (e.g. predicted) energy-related characteristics of their serving network.
NOTE 8: Future (e.g. predicted) information can refer to the next hour(s) or day(s), to the remaining serving time before shutdown/shortage etc.
END OF CHANGES
|
S1-252450.zip
|
2026-01-13T15:08:39.390737
|
S1-252295
|
SA1
|
TSGS1_110_Fukuoka
|
CR
|
revised
|
5GSAT_Ph4 - Normative
|
3GPP TSG SA WG 1 Meeting #110 S1-252295
Fukuoka, Japan, 19 - 23 May 2025 (revision of S1-25xxxx)
CR-Form-v12.3
CHANGE REQUEST
22.261
CR
0839
rev
-
Current version:
20.2.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
x
Radio Access Network
x
Core Network
x
Title:
Addition of normative inputs based on FS_5GSAT_Ph4
Source to WG:
Novamint
Source to TSG:
SA1
Work item code:
5GSAT_Ph4-REQ
Date:
2025-05-18
Category:
B
Release:
Rel-20
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
3GPP SA1 has studied several use cases related to multi orbit in Rel-20 FS_5GSAT_Ph4. The corresponding requirements have been captured in TR 22.887.
It is proposed to introduce the corresponding requirements and KPIs into TS 22.261.
Summary of change:
This CR introduces requirements and KPIs related to multi orbits as well as additional requirements as defined in TR22.887 consolidation
Consequences if not approved:
Multi orbit satellite access is not supported.
Clauses affected:
6.46.2, 6.46.3, 6.46.12 (new), 7.4.2, 9.4
Y
N
Other specs
x
Other core specifications
TS/TR ... CR ...
affected:
x
Test specifications
TS/TR ... CR ...
(show related CRs)
x
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
* * * First Change * * * *
6.46 Satellite access
6.46.1 Overview
The following requirements apply for a 5G system with satellite access.
NOTE: For the KPIs for a 5G system with satellite access, see clause 7.4.
6.46.2 General
A 5G system with satellite access shall support different configurations where the radio access network is either a satellite NG-RAN or a non-3GPP satellite access network, or both.
Subject to operator’s policies, a 5G System with satellite access shall be able to support Resilient Notification Service to notify users, with valid subscription to the notification service, about a missed mobile terminated service when the user is unreachable via satellite access.
NOTE: The Resilient Notification Service can provide the user with service information e.g. caller’s information and service type.
A UE supporting satellite access shall be able to provide or assist in providing its location to the 5G network.
A 5G system with satellite access shall be able to determine a UE's location in order to provide service (e.g. route traffic, support emergency calls) in accordance with the governing national or regional regulatory requirements applicable to that UE.
NOTE 1: This is also applicable for UE using only satellite access. The determination of a UE’s location can be based on 3GPP and/or non-3GPP positioning technologies subject to operator’s policies.
A 5G system with satellite access shall be able to support low power MIoT type of communications.
Subject to the regulatory requirements and operator’s policy, a 5G system with satellite access shall be able to provide services to an authorized UE independently of the UE’s GNSS capability.
Subject to regulatory requirements and operator’s policies, a 5G system with satellite access shall be able to support collection of information on usage statistics and location of the UEs that are connected to the satellite.
Subject to regulatory requirements and operator’s policy, a 5G System supporting satellite access and Broadcast Services shall be able to deliver Media Broadcast to a UE which is not registered.
NOTE 2: Subject to an agreement (SLA) between the MNO and the SNO the above requirement can be applied
NOTE 3: The UEs are adapted for receiving broadcast services only and are not expected to initiate transmission
[[SUGGESTION_START]]Subject to the regulatory requirements and operator’s policy, a 5G system with satellite access shall be able to support a mechanism to deliver specific data traffic of a UE related to specific service (s) to a preferred geographical location.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 4: There are satellite ground stations deployed within the preferred geographical location. The link between satellite and satellite ground station location is out of 3GPP scope[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to operator’s policy, the 5G network with satellite access shall be able to maintain the connection to an available core network with the same service level agreement (SLA) for resilient satellite communication.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 5: The resilient communication can be achieved through a single satellite or multiple satellites in different orbit types with different characteristics[[SUGGESTION_END]]
* * * Second Change * * * *
6.46.3 Service continuity
For a 5G system with satellite access, the following requirements apply:
A 5G system with satellite access shall support service continuity between 5G terrestrial access network and 5G satellite access networks owned by the same operator or owned by different operators having an agreement.
Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall support service continuity (with minimum service interruption) for a UE engaged in an active communication, when the UE changes from a direct network connection via 5G terrestrial access to an indirect network connection via a relay UE (using satellite access) and vice-versa.
NOTE: It is assumed that the 5G terrestrial access network and the satellite access network belong to the same operator.
Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall be able to support service continuity (with minimum service interruption) of a UE-Satellite-UE communication when the UE communication path moves between serving satellites (due to the movement of the UE and/or the satellites).
Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall support service continuity (with minimum service interruption) of a UE-Satellite-UE communication when the communication path between UEs extends to additional satellites (through ISLs).
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s polic[[SUGGESTION_END]][[SUGGESTION_START]]y[[SUGGESTION_END]][[SUGGESTION_START]], a 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g.; altitude, orbital characteristics, satellite capabilities) shall support service continuity with no interruption when the communication path of a UE (e.g., Relay or CPE) mounted and integrated in a high-speed platform is moving between satellites of the same network operating in any orbit types with given characteristics (e.g., GEO, MEO or LEO).[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s polic[[SUGGESTION_END]][[SUGGESTION_START]]y[[SUGGESTION_END]][[SUGGESTION_START]], a 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g.; altitude, orbital characteristics, satellite capabilities) shall support minimum service interruption when the communication path of a UE (e.g., Relay or CPE) mounted and integrated in a high-speed platform is moving between terrestrial access and satellite access networks or between satellites networks potentially supporting different orbit types with different characteristics.[[SUGGESTION_END]]
* * * Third Change * * * *
[[SUGGESTION_START]]6.46.12 [[SUGGESTION_END]][[SUGGESTION_START]]M[[SUGGESTION_END]][[SUGGESTION_START]]ulti-orbit[[SUGGESTION_END]]
[[SUGGESTION_START]]6.46.12.1 Description[[SUGGESTION_END]]
[[SUGGESTION_START]]A 5G system with satellite access ca[[SUGGESTION_END]][[SUGGESTION_START]]n be composed of multiple satellite orbit types such as GEO, [[SUGGESTION_END]][[SUGGESTION_START]]MEO, LEO and each of these satellite orbit types can have [[SUGGESTION_END]][[SUGGESTION_START]]different characteristics (e.g., altitude, orbital characteristics, satellite capabilities)[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]The requirements below refer to multi-orbit.[[SUGGESTION_END]]
[[SUGGESTION_START]]6.46.12.2 Requirements[[SUGGESTION_END]]
[[SUGGESTION_START]]A 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to improve the network’s energy efficiency, by e.g., switching different power saving modes considering user density and data demand.[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s policy, a 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to provide the UE with the necessary information to acquire and access the suitable satellite among those available.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 1: The information provided to the UE can include satellites information operated by roaming partners.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 2: Acquisition of satellites may be required prior to the initial access procedure, including, e.g. the determination of the antenna pointing direction. In this use case, network selection procedures follow the existing procedures.[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator policies, the 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support switching between satellites with orbit types having different characteristics depending on e.g. latency requirements, QoS requirements, access capabilities and availability to support effective management of capacity and quality, aligned with the anticipated level of service.[[SUGGESTION_END]]
[[SUGGESTION_START]]A 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to support establishing the initial UE connection to the network through satellites with a given characteristics and then having the UE data traffic using satellite with different characteristics.[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s policies, a 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to provide high throughput performances to the UE (e.g., Relay or CPE) mounted and integrated in a high-speed platform ensuring consistent and maintained QoE to the end users.[[SUGGESTION_END]]
[[SUGGESTION_START]]Based on regulatory requirements and operators’ policy, the 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support Service Hosting Environment (e.g., edge application server) on satellites with orbit types with different characteristics.[[SUGGESTION_END]]
[[SUGGESTION_START]]Based on regulatory requirements, operators’ policy and agreement with 3rd party, the 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support a mechanism to provide most suitable service hosting environment across satellites with orbit types having different characteristics.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE[[SUGGESTION_END]][[SUGGESTION_START]] 3[[SUGGESTION_END]][[SUGGESTION_START]]: One example is an operator can choose the most suitable service hosting environment on-board of satellites, based on the topology of satellites as they are moving on the orbits.[[SUGGESTION_END]]
[[SUGGESTION_START]]6.46.12.3 Requirements related to MBRS[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s policies, the 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to support mobile base station relays to access satellites with orbit types having different characteristics considering e.g., availability of the coverage, latency, data rate, required QoS.[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s policies, the 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support connectivity between a mobile base station relay (MBSR) and the 5G Core through simultaneously using terrestrial and one multi orbit satellite access path taking into account the respective capabilities (e.g., latency, data rate) and availability of the different satellite access (e.g., over GEO, MEO, LEO) to map the traffic with the aggregated QoS required at the MBSR.[[SUGGESTION_END]]
* * * Fourth Change * * * *
7.4.2 Requirements
A 5G system providing service with satellite access shall be able to support GEO based satellite access with up to 285 ms end-to-end latency.
NOTE 1: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system providing service with satellite access shall be able to support MEO based satellite access with up to 95 ms end-to-end latency.
NOTE 2: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system providing service with satellite access shall be able to support LEO based satellite access with up to 35 ms end-to-end latency.
NOTE 3: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system shall support negotiation on quality of service taking into account latency penalty to optimise the QoE for UE.
The 5G system with satellite access shall support high uplink data rates for 5G satellite UEs.
The 5G system with satellite access shall support high downlink data rates for 5G satellite UEs.
The 5G system with satellite access shall support communication service availabilities of at least 99,99%.
Table 7.4.2-1: Performance requirements for satellite access
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
(note 1)
Area traffic capacity
(UL)
(note 1)
Overall user density
Activity factor
UE speed
UE type
Others
Pedestrian
(note 2)
[1] Mbit/s
[100] kbit/s
1,5 Mbit/s/km2
150 kbit/s/km2
[100]/km2
[1,5] %
Pedestrian
Handheld
-
Public safety
[3,5] Mbit/s
[3,5] Mbit/s
TBD
TBD
TBD
N/A
100 km/h
Handheld
-
Vehicular connectivity
(note 3)
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
50 %
Up to 250 km/h
Vehicle mounted
-
Airplanes connectivity
(note 4)
[[SUGGESTION_START]][1] Gbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]per plane[[SUGGESTION_END]]
[[SUGGESTION_START]][300] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]per plane[[SUGGESTION_END]]
TBD
TBD
TBD
[[SUGGESTION_START]]40%[[SUGGESTION_END]]
[[SUGGESTION_START]]Up to [1500] km/h[[SUGGESTION_END]]
Airplane mounted
-
Stationary
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
N/A
Stationary
Building mounted
-
Video surveillance
(note 4a)
[0,5] Mbit/s
[3] Mbit/s
TBD
TBD
TBD
N/A
Up to 120km/h or
stationary
(note 4b)
Vehicle mounted or fixed installation
-
Narrowband IoT connectivity
[2] kbit/s
[10] kbit/s
8 kbit/s/km2
40 kbit/s/km2
[400]/km2
[1] %
[Up to 100 km/h]
IoT
-
IMS voice call using GEO
(note 7)
[1-3] kbit/s
[1-3] kbit/s
N/A
N/A
N/A
N/A
TBD
Handheld
Call set up time (note 8) ≤30 s
(note 9)
Note 1: Area capacity is averaged over a satellite beam.
Note 2: Data rates based on Extreme long-range coverage target values in clause 6.17.2. User density based on rural area in Table 7.1-1.
Note 3: Based on Table 7.1-1
Note 4: [[SUGGESTION_START]]Required experienced peak data rate corresponding to the aggregated passenger traffic at aircraft level[[SUGGESTION_END]]
[[SUGGESTION_START]]Based on an assumption of 450 seats, average take rate of 75% (free model) and load factor of 85%[[SUGGESTION_END]]
[[SUGGESTION_START]]Assumption of 3:1 Downlink / Uplink ratio, anticipating future usages[[SUGGESTION_END]]
[[SUGGESTION_START]]The Downlink & Uplink throughput can be achieved using one or multiple links[[SUGGESTION_END]]
Note 4a: Refer to video surveillance data transmitted (in UL) from a UE on the ground (e.g. picture or video from a camera) using satellite NG-RAN to connect to 5GC, and video surveillance-related configuration or control data sent (in DL) to the UE/device. 0.5 Mbit/s for DL experienced data rate is based on MAVLINK protocol that is widely used for UAV control. 3 Mbit/s for UL experienced data rate is based on the assumed sum from 2.5 Mbit/s for video streaming and 0.5 Mbit/s for data transmission.
Note 4b: Up to 120km/h applies to vehicle mounted while stationary applies to fixed installation.
Note 5: All the values in this table are targeted values and not strict requirements.
Note 6: Performance requirements for all the values in this table should be analyzed independently for each scenario.
Note 7: All the values defined for IMS voice call using GEO satellite access are generic for both regular and emergency calls.
Note 8: call set up time refers to [56];
Note 9: 30s is the upper bound that is derived based on the user’s patience suggestions (30s) in [57];
* * * Fifth Change * * * *
9.4 Satellite Access
In a 5G system with satellite access, charging data records associated with satellite access(es) shall include the location of the associated UE(s) with satellite access.
NOTE: The precision of the location of the UE can be based on the capabilities of the UE or of the network.
A 5G system with satellite access supporting S&F Satellite operation shall be able to collect charging information per UE or per application (e.g., number of UEs, data volume, duration, involved satellites).
A 5G system with satellite access shall be able to collect charging information for a UE registered to a HPLMN or a VPLMN, for UE-Satellite-UE communication.
[[SUGGESTION_START]]A 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to collect and distinguish charging information related to user data traffic or mobile base station relay traffic over different orbit types having different characteristics (e.g., LEO, MEO, GEO).[[SUGGESTION_END]]
* * * End of Changes * * * *
|
S1-252295.zip
|
2026-01-13T15:11:55.632231
|
S1-252452
|
SA1
|
TSGS1_110_Fukuoka
|
CR
|
agreed
|
5GSAT_Ph4 - Normative
|
3GPP TSG SA WG 1 Meeting #110 S1-252452
Fukuoka, Japan, 19 - 23 May 2025 (revision of S1-252295)
CR-Form-v12.3
CHANGE REQUEST
22.261
CR
0839
rev
1
Current version:
20.2.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
x
Radio Access Network
x
Core Network
x
Title:
Addition of normative inputs based on FS_5GSAT_Ph4
Source to WG:
Novamint
Source to TSG:
SA1
Work item code:
5GSAT_Ph4-REQ
Date:
2025-05-18
Category:
B
Release:
Rel-20
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
3GPP SA1 has studied several use cases related to multi orbit in Rel-20 FS_5GSAT_Ph4. The corresponding requirements have been captured in TR 22.887.
It is proposed to introduce the corresponding requirements and KPIs into TS 22.261.
Summary of change:
This CR introduces requirements and KPIs related to multi orbits as well as additional requirements as defined in TR22.887 consolidation
Consequences if not approved:
Multi orbit satellite access is not supported.
Clauses affected:
6.46.2, 6.46.3, 6.46.12 (new), 7.4.2, 9.4
Y
N
Other specs
x
Other core specifications
TS/TR ... CR ...
affected:
x
Test specifications
TS/TR ... CR ...
(show related CRs)
x
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
S1-252295
* * * First Change * * * *
6.46 Satellite access
6.46.1 Overview
The following requirements apply for a 5G system with satellite access.
NOTE: For the KPIs for a 5G system with satellite access, see clause 7.4.
6.46.2 General
A 5G system with satellite access shall support different configurations where the radio access network is either a satellite NG-RAN or a non-3GPP satellite access network, or both.
Subject to operator’s policies, a 5G System with satellite access shall be able to support Resilient Notification Service to notify users, with valid subscription to the notification service, about a missed mobile terminated service when the user is unreachable via satellite access.
NOTE: The Resilient Notification Service can provide the user with service information e.g. caller’s information and service type.
A UE supporting satellite access shall be able to provide or assist in providing its location to the 5G network.
A 5G system with satellite access shall be able to determine a UE's location in order to provide service (e.g. route traffic, support emergency calls) in accordance with the governing national or regional regulatory requirements applicable to that UE.
NOTE 1: This is also applicable for UE using only satellite access. The determination of a UE’s location can be based on 3GPP and/or non-3GPP positioning technologies subject to operator’s policies.
A 5G system with satellite access shall be able to support low power MIoT type of communications.
Subject to the regulatory requirements and operator’s policy, a 5G system with satellite access shall be able to provide services to an authorized UE independently of the UE’s GNSS capability.
Subject to regulatory requirements and operator’s policies, a 5G system with satellite access shall be able to support collection of information on usage statistics and location of the UEs that are connected to the satellite.
Subject to regulatory requirements and operator’s policy, a 5G System supporting satellite access and Broadcast Services shall be able to deliver Media Broadcast to a UE which is not registered.
NOTE 2: Subject to an agreement (SLA) between the MNO and the SNO the above requirement can be applied
NOTE 3: The UEs are adapted for receiving broadcast services only and are not expected to initiate transmission
[[SUGGESTION_START]]Subject to the regulatory requirements and operator’s policy, a 5G system with satellite access shall be able to support a mechanism to deliver specific data traffic of a UE related to specific service(s) to a preferred geographical location.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE[[SUGGESTION_END]][[SUGGESTION_START]] 4[[SUGGESTION_END]][[SUGGESTION_START]]: There are satellite ground stations deployed within the preferred geographical location. The link between satellite and satellite ground station location is out of 3GPP scope[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to operator’s policy, the 5G network with satellite access shall be able to maintain the connection to an available core network with the same service level agreement (SLA) for resilient satellite communication.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE[[SUGGESTION_END]][[SUGGESTION_START]] 5[[SUGGESTION_END]][[SUGGESTION_START]]: The resilient communication can be achieved through a single satellite or multiple satellites in different orbit types with different characteristics[[SUGGESTION_END]]
* * * Second Change * * * *
6.46.3 Service continuity
For a 5G system with satellite access, the following requirements apply:
A 5G system with satellite access shall support service continuity between 5G terrestrial access network and 5G satellite access networks owned by the same operator or owned by different operators having an agreement.
Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall support service continuity (with minimum service interruption) for a UE engaged in an active communication, when the UE changes from a direct network connection via 5G terrestrial access to an indirect network connection via a relay UE (using satellite access) and vice-versa.
NOTE: It is assumed that the 5G terrestrial access network and the satellite access network belong to the same operator.
Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall be able to support service continuity (with minimum service interruption) of a UE-Satellite-UE communication when the UE communication path moves between serving satellites (due to the movement of the UE and/or the satellites).
Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall support service continuity (with minimum service interruption) of a UE-Satellite-UE communication when the communication path between UEs extends to additional satellites (through ISLs).
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s policies, a 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support service continuity with no service interruption when the UE communication path changes between satellites of the same network operating in any orbit types with given characteristics (e.g., GEO, MEO or LEO).[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s policies, a 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support minimum service interruption when the UE communication path (e.g., Relay or CPE) changes between terrestrial access and satellite access networks or between satellites networks potentially supporting different orbit types with different characteristics.[[SUGGESTION_END]]
* * * Third Change * * * *
[[SUGGESTION_START]]6.46.12 [[SUGGESTION_END]][[SUGGESTION_START]]M[[SUGGESTION_END]][[SUGGESTION_START]]ulti-orbit[[SUGGESTION_END]]
[[SUGGESTION_START]]6.46.12.1 Description[[SUGGESTION_END]]
[[SUGGESTION_START]]A 5G system with satellite access ca[[SUGGESTION_END]][[SUGGESTION_START]]n be composed of multiple satellite orbit types such as GEO, [[SUGGESTION_END]][[SUGGESTION_START]]MEO, LEO and each of these satellite orbit types can have [[SUGGESTION_END]][[SUGGESTION_START]]different characteristics (e.g., altitude, orbital characteristics, satellite capabilities)[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]The requirements below refer to multi-orbit.[[SUGGESTION_END]]
[[SUGGESTION_START]]6.46.12.2 Requirements[[SUGGESTION_END]]
[[SUGGESTION_START]]A 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to improve the network’s energy efficiency of the satellite access network, by e.g., switching different power saving modes of the satellites based on user density and/or data demand.[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s policy, a 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to assist a UE (e.g., provide the UE with the necessary information) to acquire and access the suitable satellite among those available.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 1: The information provided to the UE can include satellites information operated by roaming partners.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 2: Acquisition of satellite signals may be required prior to the initial access procedure, including, e.g. the determination of the antenna pointing direction. In this use case, network selection procedures follow the existing procedures.[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator policies, the 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support the selection of suitable satellites for the UE and the switching between satellites with orbit types having different characteristics depending on e.g. latency requirements, QoS requirements, access capabilities and availability.[[SUGGESTION_END]]
[[SUGGESTION_START]]A 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to support establishing the initial UE connection to the network through satellites with a given characteristics and then having the UE data traffic using satellite with different characteristics.[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s policies, a 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to provide high throughput performance and maintain consistent QoE to the users accessing the UE (e.g., Relay or CPE) when the UE is moving with a high speed.[[SUGGESTION_END]]
[[SUGGESTION_START]]Based on regulatory requirements, operators’ policy and agreement with 3rd party, the 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support a mechanism to provide suitable Service Hosting Environment across satellites with orbit types having different characteristics.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 3: One example is an operator can choose the most suitable service hosting environment on-board of satellites, based on the topology of satellites as they are moving on the orbits.[[SUGGESTION_END]]
[[SUGGESTION_START]]6.46.12.3 Requirements related to [[SUGGESTION_END]][[SUGGESTION_START]]mobile base station relays[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s policies, the 5G network with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to support mobile base station relays to access satellites with orbit types having different characteristics considering e.g., availability of the coverage, latency, data rate, required QoS.[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to regulatory requirements and operator’s policies, the 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall support connectivity between a mobile base station relay and the 5G Core through simultaneously using terrestrial and one multi orbit satellite access path taking into account the respective capabilities (e.g., latency, data rate) and availability of the different satellite access (e.g., over GEO, MEO, LEO) to map the traffic with the aggregated QoS required at the mobile base station relay.[[SUGGESTION_END]]
* * * Fourth Change * * * *
7.4.2 Requirements
A 5G system providing service with satellite access shall be able to support GEO based satellite access with up to 285 ms end-to-end latency.
NOTE 1: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system providing service with satellite access shall be able to support MEO based satellite access with up to 95 ms end-to-end latency.
NOTE 2: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system providing service with satellite access shall be able to support LEO based satellite access with up to 35 ms end-to-end latency.
NOTE 3: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system shall support negotiation on quality of service taking into account latency penalty to optimise the QoE for UE.
The 5G system with satellite access shall support high uplink data rates for 5G satellite UEs.
The 5G system with satellite access shall support high downlink data rates for 5G satellite UEs.
The 5G system with satellite access shall support communication service availabilities of at least 99,99%.
Table 7.4.2-1: Performance requirements for satellite access
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
(note 1)
Area traffic capacity
(UL)
(note 1)
Overall user density
Activity factor
UE speed
UE type
Others
Pedestrian
(note 2)
[1] Mbit/s
[100] kbit/s
1,5 Mbit/s/km2
150 kbit/s/km2
[100]/km2
[1,5] %
Pedestrian
Handheld
-
Public safety
[3,5] Mbit/s
[3,5] Mbit/s
TBD
TBD
TBD
N/A
100 km/h
Handheld
-
Vehicular connectivity
(note 3)
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
50 %
Up to 250 km/h
Vehicle mounted
-
Airplanes connectivity
(note 4)
[[SUGGESTION_START]][1] Gbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]per plane[[SUGGESTION_END]]
[[SUGGESTION_START]][300] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]per plane[[SUGGESTION_END]]
TBD
TBD
TBD
[[SUGGESTION_START]]40%[[SUGGESTION_END]]
[[SUGGESTION_START]]Up to [1500] km/h[[SUGGESTION_END]]
Airplane mounted
-
Stationary
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
N/A
Stationary
Building mounted
-
Video surveillance
(note 4a)
[0,5] Mbit/s
[3] Mbit/s
TBD
TBD
TBD
N/A
Up to 120km/h or
stationary
(note 4b)
Vehicle mounted or fixed installation
-
Narrowband IoT connectivity
[2] kbit/s
[10] kbit/s
8 kbit/s/km2
40 kbit/s/km2
[400]/km2
[1] %
[Up to 100 km/h]
IoT
-
IMS voice call using GEO
(note 7)
[1-3] kbit/s
[1-3] kbit/s
N/A
N/A
N/A
N/A
TBD
Handheld
Call set up time (note 8) ≤30 s
(note 9)
Note 1: Area capacity is averaged over a satellite beam.
Note 2: Data rates based on Extreme long-range coverage target values in clause 6.17.2. User density based on rural area in Table 7.1-1.
Note 3: Based on Table 7.1-1
Note 4: [[SUGGESTION_START]]Required experienced peak data rate corresponding to the aggregated passenger traffic at aircraft level[[SUGGESTION_END]]
[[SUGGESTION_START]]Based on an assumption of 450 seats, average take rate of 75% (free model) and load factor of 85%[[SUGGESTION_END]]
[[SUGGESTION_START]]Assumption of 3:1 Downlink / Uplink ratio, anticipating future usages[[SUGGESTION_END]]
[[SUGGESTION_START]]The Downlink & Uplink throughput can be achieved using one or multiple links[[SUGGESTION_END]]
Note 4a: Refer to video surveillance data transmitted (in UL) from a UE on the ground (e.g. picture or video from a camera) using satellite NG-RAN to connect to 5GC, and video surveillance-related configuration or control data sent (in DL) to the UE/device. 0.5 Mbit/s for DL experienced data rate is based on MAVLINK protocol that is widely used for UAV control. 3 Mbit/s for UL experienced data rate is based on the assumed sum from 2.5 Mbit/s for video streaming and 0.5 Mbit/s for data transmission.
Note 4b: Up to 120km/h applies to vehicle mounted while stationary applies to fixed installation.
Note 5: All the values in this table are targeted values and not strict requirements.
Note 6: Performance requirements for all the values in this table should be analyzed independently for each scenario.
Note 7: All the values defined for IMS voice call using GEO satellite access are generic for both regular and emergency calls.
Note 8: call set up time refers to [56];
Note 9: 30s is the upper bound that is derived based on the user’s patience suggestions (30s) in [57];
* * * Fifth Change * * * *
9.4 Satellite Access
In a 5G system with satellite access, charging data records associated with satellite access(es) shall include the location of the associated UE(s) with satellite access.
NOTE: The precision of the location of the UE can be based on the capabilities of the UE or of the network.
A 5G system with satellite access supporting S&F Satellite operation shall be able to collect charging information per UE or per application (e.g., number of UEs, data volume, duration, involved satellites).
A 5G system with satellite access shall be able to collect charging information for a UE registered to a HPLMN or a VPLMN, for UE-Satellite-UE communication.
[[SUGGESTION_START]]A 5G system with satellite access supporting multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to collect and distinguish charging information related to user data traffic across satellites with different orbit types having different characteristics (e.g., LEO, MEO, GEO).[[SUGGESTION_END]]
[[SUGGESTION_START]]The 5G system with satellite access supporting connectivity over multiple satellite orbit types with different characteristics (e.g., altitude, orbital characteristics, satellite capabilities) shall be able to collect and distinguish charging information related to the different satellite access traffic carried via a mobile base station relay.[[SUGGESTION_END]]
* * * End of Changes * * * *
|
S1-252452.zip
|
2026-01-13T15:12:21.567833
|
S1-242053
|
SA1
|
TSGS1_107_Maastricht
|
CR
|
revised
|
Quality improvement contributions
|
3GPP TSG SA WG 1 Meeting #107 S1-242053
Maastricht, The Netherlands, 19-23 August 2024
CR-Form-v12.2
CHANGE REQUEST
22.261
CR
0794
rev
-
Current version:
19.7.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
Radio Access Network
Core Network
Title:
Correction of editoral errors in punctuation mark and format
Source to WG:
CATT
Source to TSG:
SA1
Work item code:
NetShare
Date:
2024-08-09
Category:
F
Release:
Rel-19
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
There are some punctuation mark and format errors, which are impacting the reader experience and understanding of the requirements, e.g. missing space between the words, redundant blank lines left.
Summary of change:
This CR intends to correct the editoral errors.
Consequences if not approved:
Some descriptions and requirements are misunderstood.
Clauses affected:
6.21.1, 6.21.2
Y
N
Other specs
Other core specifications
TS/TR ... CR ...
affected:
Test specifications
TS/TR ... CR ...
(show related CRs)
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
* * * First Change * * * *
6.21 NG-RAN Sharing
6.21.1 Description
The increased density of access nodes needed to meet future performance objectives poses considerable challenges in deployment and acquiring spectrum and antenna locations. RAN sharing is seen as a technical solution to these issues.
In RAN Sharing operations, NG-RAN resources can be used by multiple network operators. Indirect Network Sharing is one of the possible sharing methods.
During NG-RAN sharing, the security and privacy of shared networks, non-shared networks, and subscribers need to be maintained without negative effects. Especially in the case of Indirect Network Sharing, where the involvement of the core network of the hosting operator e.g. for signalling exchange between the users and the core network of the participating operator could cause exposure of the subscriber’s information to the hosting network, an extra scrutiny of the security mechanism is expected to avoid sharing the information that is not needed for the Indirect Network Sharing operation (e.g. network topology) and protect the information that is needed for the Indirect Network Sharing operation between the hosting operator and the participating operator.
6.21.2 Requirements
6.21.2.1 General
Requirements related to NG-RAN sharing are described in 3GPP TS 22.101 [6], mainly in clause 28.2.
A 5G satellite access network shall support NG-RAN sharing.
6.21.2.2 Indirect network sharing
The 5G system shall be able to support Indirect Network Sharing between the Shared NG-RAN and one or more Participating NG-RAN Operators’ core networks, by means of the connection being routed through the Hosting NG-RAN Operator’s core network.
NOTE 1: Requirements of Indirect Network Sharing assume no impact on UE.
NOTE 2: For more information on Indirect Network Sharing see Annex I.
Indirect Network Sharing shall be transparent to the user.
NOTE 3: This requirement is aligned with the existing requirement in 3GPP TS 22.101 [6] clause 4.9.
The following existing service requirements related to network sharing in 3GPP TS 22.101 [6] [[SUGGESTION_START]]are [[SUGGESTION_END]]appl[[SUGGESTION_START]]ied[[SUGGESTION_END]][[SUGGESTION_START]] to Indirect Network Sharing[[SUGGESTION_END]]:
- clause 4.2.1,
- clause 28.2.3, and
- clause 28.2.5.
Subject to the agreement between the hosting and participating operator, the 5G system shall support a means to
enable a UE of the Participating NG-RAN Operator to:
- access their subscribed PLMN services when accessing a Shared NG-RAN, and/or,
- obtain its subscribed services, including Hosted Services, of participating operator via a Shared NG-RAN.
Based on operator policy, the 5G system shall support a mechanism to enable an authorized UE with a subscription to a Participating Operator to select and access a Shared NG-RAN.
Based on operator policy, the 5G system shall support access control for an authorized UE accessing a Shared NG-RAN and be able to apply differentiated access control for different Shared NG-RANs when more than one Shared NG-RAN are available for the Participating Operator to choose from.
Based on operator policy, the 5G system shall enable the Participating Operator to provide steering information in order to assist a UE with access network selection amongst the Hosting Operator’s available Shared RAN(s).
The 5G system shall support service continuity for UEs that are moving between different Shared NG-RANs and/or between a Shared NG-RAN and a non-Shared NG-RAN.
The 5G system shall be able to provide a UE accessing a Shared NG-RAN network with positioning service in compliance with regulatory requirements.
Subject to regulatory requirements and mutual agreement between the participating operators and the hosting operator, the requirements to support regulatory services, e.g., PWS or emergency calls apply to Indirect Network Sharing.
[[SUGGESTION_START]]S[[SUGGESTION_END]]ubject to agreement between operators, the 5G system shall enable the Shared NG-RAN of a hosting operator to provide services for inbound roaming users.
The 5G core network shall be able to support collection of charging information associated with a UE accessing a Shared NG-RAN using Indirect Network Sharing, which refers to the resource usage of hosting operator’s core network.
* * * End of Changes * * * *
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S1-242053.zip
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2026-01-13T17:04:22.168341
|
S1-242093
|
SA1
|
TSGS1_107_Maastricht
|
CR
|
revised
|
Quality improvement contributions
|
3GPP TSG-SA1 Meeting #107 S1-242093
Maastricht, The Netherlands, 19-23 August 2024
CR-Form-v12.3
CHANGE REQUEST
22.261
CR
0796
rev
-
Current version:
19.7.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
Radio Access Network
Core Network
Title:
Addrssing editoral errors
Source to WG:
ZTE
Source to TSG:
SA1
Work item code:
5GSAT_Ph3
Date:
2024-08-08
Category:
D
Release:
Rel-19
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
Several typos were identified.
Summary of change:
Addressing identified editoral errors.
Consequences if not approved:
Less readability of the specification.
Clauses affected:
6.9.1, 6.46.7, 7.4.2
Y
N
Other specs
Other core specifications
TS/TR ... CR ...
affected:
Test specifications
TS/TR ... CR ...
(show related CRs)
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
* * * * Start of 1st Change * * * *
6.9.1 Description
The UE (remote UE) can connect to the network directly (direct network connection), connect using another UE as a relay UE (indirect network connection), or connect using both direct and indirect connections. Relay UEs can be used in many different scenarios and verticals (inHome, SmartFarming, SmartFactories, Public Safety and others). In these cases, the use of relay UEs can be used to improve the energy efficiency and coverage of the system.
Remote UEs can be anything from simple wearables, such as sensors embedded in clothing, to a more sophisticated wearable UE monitoring biometrics. They can also be non-wearable UEs that communicate in a Personal Area Network such as a set of home appliances (e.g. smart thermostat and entry key), or the electronic UEs in an office setting (e.g. smart printers), or a smart flower pot that can be remotely activated to water the plant.
When a remote UE is attempting to establish an indirect network connection, there might be several relay UEs that are available in proximity and supporting selection procedures of an appropriate relay UE among the available relay UEs is needed.
Indirect network connection covers the use of relay UEs for connecting a remote UE to the 3GPP network. There can be one or more relay UE(s) (more than one hop) between the network and the remote UE.
A ProSe UE-to-UE Relay can also be used to connect two remote Public Safety UEs using direct device connection. There can be one or more ProSe UE-to-UE Relay(s) (more than one hop) between the two remote Public Safety UEs.
* * * * Start of 2nd Change * * * *
6.46.7 Satellite and Relay UEs
For a 5G system with satellite access, the following requirements apply:
- A 5G system with satellite access shall be able to support relay UEs with satellite access.
NOTE: The connection between a relay UE and a remote UE is the same regardless of whether the relay UE is using satellite access or not.
- A 5G system with satellite access shall support mobility management of relay UEs and the remote UEs connected to the relay UE between a 5G satellite access network and a 5G terrestrial network, and between 5G satellite access networks.
- A 5G system with satellite access shall support joint roaming between different 5G networks of a relay UE and the remote UEs connected to that relay UE.
* * * * Start of 3rd Change * * * *
7.4.2 Requirements
A 5G system providing service with satellite access shall be able to support GEO based satellite access with up to 285 ms end-to-end latency.
NOTE 1: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system providing service with satellite access shall be able to support MEO based satellite access with up to 95 ms end-to-end latency.
NOTE 2: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system providing service with satellite access shall be able to support LEO based satellite access with up to 35 ms end-to-end latency.
NOTE 3: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system shall support negotiation on quality of service taking into account latency penalty to optimise the QoE for UE.
The 5G system with satellite access shall support high uplink data rates for 5G satellite UEs.
The 5G system with satellite access shall support high downlink data rates for 5G satellite UEs.
The 5G system with satellite access shall support communication service availabilities of at least [[SUGGESTION_START]]99.99%[[SUGGESTION_END]].
Table 7.4.2-1: Performance requirements for satellite access
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
(note 1)
Area traffic capacity
(UL)
(note 1)
Overall user density
Activity factor
UE speed
UE type
Pedestrian
(note 2)
[1] Mbit/s
[100] kbit/s
1,5 Mbit/s/km2
150 kbit/s/km2
[100]/km2
[1,5] %
Pedestrian
Handheld
Public safety
[3,5] Mbit/s
[3,5] Mbit/s
TBD
TBD
TBD
N/A
100 km/h
Handheld
Vehicular connectivity
(note 3)
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
50 %
Up to 250 km/h
Vehicle mounted
Airplanes connectivity
(note 4)
360 Mbit/s/ plane
180 Mbit/s/ plane
TBD
TBD
TBD
N/A
Up to 1000 km/h
Airplane mounted
Stationary
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
N/A
Stationary
Building mounted
Video surveillance
(note 4a)
[0,5] Mbit/s
[3] Mbit/s
TBD
TBD
TBD
N/A
Up to 120km/h or
stationary
(note 4b)
Vehicle mounted or fixed installation
Narrowband IoT connectivity
[2] kbit/s
[10] kbit/s
8 kbit/s/km2
40 kbit/s/km2
[400]/[[SUGGESTION_START]] km2[[SUGGESTION_END]]
[1] %
[Up to 100 km/h]
IoT
Note 1: Area capacity is averaged over a satellite beam.
Note 2: Data rates based on Extreme long-range coverage target values in clause 6.17.2. User density based on rural area in Table 7.1-1.
Note 3: Based on Table 7.1-1
Note 4: Based on an assumption of 120 users per plane 15/7.5 Mbit/s data rate and 20 % activity factor per user
Note 4a: Refer to video surveillance data transmitted (in UL) from a UE on the ground (e.g. picture or video from a camera) using satellite NG-RAN to connect to 5GC, and video surveillance-related configuration or control data sent (in DL) to the UE/device. 0.5 Mbit/s for DL experienced data rate is based on MAVLINK protocol that is widely used for UAV control. 3 Mbit/s for UL experienced data rate is based on the assumed sum from 2.5 Mbit/s for video streaming and 0.5 Mbit/s for data transmission.
Note 4b: Up to 120km/h applies to vehicle mounted while stationary applies to fixed installation.
Note 5: All the values in this table are targeted values and not strict requirements.
Note 6: Performance requirements for all the values in this table should be analyzed independently for each scenario.
* * * * End of Changes * * * *
|
S1-242093.zip
|
2026-01-13T17:04:53.915535
|
S1-242223
|
SA1
|
TSGS1_107_Maastricht
|
CR
|
noted
|
Quality improvement contributions
|
3GPP TSG-SA1 Meeting #107 S1-242223
Maastricht, The Netherlands, 19-23 August (revision of S1-24xxxx)
CR-Form-v12.1
CHANGE REQUEST
22.261
CR
0807
rev
-
Current version:
19.7.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
Radio Access Network
Core Network
Title:
Quality improvement – align the terms 5G and 3GPP
Source to WG:
Huawei
Source to TSG:
SA1
Work item code:
SMARTER_Ph2, TEI19
Date:
2024-08-09
Category:
F
Release:
Rel-19
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
The service requirements in TS 22.261 are intended for the 5G system/network. Therefore, the term 5G system/network shall be used in the context of service requirements.
Summary of change:
Replaced the term 3GPP system with 5G system in the following clauses:
- 6.9 Connectivity models
- 6.10 Network capability exposure
- 6.20 eV2X aspects
- 6.24 Ethernet transport services
- 6.31 Minimization of Service Interruption
- 6.32 UAV aspects
Consequences if not approved:
Ambiguity will remain with the mixed usages of these seemingly similar terms and phrases.
Clauses affected:
6.9.2.4, 6.10.2, 6.20, 6.24.2, 6.31, 6.32
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
--- CHANGE #1 ---
6.9.2.4 Relay UE Selection
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall support selection and reselection of relay UEs based on a combination of different criteria e.g.
- the characteristics of the traffic that is intended to be relayed (e.g. expected message frequency and required QoS),
- the subscriptions of relay UEs and remote UE,
- the capabilities/capacity/coverage when using the relay UE,
- the QoS that is achievable by selecting the relay UE,
- the power consumption required by relay UE and remote UE,
- the pre-paired [[SUGGESTION_START]]relay[[SUGGESTION_END]] UE,
- the 3GPP or non-3GPP access the relay UE uses to connect to the network,
- the 3GPP network the relay UE connects to (either directly or indirectly),
- the overall optimization of the power consumption/performance of the 3GPP system, or
- battery capabilities and battery lifetime of the relay UE and the remote UE.
NOTE: Reselection may be triggered by any dynamic change in the selection criteria, e.g. by the battery of a relay UE getting depleted, a new relay capable UE getting in range, a remote UEs requesting additional resources or higher QoS, etc.
--- CHANGE #2 ---
6.10.2 Requirements
The following set of requirements complement the requirements listed in 3GPP TS 22.101 [6], clause 29.
Based on operator policy, a 5G network shall provide suitable APIs to allow a trusted third-party to create, modify, and delete network slices used for the third-party.
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party to monitor the network slice used for the third-party.
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party to define and update the set of services and capabilities supported in a network slice used for the third-party.
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party to configure the information which associates a UE to a network slice used for the third-party.
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party to configure the information which associates a service to a network slice used for the third-party.
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party to assign a UE to a network slice used for the third-party, to move a UE from one network slice used for the third-party to another network slice used for the third-party, and to remove a UE from a network slice used for the third-party based on subscription, UE capabilities, and services provided by the network slice.
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]network shall be able to provide suitable and secure means to enable an authorized third-party to provide the [[SUGGESTION_START]]5G [[SUGGESTION_END]]network via encrypted connection with the expected communication behaviour of UE(s).
NOTE 1: The expected communication behaviour is, for instance, the application servers a UE is allowed to communicate with, the time a UE is allowed to communicate, or the allowed geographic area of a UE.
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]network shall be able to provide suitable and secure means to enable an authorized third-party to provide via encrypted connection the [[SUGGESTION_START]]5G [[SUGGESTION_END]]network with the actions expected from the [[SUGGESTION_START]]5G [[SUGGESTION_END]]network when detecting behaviour that falls outside the expected communication behaviour.
NOTE 2: Such actions can be, for instance, to terminate the UE's communication, to block the transferred data between the UE and the not allowed application.
The 5G network shall be able to provide secure means for providing communication scheduling information (i.e. the time period the UE(s) will use a communication service) to an NPN via encrypted connection. This communication scheduling information is used by the 5G network to perform network energy saving and network resource optimization.
The 5G network shall provide a mechanism to expose broadcasting capabilities to trusted third-party broadcasters' management systems.
Based on operator policy, a 5G network shall provide suitable APIs to allow a trusted third-party to manage this trusted third-party owned application(s) in the operator's Service Hosting Environment.
Based on operator policy, the 5G network shall provide suitable APIs to allow a third-party to monitor this trusted third-party owned application(s) in the operator's Service Hosting Environment.
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party to scale a network slice used for the third-party, i.e. to adapt its capacity.
Based on operator policy, a 5G network shall provide suitable APIs to allow one type of traffic (from trusted third-party owned applications in the operator's Service Hosting Environment) to/from a UE to be offloaded to a Service Hosting Environment close to the UE's location.
Based on operator policy, the 5G network shall provide suitable APIs to allow a trusted third-party application to request appropriate QoE from the network.
Based on operator policy, the 5G network shall expose a suitable API to an authorized third-party to provide the information regarding the availability status of a geographic location that is associated with that third-party.
Based on operator policy, the 5G network shall expose a suitable API to allow an authorized third-party to monitor the resource utilisation of the network service (radio access point and the transport network (front, backhaul)) that are associated with the third-party.
Based on operator policy, the 5G network shall expose a suitable API to allow an authorized third-party to define and reconfigure the properties of the communication services offered to the third-party.
The 5G system shall support the means for disengagement (tear down) of communication services by an authorized third-party.
Based on operator policy, the 5G network shall expose a suitable API to provide the security logging information of UEs, for example, the active 3GPP security mechanisms (e.g. data privacy, authentication, integrity protection) to an authorized third-party.
Based on operator policy, the 5G system shall provide suitable means to allow a trusted and authorized third-party to consult security related logging information for the network slices dedicated to that third-party.
Based on operator policy, the 5G network shall be able to acknowledge within 100 ms a communication service request from an authorized third-party via a suitable API.
The 5G network shall provide suitable APIs to allow a trusted third-party to monitor the status (e.g. locations, lifecycle, registration status) of its own UEs.
NOTE 3: The number of UEs could be in the range from single digit to tens of thousands.
The 5G network shall provide suitable APIs to allow a trusted third-party to get the network status information of a private slice dedicated for the third-party, e.g. the network communication status between the slice and a specific UE.
The 5G system shall support APIs to allow the non-public network to be managed by the MNO's Operations System.
The 5G system shall provide suitable APIs to allow third-party infrastructure (i.e. physical/virtual network entities at RAN/core level) to be used in a private slice.
A 5G system shall provide suitable APIs to enable a third-party to manage its own non-public network and its private slice(s) in the PLMN in a combined manner.
The 5G system shall support suitable APIs to allow an MNO to offer automatic configuration services (for instance, interference management) to non-public networks deployed by third parties and connected to the MNO's Operations System through standardized interfaces.
The 5G system shall be able to:
- provide a third-party with secure access to APIs (e.g. triggered by an application that is visible to the 5G system), by authenticating and authorizing both the third-party and the UE using the third-party's service.
- provide a UE with secure access to APIs (e.g. triggered by an application that is not visible to the 5G system), by authenticating and authorizing the UE.
- allow the UE to provide/revoke consent for information (e.g., location, presence) to be shared with the third-party.
- preserve the confidentiality of the UE's external identity (e.g. MSISDN) against the third-party.
- provide a third-party with information to identify networks and APIs on those networks.
Based on operator policy, the 5G system shall provide means by which an MNO informs a third party of changes in UE subscription information. The 5G system shall also provide a means for an authorised third party to request this information at any time from the MNO.
NOTE 4: Examples of UE subscription information include IP address, 5G LAN-VN membership, and configuration parameters for data network access.
NOTE 5: These changes can have strong impacts in the stability of the third-party service.
The 5G system shall provide a means by which an MNO can inform authorised 3rd parties of changes in the
- RAT type that is serving a UE;
- cell ID;
- RAN quality of signal information;
- assigned frequency band.
This information listed above shall be provided with a suitable frequency via OAM and/or 5G core network.
NOTE 6: The information aids the third party user to take proactive actions so that it can achieve high service availability in delivery of its services.
--- CHANGE #3 ---
6.20 eV2X aspects
6.20.1 Description
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system is expected to support various enhanced V2X scenarios.
Vehicles Platooning enables the vehicles to dynamically form a group travelling together. All the vehicles in the platoon receive periodic data from the leading vehicle, in order to carry on platoon operations. This information allows the distance between vehicles to become extremely small, i.e. the gap distance translated to time can be very low (sub second). Platooning applications can allow the vehicles following to be autonomously driven.
Advanced Driving enables semi-automated or fully-automated driving. Longer inter-vehicle distance is assumed. Each vehicle and/or RSU shares data obtained from its local sensors with vehicles in proximity, thus allowing vehicles to coordinate their trajectories or manoeuvres. In addition, each vehicle shares its driving intention with vehicles in proximity. The benefits of this use case group are safer traveling, collision avoidance, and improved traffic efficiency.
Extended Sensors enables the exchange of raw or processed data gathered through local sensors or live video data among vehicles, Road Site Units, UEs of pedestrians and V2X application servers. The vehicles can enhance the perception of their environment beyond what their own sensors can detect and have a more holistic view of the local situation.
Remote Driving enables a remote driver or a V2X application to operate a remote vehicle for those passengers who cannot drive themselves or a remote vehicle located in dangerous environments. For a case where variation is limited and routes are predictable, such as public transportation, driving based on cloud computing can be used. In addition, access to cloud-based back-end service platform can be considered for this use case group.
6.20.2 Requirements
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system supports the transport of messages with different performance requirements to support V2X scenarios. The associated requirements are described in eV2X 3GPP TS 22.186 [9].
--- CHANGE #4 ---
6.24.2 Requirements
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall be able to support an Ethernet transport service.
The 5G network shall support the routing of non-IP packet (e.g. Ethernet frame) efficiently for private communication between UEs within a 5G LAN-type service.
The 5G network shall be able to provide the required QoS (e.g. reliability, latency, and bandwidth) for non-IP packet (e.g. Ethernet frame) for private communication between UEs within a 5G LAN-type service.
The Ethernet transport service shall support routing based on information extracted from Virtual LAN (VLAN) ID by the [[SUGGESTION_START]]5G [[SUGGESTION_END]]system.
The Ethernet transport service shall support the transport of Ethernet frames between UEs that Ethernet devices are connected to.
The Ethernet transport service shall support the transport of Ethernet frames between a UE that an Ethernet device is connected to and an Ethernet network in DN (Data Network).
NOTE: If more than one Ethernet devices need to be connected to a UE, they can be connected using an Ethernet switch between the devices and the UE.
The Ethernet transport service shall support the transport of Ethernet broadcast frames.
The Ethernet transport service shall support traffic filtering and prioritization based on source and destination MAC addresses.
The Ethernet transport service shall support traffic filtering and prioritization based on Ethertype (including multiple Ethertypes in double tagging).
The Ethernet transport service shall support traffic filtering and prioritization based on IEEE 802.1Q VLAN tags (including double tagging).
The Ethernet transport service shall support routing based on information extracted by the [[SUGGESTION_START]]5G [[SUGGESTION_END]]system from the Bridge Protocol Data Units created in the Ethernet network based on a Spanning Tree Protocol (e.g. RSTP, MSTP [54]).
--- CHANGE #5 ---
6.31 Minimization of Service Interruption
6.31.1 Description
A mobile network can fail to provide service in the event of a disaster (for example a fire.) The requirements listed in this clause provide the 5GS with the capability to mitigate interruption of service. UEs can obtain service in the event of a disaster, if there are PLMN operators prepared to offer service. The minimization of service interruption is constrained to a particular time and place. To reduce the impact to the 5G System and [[SUGGESTION_START]]the [[SUGGESTION_END]]EPS of supporting Disaster Roaming, the potential congestion resulting from an influx or outflux of Disaster Inbound Roamers is taken into account.
Scenarios where network failures render the network subject to a disaster unable to authenticate its subscribers are excluded.
6.31.2 Requirements
6.31.2.1 General
Subject to regulatory requirements or operator's policy, [[SUGGESTION_START]]the [[SUGGESTION_END]][[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall be able to enable a UE of a given PLMN to obtain connectivity service (e.g. voice call, mobile data service) from another PLMN for the area where a Disaster Condition applies.
Subject to regulatory requirements, operator's policy or UE capabilities, the [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall be able to support a UE, with 5G-only national roaming access to a VPLMN, to obtain 4G connectivity service (e.g. voice call, mobile data service) from that VPLMN in the area where a Disaster Condition applies.
Subject to regulatory requirements or operator's policy, in case of shared RAN between participating PLMNs, the [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall be able to support a UE of a given PLMN to obtain connectivity service (e.g. voice call, mobile data service) from another participating network when a Disaster Condition applies to the UE’s PLMN.
6.31.2.2 Disaster Condition
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall enable UEs to obtain information that a Disaster Condition applies to a particular PLMN or PLMNs.
NOTE: If a UE has no coverage of its HPLMN, then obtains information that a Disaster Condition applies to the UE's HPLMN, the UE can register with a PLMN offering Disaster Roaming service.
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall support means for a PLMN operator to be aware of the area where Disaster Condition applies.
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall be able to support provision of service to Disaster Inbound Roamer only within the specific region where Disaster Condition applies.
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall be able to provide efficient means for a network to inform Disaster Inbound roamers that a Disaster Condition is no longer applicable.
Subject to regulatory requirements or operator’s policy, the [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall support a PLMN operator to be made aware of the failure or recovery of other PLMN(s) in the same country when the Disaster Condition is applies, or when the Disaster Condition is not applicable.
6.31.2.3 Disaster Roaming
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall be able to provide means to enable a UE to access PLMNs in a forbidden PLMN list if a Disaster condition applies and no other PLMN is available except for PLMNs in the forbidden PLMN list.
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall provide means to enable that a Disaster Condition applies to UEs of a specific PLMN.
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall be able to provide a resource efficient means for a PLMN to indicate to potential Disaster Inbound Roamers whether they can access the PLMN or not.
Disaster Inbound Roamers shall perform network reselection when a Disaster Condition has ended.
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall minimize congestion caused by Disaster Roaming.
The 5G system and [[SUGGESTION_START]]the [[SUGGESTION_END]]EPS shall support a mechanism for the HPLMN to control whether a UE, with HPLMN subscription, should apply Disaster Roaming when a Disaster Condition arises (in the HPLMN or a VPLMN).
[[SUGGESTION_START]]The [[SUGGESTION_END]][[SUGGESTION_START]]5G [[SUGGESTION_END]]system shall be able to collect charging information for a Disaster Inbound Roamer with information about the applied disaster condition.
--- CHANGE #6 ---
6.32 UAV aspects
6.32.1 Description
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system is expected to support various enhanced UAV scenarios, especially for a wide range of applications and scenarios by using low altitude UAVs in various commercial and government sectors.
6.32.2 Requirements
The [[SUGGESTION_START]]5G [[SUGGESTION_END]]system supports service requirements and KPIs related to command and control (C2), payload (e.g. camera) and the operation of radio access nodes on-board of UAVs. The associated requirements are described in 3GPP TS 22.125 [26].
|
S1-242223.zip
|
2026-01-13T17:05:21.913652
|
S1-242226
|
SA1
|
TSGS1_107_Maastricht
|
CR
|
noted
|
Quality improvement contributions
|
3GPP TSG-SA1 Meeting #107 S1-242226
Maastricht, The Netherlands, 19-23 August (revision of S1-24xxxx)
CR-Form-v12.1
CHANGE REQUEST
22.261
CR
0808
rev
-
Current version:
19.7.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
Radio Access Network
Core Network
Title:
Quality improvement – align the terms for service exposure related requirements
Source to WG:
Huawei
Source to TSG:
SA1
Work item code:
SMARTER_Ph2, TEI19
Date:
2024-08-09
Category:
F
Release:
Rel-19
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
There are quite a few service exposure related requirements are captured in TS 22.261, where the terms means and API are both used. The term means is a more general term without pointing to any specific solutions, and should be used instead of the term API.
Summary of change:
Replaced the term API with means in the following clauses:
- 6.1 Network slicing
- 6.9 Connectivity models
- 6.10 Network capability exposure
- 6.26 5G LAN-type service
Consequences if not approved:
Ambiguity will remain with the mixed usages of these seemingly similar terms and phrases.
Clauses affected:
6.1.2.4, 6.9.2.3, 6.10.2, 6.26.2.9
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
--- CHANGE #1 ---
6.1.2.4 Cross-network slice coordination
The 5G system shall support a mechanism to provide time stamps with a common time base at the monitoring API, for services that cross multiple network slices and 5G networks.
The 5G system shall provide suitable [[SUGGESTION_START]]means [[SUGGESTION_END]]to coordinate network slices in multiple 5G networks so that the selected communication services of a non-public network can be extended through a PLMN (e.g. the service is supported by a slice in the non-public network and a slice in the PLMN).
The 5G system shall provide a mechanism to enable an MNO to operate a hosted non-public network and private slice(s) of its PLMN associated with the hosted non-public network in a combined manner.
--- CHANGE #2 ---
6.9.2.3 Permission and Authorization
The 5G system shall enable the network operator to authorize a UE to use indirect network connection. The authorization shall be able to be restricted to using only relay UEs belonging to the same network operator. The authorization shall be able to be restricted to only relay UEs belonging to the same application layer group.
The 5G system shall enable the network operator to authorize a UE to relay traffic as relay UE. The authorization shall be able to allow relaying only for remote UEs belonging to the same network operator. The authorization shall be able to allow relaying only for remote UEs belonging to the same application layer group.
The 5G system shall support a mechanism for an end user to provide/revoke permission to an authorized UE to act as a relay UE.
The 5G system shall support a mechanism for an authorized third-party to provide/revoke permission to an authorized UE to act as a relay UE.
The 5G system shall provide a suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] by which an authorized third-party shall be able to authorize (multiple) UEs under control of the third-party to act as a relay UE or remote UE.
The 5G system shall provide a suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] by which an authorized third-party shall be able to enable/disable (multiple) UEs under control of the third-party to act as a relay UE or remote UE.
--- CHANGE #3 ---
6.10.2 Requirements
The following set of requirements complement the requirements listed in 3GPP TS 22.101 [6], clause 29.
Based on operator policy, a 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to create, modify, and delete network slices used for the third-party.
Based on operator policy, the 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to monitor the network slice used for the third-party.
Based on operator policy, the 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to define and update the set of services and capabilities supported in a network slice used for the third-party.
Based on operator policy, the 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to configure the information which associates a UE to a network slice used for the third-party.
Based on operator policy, the 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to configure the information which associates a service to a network slice used for the third-party.
Based on operator policy, the 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to assign a UE to a network slice used for the third-party, to move a UE from one network slice used for the third-party to another network slice used for the third-party, and to remove a UE from a network slice used for the third-party based on subscription, UE capabilities, and services provided by the network slice.
The 3GPP network shall be able to provide suitable and secure means to enable an authorized third-party to provide the 3GPP network via encrypted connection with the expected communication behaviour of UE(s).
NOTE 1: The expected communication behaviour is, for instance, the application servers a UE is allowed to communicate with, the time a UE is allowed to communicate, or the allowed geographic area of a UE.
The 3GPP network shall be able to provide suitable and secure means to enable an authorized third-party to provide via encrypted connection the 3GPP network with the actions expected from the 3GPP network when detecting behaviour that falls outside the expected communication behaviour.
NOTE 2: Such actions can be, for instance, to terminate the UE's communication, to block the transferred data between the UE and the not allowed application.
The 5G network shall be able to provide secure means for providing communication scheduling information (i.e. the time period the UE(s) will use a communication service) to an NPN via encrypted connection. This communication scheduling information is used by the 5G network to perform network energy saving and network resource optimization.
The 5G network shall provide a mechanism to expose broadcasting capabilities to trusted third-party broadcasters' management systems.
Based on operator policy, a 5G network shall provide [[SUGGESTION_START]]means [[SUGGESTION_END]]to allow a trusted third-party to manage this trusted third-party owned application(s) in the operator's Service Hosting Environment.
Based on operator policy, the 5G network shall provide suitable [[SUGGESTION_START]]means [[SUGGESTION_END]]to allow a third-party to monitor this trusted third-party owned application(s) in the operator's Service Hosting Environment.
Based on operator policy, the 5G network shall provide suitable [[SUGGESTION_START]]means [[SUGGESTION_END]]to allow a trusted third-party to scale a network slice used for the third-party, i.e. to adapt its capacity.
Based on operator policy, a 5G network shall provide suitable [[SUGGESTION_START]]means [[SUGGESTION_END]]to allow one type of traffic (from trusted third-party owned applications in the operator's Service Hosting Environment) to/from a UE to be offloaded to a Service Hosting Environment close to the UE's location.
Based on operator policy, the 5G network shall provide suitable [[SUGGESTION_START]]means [[SUGGESTION_END]]to allow a trusted third-party application to request appropriate QoE from the network.
Based on operator policy, the 5G network shall expose a suitable [[SUGGESTION_START]]means [[SUGGESTION_END]]to an authorized third-party to provide the information regarding the availability status of a geographic location that is associated with that third-party.
Based on operator policy, the 5G network shall expose a suitable [[SUGGESTION_START]]means [[SUGGESTION_END]]to allow an authorized third-party to monitor the resource utilisation of the network service (radio access point and the transport network (front, backhaul)) that are associated with the third-party.
Based on operator policy, the 5G network shall expose a suitable [[SUGGESTION_START]]means [[SUGGESTION_END]]to allow an authorized third-party to define and reconfigure the properties of the communication services offered to the third-party.
The 5G system shall support the means for disengagement (tear down) of communication services by an authorized third-party.
Based on operator policy, the 5G network shall expose a suitable [[SUGGESTION_START]]means [[SUGGESTION_END]]to provide the security logging information of UEs, for example, the active 3GPP security mechanisms (e.g. data privacy, authentication, integrity protection) to an authorized third-party.
Based on operator policy, the 5G system shall provide suitable means to allow a trusted and authorized third-party to consult security related logging information for the network slices dedicated to that third-party.
Based on operator policy, the 5G network shall be able to acknowledge within 100 ms a communication service request from an authorized third-party via a suitable [[SUGGESTION_START]]means[[SUGGESTION_END]].
The 5G network shall provide suitable [[SUGGESTION_START]]means [[SUGGESTION_END]]to allow a trusted third-party to monitor the status (e.g. locations, lifecycle, registration status) of its own UEs.
NOTE 3: The number of UEs could be in the range from single digit to tens of thousands.
The 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to get the network status information of a private slice dedicated for the third-party, e.g. the network communication status between the slice and a specific UE.
The 5G system shall support [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow the non-public network to be managed by the MNO's Operations System.
The 5G system shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow third-party infrastructure (i.e. physical/virtual network entities at RAN/core level) to be used in a private slice.
A 5G system shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to enable a third-party to manage its own non-public network and its private slice(s) in the PLMN in a combined manner.
The 5G system shall support suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow an MNO to offer automatic configuration services (for instance, interference management) to non-public networks deployed by third parties and connected to the MNO's Operations System through standardized interfaces.
The 5G system shall be able to:
- provide a third-party with secure access to APIs (e.g. triggered by an application that is visible to the 5G system), by authenticating and authorizing both the third-party and the UE using the third-party's service.
- provide a UE with secure access to APIs (e.g. triggered by an application that is not visible to the 5G system), by authenticating and authorizing the UE.
- allow the UE to provide/revoke consent for information (e.g., location, presence) to be shared with the third-party.
- preserve the confidentiality of the UE's external identity (e.g. MSISDN) against the third-party.
- provide a third-party with information to identify networks and APIs on those networks.
Based on operator policy, the 5G system shall provide means by which an MNO informs a third party of changes in UE subscription information. The 5G system shall also provide a means for an authorised third party to request this information at any time from the MNO.
NOTE 4: Examples of UE subscription information include IP address, 5G LAN-VN membership, and configuration parameters for data network access.
NOTE 5: These changes can have strong impacts in the stability of the third-party service.
The 5G system shall provide a means by which an MNO can inform authorised 3rd parties of changes in the
- RAT type that is serving a UE;
- cell ID;
- RAN quality of signal information;
- assigned frequency band.
This information listed above shall be provided with a suitable frequency via OAM and/or 5G core network.
NOTE 6: The information aids the third party user to take proactive actions so that it can achieve high service availability in delivery of its services.
--- CHANGE #4 ---
6.26.2.9 Service exposure
Based on MNO policy, the 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to create/remove a 5G LAN-VN.
Based on MNO policy, the 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to manage a 5G LAN-VN dedicated for the usage by the trusted third-party, including the address allocation.
Based on MNO policy, the 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to authorize/deauthorize UEs to access a specific 5G LAN-VN managed by the trusted third-party.
Based on MNO policy, the 5G network shall provide suitable [[SUGGESTION_START]]means[[SUGGESTION_END]] to allow a trusted third-party to add/remove an authorized UE to/from a specific 5G LAN-VN managed by the trusted third-party.
|
S1-242226.zip
|
2026-01-13T17:05:54.391325
|
S1-242248
|
SA1
|
TSGS1_107_Maastricht
|
CR
|
noted
|
Quality improvement contributions
|
3GPP TSG-SA1 Meeting #107 S1-242248
Maastricht, Netherlands, 19th Aug 2024 - 23rd Aug 2024
CR-Form-v12.3
CHANGE REQUEST
22.369
CR
0008
rev
-
Current version:
19.2.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
Radio Access Network
Core Network
Title:
Editorial CR-separate unrelated requirements
Source to WG:
Huawei
Source to TSG:
SA1
Work item code:
AmbientIoT
Date:
2024-08-09
Category:
D
Release:
Rel-19
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
Two separate requirements are captured into one paragraph, which has caused confusion in e.g. SA2 (see Q4 of LS S2-2407231).
Summary of change:
Separate the two requirements editorially to improve readability.
Consequences if not approved:
Confusion remains.
Clauses affected:
5.2.3
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
--- change #1 ---
5.2.3 Management
The 5G network shall support suitable management mechanisms for an Ambient IoT device or a group of Ambient IoT devices.
The 5G system shall support a mechanism to:
- disable the capability to transmit RF signals for one or more Ambient IoT device that is / are currently able to transmit RF signals
- enable the capability to transmit RF signals for one or more Ambient IoT device that is / are currently disabled to transmit RF signals
Based on operator policy, the 5G system shall provide a suitable mechanism to permanently disable the capability of an Ambient IoT device or a group of Ambient IoT devices to transmit RF signals.
Subject to operator policy and regulatory requirements, the 5G system shall support suitable mechanisms for the Ambient IoT device to move between one or more networks and countries.
--- end of change #1 ---
|
S1-242248.zip
|
2026-01-13T17:08:27.589021
|
S1-242337
|
SA1
|
TSGS1_107_Maastricht
|
CR
|
revised
|
Quality improvement contributions
|
3GPP TSG-SA1 Meeting #107 S1-242337
Maastricht, The Netherlands, 19-23 August (revision of S1-242053)
CR-Form-v12.2
CHANGE REQUEST
22.261
CR
0794
rev
1
Current version:
19.7.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
Radio Access Network
Core Network
x
RR
Title:
Editorial correction to the requirements of Indirect Network Sharing
Source to WG:
CATT
Source to TSG:
SA1
Work item code:
TEI19, NetShare
Date:
2024-08-20
Category:
D
Release:
Rel-19
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
When adapt the requirements to TS, the wording of “in case of indirect network sharing” is left in some requirements, which are self-contained as the clause title.
Also, there are some punctuation mark and format errors, which are impacting the reading experience and understanding of the requirements, e.g. missing space between the words, redundant blank lines left.
Summary of change:
This CR intends to correct the editoral errors, including
-- Add one space between “Sharing” and “is”
6.21.2.2 -- remove redundent words.
Consequences if not approved:
Some descriptions and requirements are misunderstood.
Clauses affected:
6.21.1, 6.21.2.2
Y
N
Other specs
Other core specifications
TS/TR ... CR ...
affected:
Test specifications
TS/TR ... CR ...
(show related CRs)
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
rev0, S1-242053 original version
rev1, S1-242377 modify the cover page, remove unchanged sub-clauses.
* * * First Change * * * *
6.21 NG-RAN Sharing
6.21.1 Description
The increased density of access nodes needed to meet future performance objectives poses considerable challenges in deployment and acquiring spectrum and antenna locations. RAN sharing is seen as a technical solution to these issues.
In RAN Sharing operations, NG-RAN resources can be used by multiple network operators. Indirect Network Sharing is one of the possible sharing methods.
During NG-RAN sharing, the security and privacy of shared networks, non-shared networks, and subscribers need to be maintained without negative effects. Especially in the case of Indirect Network Sharing, where the involvement of the core network of the hosting operator e.g. for signalling exchange between the users and the core network of the participating operator could cause exposure of the subscriber’s information to the hosting network, an extra scrutiny of the security mechanism is expected to avoid sharing the information that is not needed for the Indirect Network Sharing operation (e.g. network topology) and protect the information that is needed for the Indirect Network Sharing operation between the hosting operator and the participating operator.
* * * Second Change * * * *
6.21.2.2 Indirect network sharing
The 5G system shall be able to support Indirect Network Sharing between the Shared NG-RAN and one or more Participating NG-RAN Operators’ core networks, by means of the connection being routed through the Hosting NG-RAN Operator’s core network.
NOTE 1: Requirements of Indirect Network Sharing assume no impact on UE.
NOTE 2: For more information on Indirect Network Sharing see Annex I.
Indirect Network Sharing shall be transparent to the user.
NOTE 3: This requirement is aligned with the existing requirement in 3GPP TS 22.101 [6] clause 4.9.
The following existing service requirements related to network sharing in 3GPP TS 22.101 [6] [[SUGGESTION_START]]are [[SUGGESTION_END]]appl[[SUGGESTION_START]]ied[[SUGGESTION_END]][[SUGGESTION_START]] to Indirect Network Sharing[[SUGGESTION_END]]:
- clause 4.2.1,
- clause 28.2.3, and
- clause 28.2.5.
Subject to the agreement between the hosting and participating operator, the 5G system shall support a means to
enable a UE of the Participating NG-RAN Operator to:
- access their subscribed PLMN services when accessing a Shared NG-RAN, and/or,
- obtain its subscribed services, including Hosted Services, of participating operator via a Shared NG-RAN.
Based on operator policy, the 5G system shall support a mechanism to enable an authorized UE with a subscription to a Participating Operator to select and access a Shared NG-RAN.
Based on operator policy, the 5G system shall support access control for an authorized UE accessing a Shared NG-RAN and be able to apply differentiated access control for different Shared NG-RANs when more than one Shared NG-RAN are available for the Participating Operator to choose from.
Based on operator policy, the 5G system shall enable the Participating Operator to provide steering information in order to assist a UE with access network selection amongst the Hosting Operator’s available Shared RAN(s).
The 5G system shall support service continuity for UEs that are moving between different Shared NG-RANs and/or between a Shared NG-RAN and a non-Shared NG-RAN.
The 5G system shall be able to provide a UE accessing a Shared NG-RAN network with positioning service in compliance with regulatory requirements.
Subject to regulatory requirements and mutual agreement between the participating operators and the hosting operator, the requirements to support regulatory services, e.g., PWS or emergency calls apply to Indirect Network Sharing.
[[SUGGESTION_START]]S[[SUGGESTION_END]]ubject to agreement between operators, the 5G system shall enable the Shared NG-RAN of a hosting operator to provide services for inbound roaming users.
The 5G core network shall be able to support collection of charging information associated with a UE accessing a Shared NG-RAN using Indirect Network Sharing, which refers to the resource usage of hosting operator’s core network.
* * * End of Changes * * * *
|
S1-242337.zip
|
2026-01-13T17:08:51.465799
|
S1-242338
|
SA1
|
TSGS1_107_Maastricht
|
CR
|
agreed
|
Quality improvement contributions
|
3GPP TSG-SA1 Meeting #107 S1-242338
Maastricht, The Netherlands, 19-23 August 2024 (revision of S1-242093)
CR-Form-v12.3
CHANGE REQUEST
22.261
CR
0796
rev
1
Current version:
19.7.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
Radio Access Network
Core Network
Title:
Addressing editorial errors
Source to WG:
ZTE
Source to TSG:
SA1
Work item code:
5GSAT_Ph3
Date:
2024-08-08
Category:
D
Release:
Rel-19
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
Several typos were identified.
Summary of change:
Addressing identified editorial errors.
Consequences if not approved:
Less readability of the specification.
Clauses affected:
6.9.1, 6.46.7, 7.4.2
Y
N
Other specs
Other core specifications
TS/TR ... CR ...
affected:
Test specifications
TS/TR ... CR ...
(show related CRs)
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
* * * * Start of 1st Change * * * *
6.9.1 Description
The UE (remote UE) can connect to the network directly (direct network connection), connect using another UE as a relay UE (indirect network connection), or connect using both direct and indirect connections. Relay UEs can be used in many different scenarios and verticals (inHome, SmartFarming, SmartFactories, Public Safety and others). In these cases, the use of relay UEs can be used to improve the energy efficiency and coverage of the system.
Remote UEs can be anything from simple wearables, such as sensors embedded in clothing, to a more sophisticated wearable UE monitoring biometrics. They can also be non-wearable UEs that communicate in a Personal Area Network such as a set of home appliances (e.g. smart thermostat and entry key), or the electronic UEs in an office setting (e.g. smart printers), or a smart flower pot that can be remotely activated to water the plant.
When a remote UE is attempting to establish an indirect network connection, there might be several relay UEs that are available in proximity and supporting selection procedures of an appropriate relay UE among the available relay UEs is needed.
Indirect network connection covers the use of relay UEs for connecting a remote UE to the 3GPP network. There can be one or more relay UE(s) (more than one hop) between the network and the remote UE.
A ProSe UE-to-UE Relay can also be used to connect two remote Public Safety UEs using direct device connection. There can be one or more ProSe UE-to-UE Relay(s) (more than one hop) between the two remote Public Safety UEs.
* * * * Start of 2nd Change * * * *
6.46.7 Satellite and Relay UEs
For a 5G system with satellite access, the following requirements apply:
- A 5G system with satellite access shall be able to support relay UEs with satellite access.
NOTE: The connection between a relay UE and a remote UE is the same regardless of whether the relay UE is using satellite access or not.
- A 5G system with satellite access shall support mobility management of relay UEs and the remote UEs connected to the relay UE between a 5G satellite access network and a 5G terrestrial network, and between 5G satellite access networks.
- A 5G system with satellite access shall support joint roaming between different 5G networks of a relay UE and the remote UEs connected to that relay UE.
* * * * Start of 3rd Change * * * *
7.4.2 Requirements
A 5G system providing service with satellite access shall be able to support GEO based satellite access with up to 285 ms end-to-end latency.
NOTE 1: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system providing service with satellite access shall be able to support MEO based satellite access with up to 95 ms end-to-end latency.
NOTE 2: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system providing service with satellite access shall be able to support LEO based satellite access with up to 35 ms end-to-end latency.
NOTE 3: 5 ms network latency is assumed and added to satellite one-way delay.
A 5G system shall support negotiation on quality of service taking into account latency penalty to optimise the QoE for UE.
The 5G system with satellite access shall support high uplink data rates for 5G satellite UEs.
The 5G system with satellite access shall support high downlink data rates for 5G satellite UEs.
The 5G system with satellite access shall support communication service availabilities of at least 99,99%.
Table 7.4.2-1: Performance requirements for satellite access
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
(note 1)
Area traffic capacity
(UL)
(note 1)
Overall user density
Activity factor
UE speed
UE type
Pedestrian
(note 2)
[1] Mbit/s
[100] kbit/s
1,5 Mbit/s/km2
150 kbit/s/km2
[100]/km2
[1,5] %
Pedestrian
Handheld
Public safety
[3,5] Mbit/s
[3,5] Mbit/s
TBD
TBD
TBD
N/A
100 km/h
Handheld
Vehicular connectivity
(note 3)
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
50 %
Up to 250 km/h
Vehicle mounted
Airplanes connectivity
(note 4)
360 Mbit/s/ plane
180 Mbit/s/ plane
TBD
TBD
TBD
N/A
Up to 1000 km/h
Airplane mounted
Stationary
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
N/A
Stationary
Building mounted
Video surveillance
(note 4a)
[0,5] Mbit/s
[3] Mbit/s
TBD
TBD
TBD
N/A
Up to 120km/h or
stationary
(note 4b)
Vehicle mounted or fixed installation
Narrowband IoT connectivity
[2] kbit/s
[10] kbit/s
8 kbit/s/km2
40 kbit/s/km2
[400]/[[SUGGESTION_START]]km2[[SUGGESTION_END]]
[1] %
[Up to 100 km/h]
IoT
Note 1: Area capacity is averaged over a satellite beam.
Note 2: Data rates based on Extreme long-range coverage target values in clause 6.17.2. User density based on rural area in Table 7.1-1.
Note 3: Based on Table 7.1-1
Note 4: Based on an assumption of 120 users per plane 15/7.5 Mbit/s data rate and 20 % activity factor per user
Note 4a: Refer to video surveillance data transmitted (in UL) from a UE on the ground (e.g. picture or video from a camera) using satellite NG-RAN to connect to 5GC, and video surveillance-related configuration or control data sent (in DL) to the UE/device. 0.5 Mbit/s for DL experienced data rate is based on MAVLINK protocol that is widely used for UAV control. 3 Mbit/s for UL experienced data rate is based on the assumed sum from 2.5 Mbit/s for video streaming and 0.5 Mbit/s for data transmission.
Note 4b: Up to 120km/h applies to vehicle mounted while stationary applies to fixed installation.
Note 5: All the values in this table are targeted values and not strict requirements.
Note 6: Performance requirements for all the values in this table should be analyzed independently for each scenario.
* * * * End of Changes * * * *
|
S1-242338.zip
|
2026-01-13T17:09:17.401644
|
S1-242533
|
SA1
|
TSGS1_107_Maastricht
|
CR
|
revised
|
Quality improvement contributions
|
3GPP TSG-SA1 Meeting #107 S1-242533
Maastricht, The Netherlands, 19-23 August (revision of S1-242053,2337)
CR-Form-v12.2
CHANGE REQUEST
22.261
CR
0794
rev
2
Current version:
19.7.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
Radio Access Network
Core Network
x
RR
Title:
Editorial correction to the requirements of Indirect Network Sharing
Source to WG:
CATT
Source to TSG:
SA1
Work item code:
TEI19, NetShare
Date:
2024-08-20
Category:
D
Release:
Rel-19
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
When adapt the requirements to TS, the wording of “in case of indirect network sharing” is left in some requirements, which are self-contained as the clause title.
Also, there are some punctuation mark and format errors, which are impacting the reading experience and understanding of the requirements, e.g. missing space between the words, redundant blank lines left.
Summary of change:
This CR intends to correct the editoral errors, including
-- Add one space between “Sharing” and “is”
6.21.2.2 -- remove redundent words.
Consequences if not approved:
Some descriptions and requirements are misunderstood.
Clauses affected:
6.21.1, 6.21.2.2
Y
N
Other specs
x
Other core specifications
TS/TR ... CR ...
affected:
x
Test specifications
TS/TR ... CR ...
(show related CRs)
x
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
* * * First Change * * * *
6.21 NG-RAN Sharing
6.21.1 Description
The increased density of access nodes needed to meet future performance objectives poses considerable challenges in deployment and acquiring spectrum and antenna locations. RAN sharing is seen as a technical solution to these issues.
In RAN Sharing operations, NG-RAN resources can be used by multiple network operators. Indirect Network Sharing is one of the possible sharing methods.
During NG-RAN sharing, the security and privacy of shared networks, non-shared networks, and subscribers need to be maintained without negative effects. Especially in the case of Indirect Network Sharing, where the involvement of the core network of the hosting operator e.g. for signalling exchange between the users and the core network of the participating operator could cause exposure of the subscriber’s information to the hosting network, an extra scrutiny of the security mechanism is expected to avoid sharing the information that is not needed for the Indirect Network Sharing operation (e.g. network topology) and protect the information that is needed for the Indirect Network Sharing operation between the hosting operator and the participating operator.
* * * Second Change * * * *
6.21.2.2 Indirect network sharing
The 5G system shall be able to support Indirect Network Sharing between the Shared NG-RAN and one or more Participating NG-RAN Operators’ core networks, by means of the connection being routed through the Hosting NG-RAN Operator’s core network.
NOTE 1: Requirements of Indirect Network Sharing assume no impact on UE.
NOTE 2: For more information on Indirect Network Sharing see Annex I.
Indirect Network Sharing shall be transparent to the user.
NOTE 3: This requirement is aligned with the existing requirement in 3GPP TS 22.101 [6] clause 4.9.
The following existing service requirements related to network sharing in 3GPP TS 22.101 [6] [[SUGGESTION_START]]are [[SUGGESTION_END]]appl[[SUGGESTION_START]]ied[[SUGGESTION_END]][[SUGGESTION_START]] to Indirect Network Sharing[[SUGGESTION_END]]:
- clause 4.2.1,
- clause 28.2.3, and
- clause 28.2.5.
Subject to the agreement between the hosting and participating operator, the 5G system shall support a means to
enable a UE of the Participating NG-RAN Operator to:
- access their subscribed PLMN services when accessing a Shared NG-RAN, and/or,
- obtain its subscribed services, including Hosted Services, of participating operator via a Shared NG-RAN.
Based on operator policy, the 5G system shall support a mechanism to enable an authorized UE with a subscription to a Participating Operator to select and access a Shared NG-RAN.
Based on operator policy, the 5G system shall support access control for an authorized UE accessing a Shared NG-RAN and be able to apply differentiated access control for different Shared NG-RANs when more than one Shared NG-RAN are available for the Participating Operator to choose from.
Based on operator policy, the 5G system shall enable the Participating Operator to provide steering information in order to assist a UE with access network selection amongst the Hosting Operator’s available Shared RAN(s).
The 5G system shall support service continuity for UEs that are moving between different Shared NG-RANs and/or between a Shared NG-RAN and a non-Shared NG-RAN.
The 5G system shall be able to provide a UE accessing a Shared NG-RAN network with positioning service in compliance with regulatory requirements.
Subject to regulatory requirements and mutual agreement between the participating operators and the hosting operator, the requirements to support regulatory services, e.g., PWS or emergency calls apply to Indirect Network Sharing.
[[SUGGESTION_START]]S[[SUGGESTION_END]]ubject to agreement between operators, the 5G system shall enable the Shared NG-RAN of a hosting operator to provide services for inbound roaming users.
The 5G core network shall be able to support collection of charging information associated with a UE accessing a Shared NG-RAN using Indirect Network Sharing, which refers to the resource usage of hosting operator’s core network.
* * * End of Changes * * * *
|
S1-242533.zip
|
2026-01-13T17:09:43.511021
|
S1-242549
|
SA1
|
TSGS1_107_Maastricht
|
CR
|
agreed
|
Quality improvement contributions
|
3GPP TSG-SA1 Meeting #107 S1-242549
Maastricht, The Netherlands, 19-23 August (revision of S1-242053, 2337, 2533)
CR-Form-v12.2
CHANGE REQUEST
22.261
CR
0794
rev
3
Current version:
19.7.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
Radio Access Network
Core Network
x
RR
Title:
Correction to the requirements of Indirect Network Sharing
Source to WG:
CATT
Source to TSG:
SA1
Work item code:
TEI19, NetShare
Date:
2024-08-23
Category:
F
Release:
Rel-19
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
When adapt the requirements to TS, the wording of “in case of indirect network sharing” is left in some requirements, which are self-contained as the clause title.
Also, there are some punctuation mark and format errors, which are impacting the reading experience and understanding of the requirements, e.g. missing space between the words, redundant blank lines left.
Summary of change:
This CR intends to correct the editoral errors, including
-- Add one space between “Sharing” and “is”
6.21.2.2 -- remove redundent words.
Consequences if not approved:
Some descriptions and requirements are misunderstood.
Clauses affected:
6.21.1, 6.21.2.2
Y
N
Other specs
x
Other core specifications
TS/TR ... CR ...
affected:
x
Test specifications
TS/TR ... CR ...
(show related CRs)
x
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
* * * First Change * * * *
6.21 NG-RAN Sharing
6.21.1 Description
The increased density of access nodes needed to meet future performance objectives poses considerable challenges in deployment and acquiring spectrum and antenna locations. RAN sharing is seen as a technical solution to these issues.
In RAN Sharing operations, NG-RAN resources can be used by multiple network operators. Indirect Network Sharing is one of the possible sharing methods.
During NG-RAN sharing, the security and privacy of shared networks, non-shared networks, and subscribers need to be maintained without negative effects. Especially in the case of Indirect Network Sharing, where the involvement of the core network of the hosting operator e.g. for signalling exchange between the users and the core network of the participating operator could cause exposure of the subscriber’s information to the hosting network, an extra scrutiny of the security mechanism is expected to avoid sharing the information that is not needed for the Indirect Network Sharing operation (e.g. network topology) and protect the information that is needed for the Indirect Network Sharing operation between the hosting operator and the participating operator.
* * * Second Change * * * *
6.21.2.2 Indirect network sharing
The 5G system shall be able to support Indirect Network Sharing between the Shared NG-RAN and one or more Participating NG-RAN Operators’ core networks, by means of the connection being routed through the Hosting NG-RAN Operator’s core network.
NOTE 1: Requirements of Indirect Network Sharing assume no impact on UE.
NOTE 2: For more information on Indirect Network Sharing see Annex I.
Indirect Network Sharing shall be transparent to the user.
NOTE 3: This requirement is aligned with the existing requirement in 3GPP TS 22.101 [6] clause 4.9.
The following existing service requirements related to network sharing in 3GPP TS 22.101 [6] [[SUGGESTION_START]]are [[SUGGESTION_END]]appl[[SUGGESTION_START]]ied[[SUGGESTION_END]][[SUGGESTION_START]] to Indirect Network Sharing[[SUGGESTION_END]]:
- clause 4.2.1,
- clause 28.2.3, and
- clause 28.2.5.
Subject to the agreement between the hosting and participating operator, the 5G system shall support a means to
enable a UE of the Participating NG-RAN Operator to:
- access their subscribed PLMN services when accessing a Shared NG-RAN, and/or,
- obtain its subscribed services, including Hosted Services, of participating operator via a Shared NG-RAN.
Based on operator policy, the 5G system shall support a mechanism to enable an authorized UE with a subscription to a Participating Operator to select and access a Shared NG-RAN.
Based on operator policy, the 5G system shall support access control for an authorized UE accessing a Shared NG-RAN and be able to apply differentiated access control for different Shared NG-RANs when more than one Shared NG-RAN are available for the Participating Operator to choose from.
Based on operator policy, the 5G system shall enable the Participating Operator to provide steering information in order to assist a UE with access network selection amongst the Hosting Operator’s available Shared RAN(s).
The 5G system shall support service continuity for UEs that are moving between different Shared NG-RANs and/or between a Shared NG-RAN and a non-Shared NG-RAN.
The 5G system shall be able to provide a UE accessing a Shared NG-RAN network with positioning service in compliance with regulatory requirements.
Subject to regulatory requirements and mutual agreement between the participating operators and the hosting operator, the requirements to support regulatory services, e.g., PWS or emergency calls apply to Indirect Network Sharing.
[[SUGGESTION_START]]S[[SUGGESTION_END]]ubject to agreement between operators, the 5G system shall enable the Shared NG-RAN of a hosting operator to provide services for inbound roaming users.
The 5G core network shall be able to support collection of charging information associated with a UE accessing a Shared NG-RAN using Indirect Network Sharing, which refers to the resource usage of hosting operator’s core network.
* * * End of Changes * * * *
|
S1-242549.zip
|
2026-01-13T17:10:11.442412
|
S1-242041
|
SA1
|
TSGS1_107_Maastricht
|
pCR
|
noted
|
FS_5GSAT_Ph4
|
3GPP TSG SA WG 1 Meeting #107 S1-242041
Maastricht, The Netherlands, 19-23 August 2024 (revision of S1-24xxxx)
Source: IPLOOK
pCR Title: Pseudo-CR: Use Case on Resilient Notification and Pre-notification to OTA update
Draft Spec: 3GPP TR 22.887 v0.1.0
Agenda item: 7.3
Document for: Approval
Contact: Junle Liao liao.junle@iplook.com
Abstract: This contribution proposes a use case of satellite access applied in vehicle OTA update Resilient Notification Service, Pre-notification Service, and potential service requirements for TR 22.887.
1. Introduction
To ensure the continuity and reachability of the vehicular communication such as OTA update in the satellite access system, this use case proposes to consider providing Resilient Notification Service and Pre-notification Service as requirements.
2. Reason for Change
Introduce a use case about Resilient Notification Service and Pre-notification Service with satellite access only applied in vehicular communication to FS_5GSAT_Ph4.
3. Conclusions
<Conclusion part (optional)>
4. Proposal
It is proposed that the following changes to 3GPP TR 22.887 v0.1.0 be agreed upon.
* * * First Change * * * *
5 Use cases
5.X Use case on Resilient Notification and Pre-notification to OTA update
5.X.1 Description
OTA (Over-the-Air) technology is being used to provide new firmware updates to Electric Vehicles (EVs). OTA updates improve the driving experience and safety. Based on V2N (Vehicle-to-Network) technology, EVs can seamlessly connect to mobile networks such as 5G. In urban areas, terrestrial networks have a good coverage of vehicles. Thus, those vehicles in urban can receive OTA update notifications and finish downloading through terrestrial network access. However, for those vehicles in rural or remote areas, the only option is satellite access. Due to incomplete coverage and satellite mobility (LEO, MEO), EVs may miss OTA update notifications when satellite access is unavailable in remote areas. In the update process, they may suspend or terminate downloading due to the satellite coverage gap. Therefore, to ensure the continuity of the OTA update download and reachability of OTA update notification in the satellite access system, it requires “Pre-notification Service” and “Resilient Notification Service”. “Pre-notification Service” is to pre-notify EVs before the vehicle's terminal moves out of the satellite coverage so EVs can store the data before disconnection. With “Resilient Notification Service”, EVs can receive missed OTA notifications when satellite access is reconnected.
5.X.2 Pre-conditions
Satellites (SAT#1, SAT#2, SAT#N) were deployed by the same satellite operator SATNET.
An Electric Vehicle (EV#1) is capable of LEO and MEO satellite access. EV#1 has deployed OTA service. The operator SATNET has deployed the Resilient Notification Service and the Pre-Notification Service for satellite access vehicles.
The network is capable of calculating satellite coverage information of EV#1 to recognize when is the leaving time of EV#1 approximately.
5.X.3 Service Flows
EV#1 is driving to a rural area with satellite coverage. The rural area now is in winter and the control center plans to propose an OTA update for energy optimization in cold regions. Based on the location information and the low-temperature warning sent from EV#1’s sensors, update lists include EV#1.
The control center proposes an OTA update notification. However, EV#1 entered a satellite coverage gap that cannot receive any OTA notification. The network activates the “Resilient Notification Service” to ensure the OTA update notification can reach EV#1 eventually after the next satellite coverage. Then EV#1 is reconnected to the next satellite and starts to process the OTA update as Fig 5.X.3-1 shows.
When EV#1 starts to download the OTA update data, the satellite may send a pre-notification to EV#1 if EV#1 is about to leave the satellite coverage area. This may allow EV#1 to process the highest priority tasks and back up the download session to ensure service continuity as Fig 5.X.3-2 shows. Then EV#1 suspends the OTA update session and continues downloading when the next satellite reconnected.
Fig 5.X.3-1 Resilient Notification Service Ensure Reachability of OTA Update Notification
Fig 5.X.3-2 Pre-notification Service Ensure Continuity of OTA Update Process
5.X.4 Post-conditions
EV#1 did not miss any OTA update notifications and finished the OTA update steadily in rural areas. The driver was happy that the car updated immediately to respond to different road environments.
5.X.5 Existing features partly or fully covering the use case functionality
·The 3GPP system shall allow UEs supporting V2X applications to use NR for direct communication when the UEs are not served by a RAN using NR. [Reference: TS 22.186]
·The 5G system shall support a mechanism for a 3rd party application to request resilient timing with specific KPIs (e.g., accuracy, interval, coverage area). [Reference: TS 22.261]
5.X.6 Potential New Requirements needed to support the use case
[PR 5.X.6-1] Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall support Resilient Notification Service to notify the vehicle terminal of a missed notification when the vehicle terminal is unreachable via satellite access.
[PR 5.X.6-2] Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall support Pre-notification Service to pre-notify the vehicle terminal of satellite coverage leaving time before the vehicle is driving to the edge of coverage.
[PR 5.X.6-3] Subject to regulatory requirements and operator’s policy, a 5G system with satellite access shall provide a secure mechanism for OTA updates.
|
S1-242041.zip
|
2026-01-13T17:17:38.319952
|
S1-242063
|
SA1
|
TSGS1_107_Maastricht
|
pCR
|
noted
|
FS_5GSAT_Ph4
|
3GPP TSG-SA WG1 Meeting #107 S1-242063
Maastricht, Netherlands, 19-23 August 2024
Title: Enhanced PWS in satellite access network
Agenda Item: 7.3
Source: SES, Novamint, Thales, TNO
Contact: joel.grotz@SES.com, abbas.karaki@SES.com
Abstract: This document proposes a use case on Enhanced Support for Emergency Communications with Satellite Access System and potential requirements for TR22.887 v0.1.0 (FS_5GSAT_ph4)
x.1 Enhanced PWS in satellite access network
x.1.1 Description
Satellite-based 5G serves as a crucial backup communication option during natural disasters and emergencies when terrestrial networks may be damaged or overwhelmed. This ensures that essential communication and services remain available when they are needed most.
To alert people about any emergency, the message alert shall be sent in real-time within seconds and with a high degree of reliability and the message shall reach a high percentage of people in the targeted area, not just residents but roaming visitors using their native language.
As of today, some countries are still using Location-Based SMS to alert the population of an emergency. This kind of system deliver data in a unicast channel which brings some disadvantages as:
Longer delivery times of warning messages (several hours) to groups of people in the geo-target area.
Limitation in terms of scalability since the resource allocation for SMS is not well optimized to address a huge number of people.
Satellite-based 5G excels at broadcasting data and content over wide geographic areas, making it valuable for delivering consistent information to large audiences. This capability is beneficial for applications like emergency alerts where it enables high scalability and real-time for alert delivery, allowing to reach people in isolated area outside the coverage of the Terrestrial Network.
Figure 5.x.1-1 Extending the Broadcast area outside of the originating country for Emergency Communications using Satellite Access
x.1.2 Pre-conditions
Alice is a cross-border worker, living on the French border and taking the A3 freeway to her workplace in Luxembourg every morning. Half of Luxembourg workers are in the similar situation as Alice (cross-border workers).
The terrestrial network on the border is very weak and almost unavailable, so satellite access is the alternative. When Alice is home, she sets in her phone as default network access the satellite access.
Alice has a business phone with an operator in Luxembourg. After her subscription, (following the EU rules, article 294 [X1]) Alice received automatically an SMS to inform her about the existence of a Public Warning System and easily understandable information on how to receive public warnings.
Following the received instructions, Alice enters in the settings of her professional phone and subscribes to the “EU-Alert” for “Extreme Threats” communication channels and “Severe Threats” communication channels.
It is December, the weather conditions are severe with a very high risk of black ice on the roads, including the A3 freeway.
As a precautionary measure, and in order to protect the lives of the population, the emergency services of the Luxembourg government are sending out a warning “Severe Threats” very late in the evening that people are strongly advised to stay at home and off the roads due to a very high risk of icy conditions.
x.1.3 Service Flows
Since this can concern cross-border workers, Luxembourg authorities in accordance with neighbour countries authorities has defined the broadcast area of this “Severe Threats” to some extent outside of the Luxembourg geographical frontier.
Alice receives the “Severe Threats” alert via the satellite access network, while she is in the France frontier.
x.1.4 Post-conditions
The network operator has successfully mapped the broadcast area for the alert in some region outside of the country to reach cross-border worker.
The network operator has made an agreement with a satellite network operator to expand the broadcast area for the alert outside of the country.
The alert message was delivered reliably to all users subscribed to the “Severe Threats” communication channel through the terrestrial access and the satellite access.
x.1.5 Existing features partly or fully covering the use case functionality
TS 22.268 [X2] which is providing the service requirements for PWS does not include dedicated requirements in case of satellite access
x.1.6 Potential New Requirements needed to support the use case
[PR x.1.6-001] Subject to regulatory requirements and operator’s policy, the 5G System supporting both satellite access and terrestrial access shall be able to setup a Public Warning System and to inform automatically the users how to receive alert from the PWS.
[PR x.1.6-002] Subject to regulatory requirements and operator’s policy, the 5G System supporting both satellite access and terrestrial access, shall be able to map the broadcast area of a specific Alert from PWS to a geographical area that can go to some extent outside of the originating country.
[PR x.1.6-003] Subject to regulatory requirements and operator’s policy, the 5G System supporting satellite access and PWS shall be able to deliver with a reliability of 99,99% alert messages to handsets UE (without acknowledgements from the UE).
Note: reliability shall address cases where user is not able to receive the alert message due to multiple reasons (alerting end-users entering the relevant geographic area after the initial warning, or no good coverage from terrestrial access and satellite access), this could be achieved by several different mechanisms e.g. by constantly broadcasting warning messages (ensuring that these aren’t displayed again after they have been received) or by using repetition mechanisms (frequency and time domain interleaving) for coverage enhancement purposes
[PR x.1.6-004] Subject to regulatory requirements and operator’s policy, the 5G System supporting satellite access and PWS shall be able to target only a dedicated geographic area.
* * * Next changes * * * *
2 References
The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or nonspecific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
[1] 3GPP TR 21.905: "Vocabulary for 3GPP Specifications".
[2] 3GPP TS 22.261: “Service requirements for the 5G system”
[3] 3GPP TS 22.228: “Service requirements for the Internet Protocol (IP) Multimedia core network Subsystem“.
[4] ITU-T E.800: "Definitions of terms related to quality of service ".
[5] ITU-T G.114: "One-way transmission time".
[6] ITU-T G.107: "The E-model: a computational model for use in transmission planning "
[7] X. Huang, W. Qi, X. Xia, Y. Sun, Z. Sun and M. Peng, "IoT NTN for Voice Services: Architectures, Protocols, and Challenges," in IEEE Network, 2024.
[8] “Satellite firms forge unlikely alliances to create seamless multi-orbit networks”, https://spacenews.com/satellite-firms-forge-unlikely-alliances-to-create-seamless-multi-orbit-networks/, March 2024
[9] “Autonomous shipping”, https://www.imo.org/en/MediaCentre/HotTopics/Pages/Autonomous-shipping.aspx
[10] Dhinesh Kumar R, Rammohan A, Revolutionizing Intelligent Transportation Systems with Cellular Vehicle-to-Everything (C-V2X) technology: Current trends, use cases, emerging technologies, standardization bodies, industry analytics and future directions. Vehicular Communications, Vol.43, 2023, 100638
[11] 5G Automotive Association (5GAA), C-v2x use cases and service level requirements -volume i，5GAA website, https://5gaa.org/c-v2x-use-cases-and-service-level-requirements-volume-i/, 2019
[X1] "Directive (EU) 2018/1972 establishing the European Electronic Communications Code". European Union. Retrieved 11 December 2018: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32018L1972&from=EN
[X2] 3GPP TS 22.268: Public Warning System (PWS) requirements
|
S1-242063.zip
|
2026-01-13T17:18:16.597320
|
S1-242069
|
SA1
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3GPP TSG SA WG 1 Meeting #107 S1-242069
Maastricht, The Netherlands, 19-23 August 2024
Source: Novamint
pCR Title: Pseudo-CR on scope section of TR22887
Draft Spec: 3GPP TR 22.887 v0.1.0
Agenda item: 7.3
Document for: Approval
Contact: Thierry Bérisot (tberisot@novamint.com)
Abstract: This contribution proposes a text for the scope section of TR.22887
1. Introduction
The scope of TR 22.887 is currently missing
2. Reason for Change
To provide a text for the scope section of TR 22.887.
3. Conclusions
None
4. Proposal
It is proposed to agree the following changes to 3GPP TR 22.887 v0.1.0.
* * * First Change * * * *
1 Scope
The present document [[SUGGESTION_START]]describes use cases and aspects related to enhancements of the 5G system over satellite, including:[[SUGGESTION_END]]
[[SUGGESTION_START]]multi-orbits satellite access for multiple services[[SUGGESTION_END]]
[[SUGGESTION_START]]resilient notification: the ability to notify the user that a mobile terminated communication had failed when the UE was unreachable in satellite access.[[SUGGESTION_END]]
[[SUGGESTION_START]]support for emergency communications and mission critical services using satellite access [[SUGGESTION_END]]
[[SUGGESTION_START]]support IMS voice calls using GEO satellite access[[SUGGESTION_END]]
[[SUGGESTION_START]]Potential service requirements are derived for these use cases and are consolidated in a dedicated chapter. [[SUGGESTION_END]]
[[SUGGESTION_START]]The report ends with recommendations regarding the continuation of the work.[[SUGGESTION_END]]
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S1-242069.zip
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2026-01-13T17:18:57.967866
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S1-242089
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SA1
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TSGS1_107_Maastricht
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3GPP TSG-SA WG1 Meeting #107 S1-242089
Maastricht, The Netherlands, 19-23 August 2024
Title: Use case on Broadcast Services with satellite access for unregistered UEs
Agenda Item: 7.3
Source: SES, Novamint, ESA, Inmarsat, Viasat, EchoStar, JSAT, TNO, Gilat, Airbus
Contact: joel.grotz@ses.com, abbas.karaki@ses.com
Abstract: This document proposes a use case on Broadcast Services with satellite access for unregistered UEs and potential requirements for TR22.887 v0.1.0 (FS_5GSAT_ph4) with the assumption it would be agreed by SA#107 to revise the objective of the SID “Study on satellite access - Phase 4” to integrate this use case
x.1 Use case on Broadcast Services with satellite access for unregistered UEs
x.1.1 Description
Live Television remains an essential application handled by broadcast networks today. The use case of live television, whereby hundreds of thousands of viewers watch the exact same content benefits from the scaling effect that is inherent in broadcasting.
Broadcasting however requires dedicated parallel networks to be built which are generally incompatible with the development of mobile networks. Broadcast receivers are also incompatible with other mobile reception devices.
Broadcast networks are expensive to build especially if large coverage areas are required.
For all these reasons a tighter integration with mobile network deployments both Terrestrial Networks and Satellite Networks would be desirable.
Furthermore, most of satellite networks for Public Media Broadcast services are designed today in a SIM-Card Less Mode of Operation (Unregistered UE), this is a key requirement for satellite operator to decide massive deployment of 5G/5G-Advanced for broadcast services over their existing and coming constellations.
This approach can be extended to a satellite overlay addressing not only video content but also any form of digital content that would need to be distributed towards several UEs considering the benefits of the large geographical coverage of satellite networks, with a SIM-Card Less (Unregistered UE) mode of operation.
In terms of the business model, live TV services can be offered as FTA (Free-To-Air) advertisement financed services, as subscription services or on a flexible pay per view basis, whereby viewers are charged for individual events. Other business models, for example a revenue sharing agreement between the mobile operator and broadcaster, can also be envisaged. Access to live TV could also be offered as a differentiating factor.
Figure 5.x.1-1: Illustration of a Satellite Network complementing a Terrestrial Network Broadcast environment in a rural/suburban area with fixed UEs/UEs moving through the coverage area
x.1.2 Pre-conditions
Consider a use case where a mobile network operator (MNO) provides broadcast services over a radio coverage area addressing urban and sub-urban areas. An MNO has included in its service package the distribution of television channels or video broadcast services. The demand for these distribution services is increasing steadily with the number of programs available, as well as with the quality of the content. This leads in some cases to the saturation of the transmission capabilities of MNO, yet UEs being subscribed to the service shall receive the corresponding content. At the same time, there are some areas which are with poor radio coverage even not under the coverage of the MNO, leading the UEs to experience bad QoS even preventing them to get access to the television channels or video broadcast services.
A satellite network operator SNO provides services over a radio coverage which is including the suburban area and the San Bernadino Mountain.
Let consider 3 users:
- User 1 is sitting at home in the MNO coverage area, watching TV and receiving the signal from TN coverage
- User 2 is in a car exiting the Palm Springs area and going to the direction of San Bernadino Riverside. In this car, the user's kids are watching a cartoon movie broadcasted on a TV channel
- User 3 is sitting in a holiday home located in Queen Valley while watching an entertainment program on TV
All the users are not registered in the 5G System.
x.1.3 Service Flows
Using a joint Terrestrial Access Network – Satellite Access Network antenna with a chipset on the rooftop, User 1 will get the TV service from the Terrestrial Network MNO, and in case of traffic overload in the terrestrial network infrastructure, the MNO could use the resource of the SNO to redirect the TV services targeting the Palm Springs area.
Using a Joint Terrestrial Access Network - Satellite Access Network antenna with a chipset on the rooftop of his car, User 2 will be able to get continuously connectivity while exiting the suburban area and entering the mountain area. The TV channel traffic broadcast is in this case sent on all the infrastructure (satellite and terrestrial) in these areas. At a given point-in time the Joint Terrestrial Access Network – Satellite Access Network antenna will switch the reception of the data flows coming from the MNO to the one coming from the SNO after evaluation of better link quality on the SNO. User 2 is expected to experience some service interruption in the broadcast service due to the switching of the broadcast reception from MNO to SNO.
Using a Satellite Antenna with gateway, User 3 will be able to watch the TV Channel broadcasted through the SNO in the Queen Valley area.
x.1.4 Post-conditions
MNO has maintained the QoS of the content delivery for UEs being subscribed to the content delivery service, while MNO has been able to cope with the increasing traffic on its infrastructure.
MNO has offered an improved QoS of the content delivery for some UEs which are in area not under the coverage of the terrestrial network infrastructure.
The Users don’t need to have been registered to the 5G System before receiving the broadcast service, simplifying the procedure especially when switching between Terrestrial Network and Satellite Network.
The SNO doesn’t need to take care of the provisioning of the Users when extending Broadcast Services, especially when it targets Public Media Broadcast Services (which is about ensuring equitable access to broadcast services, making them available to everyone without barriers).
x.1.5 Existing features partly or fully covering the use case functionality
Some capabilities related to 4G and TV services have been addressed with Free-to-air (FTA) in TS.22.201 (4G context) but not in the context of 5G system and not taking into account satellite access as such as part of the 5G system.
x.1.6 Potential New Requirements needed to support the use case
[PR x.1.6-001] Subject to regulatory requirements and operator’s policy, a 5G System supporting satellite access and Broadcast services, shall be able to optimise the delivery of content when using the 5G satellite access Network.
[PR x.1.6-002] Subject to regulatory requirements and operator’s policy, a 5G System supporting satellite access and Broadcast Services, shall be able to deliver Media Broadcast to an UE which is not registered.
NOTE 1: The UE may not have any SIM/USIM.
NOTE 2: This will apply only to UEs adapted for receiving broadcast services by satellite access. It is assumed that if these UE need to access terrestrial network, they will need to be registered UEs
[PR x.1.6-003] Subject to regulatory requirements and operator’s policy, a 5G System supporting satellite access and Broadcast services shall be able to support a mechanism for an unregistered UE supporting satellite access and terrestrial access with 5G System to select the appropriate RAT to receive the broadcast service while moving between terrestrial network and satellite network.
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S1-242089.zip
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2026-01-13T17:19:35.321756
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S1-242163
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3GPP TSG SA WG 1 Meeting #107 S1-242163
Maastricht, The Netherlands, 19-23 August 2024
Source: China Telecom
pCR Title: Pseudo-CR on Update of 22.887: Overview
Draft Spec: 3GPP TR 22.887 v0.1.0
Agenda item: 7.3
Document for: Approval
Contact: XU XIA, xiaxu@chinatelecom.cn
Abstract: This pCR suggests updates for the overview of 22.887
1. Introduction
The overview of TR 22.887 is currently not included in version 0.1
2. Reason for Change
See the introduction. Adding an overview reflecting what is in the TR 22.887
3. Conclusions
4. Proposal
It is proposed to agree the following changes to 3GPP TR 22.887 v0.1.0
* * * First Change * * * *
4 Overview
The present document captures a set of use cases and potential service requirements related to the 5G system with satellite access taking into account new capabilities to be addressed in Rel-20 are the following:
Enhancements of existing use cases
1/ Enhanced support for emergency communications and mission critical services using satellite access
With the first commercial deployments of 5G based satellite, there is a strong demand for enhancing 5G emergency communication and mission critical services via satellite (e.g., emergency call for automotive, emergency messaging for handsets or IoT devices). The main challenges for the satellite access compared to the terrestrial access are that the satellite coverage can be broad and/or moving while at the same time many aspects of regulatory services (e.g., the area where PWS is broadcasted) typically relate to TN areas (e.g., cell ids). Therefore, there is a need to identify the gaps and the potential new service requirements for enhanced support of emergency communications and mission critical services using satellite access while considering the related regulatory requirements (e.g., geographical area aspect).
2) Support for IMS voice calls using GEO satellite access
The newly launched mobile phones with satellite connections are becoming increasingly popular in the market. Considering enabling it as a basic mobile operator service, GEO (Geostationary Earth Orbit) satellites offer a unique advantage in terms of global coverage. IMS service is firstly proposed in 3GPP as an access-agnostic service and in the 5G system it was by default supported no matter what the access technology is. However, limitations due to GEO orbit and radio access may restrict or prevent the use of IMS service. In addition, the regulatory aspect for voice calls using GEO satellite should be considered.
New use cases:
3/ Support for multi-orbits satellite access for different services
Several satellite operators are expected to deploy multi-orbits satellite networks combining LEO, MEO and GSO with 5G systems in the coming years. To take advantage of different orbit satellite systems, it is necessary to investigate new use cases for different services to enhance the user’s service experience. Simultaneous UE connections with multi-orbits are out of scope.
4/ Ability to notify a user that a mobile terminated communication failed when the UE was unreachable in satellite access
There are several use cases where terrestrial networks are not available, and users are relying solely on satellite to provide communication and are expecting to be reachable via satellite. However, when the UE is placed in pockets, backpacks, in vehicles, boats, it results in poor reception conditions which is leading to miss calls and messages. Therefore, the ability to notify the user that a mobile terminated communication (e.g., MMTel call) had failed when the UE was unreachable in satellite access is needed.
In addition to the service requirements, KPIs for a 5G system with satellite access have been introduced in TS 22.261 (section 7.4) in the context of Rel-16. Since the KPIs for satellite access have not been updated and do not reflect KPIs for 5G Advanced for the satellite access. This is especially valid for service to vehicular/drone mounted devices for verticals as well as for IoT devices. It is important to review the KPIs of 5G satellite networks and potentially update them.
Editor’s Note: the overview maybe updated based on new inputs / use cases agreed during this or next SA1 meeting.
* * * End of Change * * * *
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S1-242163.zip
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2026-01-13T17:20:04.879535
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S1-242235
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3GPP TSG-SA WG1 Meeting #107 S1-242235
Aug 19 – 23, 2024, Maastricht, Netherlands
Source: Fraunhofer IIS, vivo
pCR Title: pCR on IMS Voice Call using GEO satellite access data rates
Draft Spec: 3GPP TR 22.887
Agenda item: 7.3
Document for: Agreement
Abstract: This document proposes a text update for the use of IMS voice calls using GEO satellite access.
1. Introduction
This contribution proposes clarifications to the requirements of the GEO IMS voice call use case relative to TR 22.887 v0.1.0 (S1-241422).
2. Reason for Change
TR 22.887 v0.1.0 contains a requirement for the optimization of IMS voice call:
[PR 5.1.6.002] The 5G system shall provide mechanisms to optimize IMS voice call setup considering the transmission data rate provided by the GEO satellite access technologies.
Considering the data rate over GEO satellites, optimization should not just entail the call setup but also the media transmission stream (as illustrated in clause 5.1.1 of TR 22.887). To optimize for the GEO satellite access technologies it is thus a necessity lower the data rate of the RTP streams with the voice media. This should consist of
Reduced data rate for speech codec payload
Reduced data rate for protocol overhead
According to S1-241071, the expected data rate for the speech codec is in the range of 0.4 – 1.2 kbps. The protocol header overhead (RTP+UDP+IP with RoHC and PDCP+RLC+MAC) for the transmission is 56 bits, which is equivalent to 2.8 kbps for the regular VoIP transmission intervall of 20 ms. The ratio between payload and protocol overhead is thus in the worst case 0.4 kbps to 2.8 kbps meaning the protocol overhead is seven times higher than the actual payload.
In comparision, the worst case currently available in 3GPP (AMR at 4.75 kbps plus RTP payload header according to RFC4867 which totals to 5.6 kbps) shows a ratio of 2 for payload / protocol overhead, meaning the protocol overhead is half the codec payload.
To ensure a healthy ratio of protocol overhead vs. real payload results from the optimization process, some guidelines are proposed.
3. Proposal
It is proposed to agree the following changes to 3GPP TR 22.887 v0.1.0.
* * * First Change * * * *
5.1.6 Potential New Requirements needed to support the use case
5.1.6.1 Potential Service Requirements
[PR 5.1.6.001] A 5G system with GEO satellite access shall be able to provide IMS voice call service as defined in TS 22.228 [3].
[PR 5.1.6.002] The 5G system shall provide mechanisms to optimize IMS [[SUGGESTION_START]]signalling and transport (e.g. protocol[[SUGGESTION_END]][[SUGGESTION_START]] overhead) of [[SUGGESTION_END]]voice call[[SUGGESTION_START]]s[[SUGGESTION_END]] considering the transmission data rate provided by the GEO satellite access technologies. [[SUGGESTION_START]]The transport optimization [[SUGGESTION_END]][[SUGGESTION_START]]shall enable a [[SUGGESTION_END]][[SUGGESTION_START]]higher data rate for the payload in comparison to the protocol[[SUGGESTION_END]][[SUGGESTION_START]] overhead.[[SUGGESTION_END]]
[PR 5.1.6.003] The 5G system with GEO satellite access shall be able to support Lawful Interception for IMS voice services.
* * * End of Change * * * *
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S1-242235.zip
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2026-01-13T17:20:28.886597
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S1-242277
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TSGS1_107_Maastricht
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3GPP TSG-SA WG1 Meeting #107 S1-242277
Maastricht, The Netherlands, 19-23 August 2024
Title: Use case on using a multi orbit satellite system to improve service reliability and availability
Agenda Item: 7.3
Source: Viasat Inc., Inmarsat, Novamint, SES
Contact: Nandan Das Nandan.das@viasat.com
Jim Petranovich jim.petranovich@viasat.com
Abstract: In this note we present multiple use cases for multi-orbit satellite systems. We consider satellites in geostationary, i.e. geosynchronous equatorial plane, orbits (GEO) orbits as well as non-geostationary (NGSO) orbits. NGSO orbits could include medium (MEO), low (LEO), and highly elliptical (HEO) Earth orbits. These use cases are envisaged for a system that has multiple satellites across different orbits, potentially with different characteristics and potentially using different frequency bands, coexisting in one network.
Introduction:
Different satellite orbits bring different characteristics that prove advantageous in different applications. E.g. GEO orbits, where the satellite has a field of view (FOV) of about 1/3 of the earth’s surface area is great for coverage and for flexibly moving bandwidth and capacity from areas of low demand to areas of high demand in real time. LEO orbits with an altitude of a few hundred km are useful for latency sensitive applications and in some cases can provide line-of-sight (LoS) advantages, link budget advantages (this may or may not be the case depending on the satellite size and orbit altitude) and in some cases polar or high latitude coverage depending on the constellation configuration. HEOs are very effective at covering latitudes close to the polar regions, and typically cover polar regions of the Earth very effectively. A system that combines some or all these orbits in a multi-orbit satellite network will be able to take advantage of these characteristics to cover a wide range of use cases that would be unlikely to be satisfied using only one orbit.
In this paper, we describe one of multiple such use cases.
---------- Use Case template ----------
2.2 Use case on using a multi orbit satellite system to improve service reliability and availability.
2.2.1 Description
NGSO satellite communications necessarily require the UE to track one or more satellites as they move through the sky. This often results in blockage (and thus interrupted communications) due to trees, buildings, mountains etc., especially when the satellites are at a low elevation angle in the sky. Moreover, when the NGSO constellation is being deployed, initially, there may not be a satellite in view of a UE at all times. In fact, NGSO constellations can be designed in a way where areas of low usage may not see a satellite at all times even when the NGSO constellation is fully deployed. The GEO satellite, however, is at fixed spot (relative to the user) at all times. Thus, a GEO satellite can be used to improve availability of the satellite network as the UE can use the GEO satellite when the NGSO is blocked or unavailable. NGSO satellites often have limited active duty cycles and may be turned off in some locations to conserve satellite power
Conversely, the GEO satellite itself may be blocked if the UE doesn’t have a good visual of the GSO arc. At high latitudes, closer to the poles, where the look angles to the GEO satellite can be very low, availability can be enhanced by using NGSO satellites that have good coverage over the polar regions.
Other than due to line-of-sight blockages, one satellite path may be unavailable due to other reasons (e.g. congestion, partial network outage etc). In these cases as well, the availability of a multi orbit alternate communication path enhances the reliability of the network.
2.2.2 Pre-conditions
The network supports both GEO and NGSO satellites
The network supports handover between GEO satellite(s) and NGSO satellite(s)
UEs can close a link to both GEO and NGSO satellites
UEs are able to handle the Doppler of NGSO satellites
UEs are able to tolerate a differential delay inherent in the GEO and NGSO networks
GEO satellites provide broad coverage via (potentially) spot beams or regional beams
NGSO satellites with focused coverage via on-demand spot beams
A network plan that deconflicts frequency allocations between the GEO and NGSO satellites.
The network is able to determine the location of a user to a sufficient granularity in order to be able to activate or redirect the user to the correct NGSO coverage beam or cell
The different satellite orbits employed by the network may be supported by the same or different RAT types
The network must be able to switch traffic between GEO and NGSO satellites quickly, or receive and transmit traffic from multiple GEO and NGSO satellites simultaneously on the same coverage area.
The network must be able withstand blockages of either the GEO or the NGSO link.
The network must be able to detect blockages/outages of either the GEO or NGSO link.
2.2.3 Service Flows
During communication, the network shall detect traffic interruption.
The network shall determine the optimal path to route traffic and send traffic through that path.
2.2.4 Post-conditions
The end-user should have a more reliable network with higher availability.
2.2.5 Existing features partly or fully covering the use case functionality
3GPP Rel 19 NTN features which support the use of satellites shall cover the use case partially.
2.2.6 Potential New Requirements needed to support the use case
[PR 5.x.6.001] A 5G system with satellite access supporting multi orbit operation, shall be able to support a mechanism to split the same service flow down multiple paths with large differential delay profiles.
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S1-242277.zip
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2026-01-13T17:21:26.118371
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S1-242378
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TSGS1_107_Maastricht
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3GPP TSG-SA WG1 Meeting #107 S1-242[[SUGGESTION_START]]3[[SUGGESTION_END]][[SUGGESTION_START]]78[[SUGGESTION_END]]
Maastricht, The Netherlands, 19-23 August 2024 (revision of S1-24[[SUGGESTION_START]]2116[[SUGGESTION_END]][[SUGGESTION_START]], 2353[[SUGGESTION_END]])
Title: Use Case on [[SUGGESTION_START]]Expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] Messaging Services
Agenda Item: 7.3 FS-5GSAT_Ph4
Source: Google, Novamint, SyncTechno Inc., ISSDU, III, Korea Telecom, Sateliot[[SUGGESTION_START]], Viasat, Inmarsat,[[SUGGESTION_END]][[SUGGESTION_START]] Ec[[SUGGESTION_END]][[SUGGESTION_START]]h[[SUGGESTION_END]][[SUGGESTION_START]]oStar[[SUGGESTION_END]]
Contact: Ellen Liao, ellenliao@google.com
Abstract: This PCR proposes a new use case on [[SUGGESTION_START]]Expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] Messaging Service for UEs using [[SUGGESTION_START]]terrestrial access or[[SUGGESTION_END]] satellite access for inclusion in TR 22.887.
[[SUGGESTION_START]]v7:[[SUGGESTION_END]]
[[SUGGESTION_START]]Removed Satellite-Enabled[[SUGGESTION_END]]
[[SUGGESTION_START]]Removed IoT devices[[SUGGESTION_END]]
[[SUGGESTION_START]]Removed resource-constrained[[SUGGESTION_END]]
[[SUGGESTION_START]]Removed IP/Non-IP data[[SUGGESTION_END]]
[[SUGGESTION_START]]Removed critical[[SUGGESTION_END]]
[[SUGGESTION_START]]Removed References [[SUGGESTION_END]][[SUGGESTION_START]]regarding emergency [[SUGGESTION_END]]
[[SUGGESTION_START]][X2] 3GPP TS 22.016: "International Mobile station Equipment Identities (IMEI)".[[SUGGESTION_END]]
[[SUGGESTION_START]][X3] 3GPP TS 22.101: "Service aspects; Service principles".[[SUGGESTION_END]]
[[SUGGESTION_START]][X5] FCC 24-28: Single Network Future: Supplemental Coverage from Space, Released: April 30, 2024.[[SUGGESTION_END]]
[[SUGGESTION_START]][X6] S1-214180, GSMA NRG (NRG_012_200): LS from NRG to 3GPP on Emergency Communication Improvement[[SUGGESTION_END]]
[[SUGGESTION_START]]Removed[[SUGGESTION_END]][[SUGGESTION_START]] PSAP[[SUGGESTION_END]]
[[SUGGESTION_START]]Removed resource-constrained[[SUGGESTION_END]]
[[SUGGESTION_START]]Removed notification[[SUGGESTION_END]]
[[SUGGESTION_START]]Remove existing features [[SUGGESTION_END]]
[[SUGGESTION_START]]Replace emergency event with urgent event[[SUGGESTION_END]][[SUGGESTION_START]] UC[[SUGGESTION_END]]
[[SUGGESTION_START]]Add[[SUGGESTION_END]][[SUGGESTION_START]] terrestrial access[[SUGGESTION_END]]
[[SUGGESTION_START]]…[[SUGGESTION_END]]
* * * First Change * * * *
3 Definitions, symbols and abbreviations
3.1 Definitions
For the purposes of the present document, the terms given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
Resilient Notification: in the context of this study, it refers to a highly reliable mechanism that delivers notifications directly to user devices, such as smartphones, when in poor conditions in satellite access.
[[SUGGESTION_START]]Expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] [[SUGGESTION_START]]Messaging Service ([[SUGGESTION_END]][[SUGGESTION_START]]EMS): [[SUGGESTION_END]][[SUGGESTION_START]]in the context of this study, a Non-IMS based messaging service provided to a [[SUGGESTION_END]][[SUGGESTION_START]]UE using satellite access to communicate [[SUGGESTION_END]][[SUGGESTION_START]]Non-IMS [[SUGGESTION_END]][[SUGGESTION_START]]data with [[SUGGESTION_END]][[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] [[SUGGESTION_START]]treatment, e.g. for [[SUGGESTION_END]][[SUGGESTION_START]]urgent[[SUGGESTION_END]][[SUGGESTION_START]] events, over the 3GPP system.[[SUGGESTION_END]]
* * * Second change * * * *
2 References
The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or nonspecific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
[1] 3GPP TR 21.905: "Vocabulary for 3GPP Specifications".
[2] 3GPP TS 22.261: “Service requirements for the 5G system”
[3] 3GPP TS 22.228: “Service requirements for the Internet Protocol (IP) Multimedia core network Subsystem“.
[4] ITU-T E.800: "Definitions of terms related to quality of service ".
[5] ITU-T G.114: "One-way transmission time".
[6] ITU-T G.107: "The E-model: a computational model for use in transmission planning "
[7] X. Huang, W. Qi, X. Xia, Y. Sun, Z. Sun and M. Peng, "IoT NTN for Voice Services: Architectures, Protocols, and Challenges," in IEEE Network, 2024.
[8] “Satellite firms forge unlikely alliances to create seamless multi-orbit networks”, https://spacenews.com/satellite-firms-forge-unlikely-alliances-to-create-seamless-multi-orbit-networks/, March 2024
[9] “Autonomous shipping”, https://www.imo.org/en/MediaCentre/HotTopics/Pages/Autonomous-shipping.aspx
[10] Dhinesh Kumar R, Rammohan A, Revolutionizing Intelligent Transportation Systems with Cellular Vehicle-to-Everything (C-V2X) technology: Current trends, use cases, emerging technologies, standardization bodies, industry analytics and future directions. Vehicular Communications, Vol.43, 2023, 100638
[11] 5G Automotive Association (5GAA), C-v2x use cases and service level requirements -volume i，5GAA website, https://5gaa.org/c-v2x-use-cases-and-service-level-requirements-volume-i/, 2019
[[SUGGESTION_START]][X4] 3GPP TS 22.278: "Service requirements for the Evolved Packet System (EPS)".[[SUGGESTION_END]]
[[SUGGESTION_START]][X7] [[SUGGESTION_END]][[SUGGESTION_START]]GSMA: [[SUGGESTION_END]][[SUGGESTION_START]]Mobile IoT Deployment Guide, October 2022.[[SUGGESTION_END]]
* * * Third Change (All New Text) * * * *
x.1 Use case on [[SUGGESTION_START]]Expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] Messaging Services
x.1.1 Description
There are growing interests in [[SUGGESTION_START]]enhanc[[SUGGESTION_END]][[SUGGESTION_START]]ing[[SUGGESTION_END]] [[SUGGESTION_START]]Non-IMS[[SUGGESTION_END]] messaging [[SUGGESTION_START]]services [[SUGGESTION_END]][[SUGGESTION_START]]for [[SUGGESTION_END]][[SUGGESTION_START]]UEs using t[[SUGGESTION_END]][[SUGGESTION_START]]errestrial[[SUGGESTION_END]][[SUGGESTION_START]] access [[SUGGESTION_END]][[SUGGESTION_START]]or[[SUGGESTION_END]][[SUGGESTION_START]] satell[[SUGGESTION_END]][[SUGGESTION_START]]ite access [[SUGGESTION_END]]due to [[SUGGESTION_START]]gre[[SUGGESTION_END]][[SUGGESTION_START]]atly[[SUGGESTION_END]] [[SUGGESTION_START]]extended[[SUGGESTION_END]] coverage. Currently, there are solutions in the field provided by third parties to enable OTT messaging [[SUGGESTION_START]]for urgent[[SUGGESTION_END]][[SUGGESTION_START]] events[[SUGGESTION_END]], [[SUGGESTION_START]]to [[SUGGESTION_END]]UEs using normal sessions over 3GPP system or for proprietary devices using non-3GPP compliant satellite access. The enabling of prioritization treatment [[SUGGESTION_START]]for [[SUGGESTION_END]][[SUGGESTION_START]]UEs using [[SUGGESTION_END]][[SUGGESTION_START]]Non-IMS [[SUGGESTION_END]][[SUGGESTION_START]]messaging services during [[SUGGESTION_END]][[SUGGESTION_START]]urgent[[SUGGESTION_END]][[SUGGESTION_START]] events [[SUGGESTION_END]]within 3GPP system with [[SUGGESTION_START]]terrestrial access and[[SUGGESTION_END]] satellite access is crucial for delivering timely [[SUGGESTION_START]]message[[SUGGESTION_END]] from the UEs using [[SUGGESTION_START]]terrestrial[[SUGGESTION_END]][[SUGGESTION_START]] ac[[SUGGESTION_END]][[SUGGESTION_START]]cess [[SUGGESTION_END]][[SUGGESTION_START]]or[[SUGGESTION_END]] satellite access to the OTT application server.
[[SUGGESTION_START]]For example, the [[SUGGESTION_END]][[SUGGESTION_START]]messaging data for [[SUGGESTION_END]][[SUGGESTION_START]]urgent[[SUGGESTION_END]][[SUGGESTION_START]] events [[SUGGESTION_END]][[SUGGESTION_START]]can be delivered to [[SUGGESTION_END]][[SUGGESTION_START]]urg[[SUGGESTION_END]][[SUGGESTION_START]]ent[[SUGGESTION_END]] [[SUGGESTION_START]]contacts [[SUGGESTION_END]][[SUGGESTION_START]]preconfigured by the user[[SUGGESTION_END]][[SUGGESTION_START]]/UE[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]] [[SUGGESTION_START]]T[[SUGGESTION_END]]he application server can subsequently dispatch the [[SUGGESTION_START]]Non-IMS [[SUGGESTION_END]]data to proper [[SUGGESTION_START]]recipients[[SUGGESTION_END]]. [[SUGGESTION_START]]The [[SUGGESTION_END]][[SUGGESTION_START]]expedit[[SUGGESTION_END]][[SUGGESTION_START]]e[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] handling of messaging data in 3GPP system [[SUGGESTION_START]]play a cru[[SUGGESTION_END]][[SUGGESTION_START]]t[[SUGGESTION_END]][[SUGGESTION_START]]ial role to[[SUGGESTION_END]] enhances the availability of [[SUGGESTION_START]]messaging [[SUGGESTION_END]]services[[SUGGESTION_START]] for urgent[[SUGGESTION_END]] [[SUGGESTION_START]]events[[SUGGESTION_END]] by leveraging the broader coverage provided by [[SUGGESTION_START]]both terrestrial access and[[SUGGESTION_END]] satellite access.
[[SUGGESTION_START]]For [[SUGGESTION_END]][[SUGGESTION_START]]an[[SUGGESTION_END]][[SUGGESTION_START]]other[[SUGGESTION_END]] [[SUGGESTION_START]]example, [[SUGGESTION_END]][[SUGGESTION_START]]t[[SUGGESTION_END]]he GSMA-published Mobile IoT Deployment Guide in [X7] is widely recognized in the IoT and satellite industries for stating that “with limited support for IMS by most IoT devices, the use of SMS over IMS may not be a viable alternative”. Both IoT devices and satellites are constrained by limited resources [2], making energy efficiency a crucial factor in delivering messaging services[[SUGGESTION_START]] in expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]][[SUGGESTION_START]] manner[[SUGGESTION_END]][[SUGGESTION_START]] during urgent[[SUGGESTION_END]][[SUGGESTION_START]] events[[SUGGESTION_END]].
Consequently, there is a significant gap in providing [[SUGGESTION_START]]Expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] Messaging Services for UEs [[SUGGESTION_START]]using terrestrial access [[SUGGESTION_END]][[SUGGESTION_START]]or[[SUGGESTION_END]] [[SUGGESTION_START]]satellite access[[SUGGESTION_END]]. When prioritization treatment is applied to [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging services over 3GPP system with satellite access during [[SUGGESTION_START]]urgent[[SUGGESTION_END]] events, availability of the [[SUGGESTION_START]]messaging [[SUGGESTION_END]]services[[SUGGESTION_START]] for urgent[[SUGGESTION_END]][[SUGGESTION_START]] events[[SUGGESTION_END]] can be enhanced. As such, this use case proposes [[SUGGESTION_START]]Expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] Messaging Services (EMS) for UEs using [[SUGGESTION_START]]terrestrial access or[[SUGGESTION_END]] satellite access over 3GPP system.
x.1.2 Pre-conditions
Nova[[SUGGESTION_START]], a 5[[SUGGESTION_END]][[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]]year[[SUGGESTION_END]][[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]]old toddler,[[SUGGESTION_END]] equips a UE[[SUGGESTION_START]] for kids[[SUGGESTION_END]], which is only capable of Non-IMS messaging data. This UE configured with [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging services settings and equipped with sensors (accelerometer, GPS) can communicate [[SUGGESTION_START]]expe[[SUGGESTION_END]][[SUGGESTION_START]]dite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging over 3GPP system with [[SUGGESTION_START]]terre[[SUGGESTION_END]][[SUGGESTION_START]]strial access and/or [[SUGGESTION_END]]satellite access. [[SUGGESTION_START]]Nova’s [[SUGGESTION_END]][[SUGGESTION_START]]Mom [[SUGGESTION_END]][[SUGGESTION_START]]subscribe[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]] [[SUGGESTION_START]]to [[SUGGESTION_END]][[SUGGESTION_START]]an OTT service for N[[SUGGESTION_END]][[SUGGESTION_START]]ova[[SUGGESTION_END]][[SUGGESTION_START]]’s UE[[SUGGESTION_END]][[SUGGESTION_START]] to ensure [[SUGGESTION_END]][[SUGGESTION_START]]Nova [[SUGGESTION_END]][[SUGGESTION_START]]stays[[SUGGESTION_END]][[SUGGESTION_START]] within a [[SUGGESTION_END]][[SUGGESTION_START]]safe distance from her [[SUGGESTION_END]][[SUGGESTION_START]]mom[[SUGGESTION_END]] [[SUGGESTION_START]]during their[[SUGGESTION_END]] [[SUGGESTION_START]]summer vacation[[SUGGESTION_END]][[SUGGESTION_START]]. [[SUGGESTION_END]]
x.1.3 Service Flows
Step1, [[SUGGESTION_START]]urgent[[SUGGESTION_END]][[SUGGESTION_START]] event [[SUGGESTION_END]]trigger: while [[SUGGESTION_START]]e[[SUGGESTION_END]][[SUGGESTION_START]]njoying [[SUGGESTION_END]][[SUGGESTION_START]]the [[SUGGESTION_END]][[SUGGESTION_START]]carnival,[[SUGGESTION_END]] Nova [[SUGGESTION_START]]and her mom [[SUGGESTION_END]][[SUGGESTION_START]]becomes[[SUGGESTION_END]][[SUGGESTION_START]] separated [[SUGGESTION_END]][[SUGGESTION_START]]amidst[[SUGGESTION_END]][[SUGGESTION_START]] the crowds. [[SUGGESTION_END]][[SUGGESTION_START]]When they are separated [[SUGGESTION_END]][[SUGGESTION_START]]over a preconfigured [[SUGGESTION_END]][[SUGGESTION_START]]safety [[SUGGESTION_END]][[SUGGESTION_START]]distance, Nova’s[[SUGGESTION_END]] UE automatically initiate [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging [[SUGGESTION_START]]service [[SUGGESTION_END]]procedure.
Step2, network selection: the UE assesses terrestrial [[SUGGESTION_START]]access [[SUGGESTION_END]][[SUGGESTION_START]]and/[[SUGGESTION_END]][[SUGGESTION_START]]or satellite [[SUGGESTION_END]]access availability, and selects a network that provides EMS service over 3GPP system with [[SUGGESTION_START]]terrestrial access and/or [[SUGGESTION_END]]satellite access.
Step3, registration: the UE using [[SUGGESTION_START]]terrestrial access or[[SUGGESTION_END]] satellite access registers to the selected network for EMS Service, which includes the device's unique identifier, e.g. IMSI/SUPI/IMEI, for registration with network operator [[SUGGESTION_START]]with [[SUGGESTION_END]][[SUGGESTION_START]]terrestrial access and/or[[SUGGESTION_END]] [[SUGGESTION_START]]satellite access[[SUGGESTION_END]], as well as the authorized third[[SUGGESTION_START]]-[[SUGGESTION_END]]party.
Step4, [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging data transmission: the UE sends [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging data, which contains Nova's precise GPS coordinates, timestamp, [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] type indicator, optional sensor data, and a pre-set text message.
Step5, [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging data delivery: based on identified [[SUGGESTION_START]]urgent[[SUGGESTION_END]] event indicated in the [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging data, the [[SUGGESTION_START]]3GPP[[SUGGESTION_END]] network forwards it to the application server of the authorized third[[SUGGESTION_START]]-[[SUGGESTION_END]]party in a [[SUGGESTION_START]]fast[[SUGGESTION_END]] and reliable manner.
Step6, application server forwards [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging data: the application server determines [[SUGGESTION_START]]and routes the expedite messaging data to [[SUGGESTION_END]]appropriate [[SUGGESTION_START]]recipient[[SUGGESTION_END]] [[SUGGESTION_START]]to Nova’s [[SUGGESTION_END]][[SUGGESTION_START]]urgent[[SUGGESTION_END]] [[SUGGESTION_START]]contacts [[SUGGESTION_END]]based on the UE's location and [[SUGGESTION_START]]urgent event [[SUGGESTION_END]]type.
Step7, r[[SUGGESTION_START]]esponse[[SUGGESTION_END]] [[SUGGESTION_START]]messaging[[SUGGESTION_END]]: [[SUGGESTION_START]]Nova’s parents as [[SUGGESTION_END]][[SUGGESTION_START]]recipient [[SUGGESTION_END]]receives the [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging data, pinpoints Nova's location on a map[[SUGGESTION_START]], and [[SUGGESTION_END]]replies a[[SUGGESTION_START]]n[[SUGGESTION_END]] [[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging data to Nova's UE [[SUGGESTION_START]]that trigger[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]] [[SUGGESTION_START]]Nova’s[[SUGGESTION_END]][[SUGGESTION_START]] UE [[SUGGESTION_END]][[SUGGESTION_START]]to [[SUGGESTION_END]][[SUGGESTION_START]]ring[[SUGGESTION_END]][[SUGGESTION_START]] for notice[[SUGGESTION_END]].
x.1.4 Post-conditions
Guided by the [[SUGGESTION_START]]ring tones and [[SUGGESTION_END]]precise location data from Nova’s UE[[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] Nova’s parents[[SUGGESTION_END]] [[SUGGESTION_START]]swiftly located Nova[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] who had been left behind a mere 0.5 miles away[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]] Nova's UE played a critical role in [[SUGGESTION_START]]finding [[SUGGESTION_END]]her, demonstrating the effectiveness of EMS.
x.1.5 Existing features partly or fully covering the use case functionality
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
x.1.6 Potential New Requirements needed to support the use case
[PR X.Y.6-001]: the [[SUGGESTION_START]]5G [[SUGGESTION_END]]system with [[SUGGESTION_START]]terrestrial access and/or[[SUGGESTION_END]] satellite access shall support E[[SUGGESTION_START]]xpedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] Messaging Service that provides [[SUGGESTION_START]]fast[[SUGGESTION_END]] and reliable transmission of Non-IMS [[SUGGESTION_START]]messaging[[SUGGESTION_END]] data with priority treatment, e.g. for [[SUGGESTION_START]]urgent[[SUGGESTION_END]] events, over the 3GPP system [[SUGGESTION_START]]f[[SUGGESTION_END]][[SUGGESTION_START]]or[[SUGGESTION_END]] a UE using [[SUGGESTION_START]]terrestrial acces[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]] [[SUGGESTION_START]]or[[SUGGESTION_END]] satellite access [[SUGGESTION_START]]to communicate with [[SUGGESTION_END]][[SUGGESTION_START]]an authorized third[[SUGGESTION_END]][[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]]party[[SUGGESTION_END]].
[PR X.Y.6-002]: the [[SUGGESTION_START]]5G[[SUGGESTION_END]] system [[SUGGESTION_START]]with [[SUGGESTION_END]][[SUGGESTION_START]]terrestrial access and/or[[SUGGESTION_END]] [[SUGGESTION_START]]satellite access [[SUGGESTION_END]]shall [[SUGGESTION_START]]enable the UE to select a [[SUGGESTION_END]]network [[SUGGESTION_START]]capable of[[SUGGESTION_END]] [[SUGGESTION_START]]Expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] Messaging Service.
[PR X.Y.6-004]: the [[SUGGESTION_START]]5G [[SUGGESTION_END]]system [[SUGGESTION_START]]with [[SUGGESTION_END]][[SUGGESTION_START]]terrestrial access and/or[[SUGGESTION_END]] [[SUGGESTION_START]]satellite access [[SUGGESTION_END]]shall be able to provide mechanisms for authorized third[[SUGGESTION_START]]-[[SUGGESTION_END]]party to [[SUGGESTION_START]]receive [[SUGGESTION_END]][[SUGGESTION_START]]expedite[[SUGGESTION_END]][[SUGGESTION_START]]d[[SUGGESTION_END]] messaging data to/from a [[SUGGESTION_START]]UE[[SUGGESTION_END]] and route replies back to the UE, including in roaming case.
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S1-242378.zip
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2026-01-13T17:23:23.176403
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S1-253088
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SA1
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TSGS1_111_Goteborg
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CR
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revised
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FRMCS_Ph6 – Normative [SP-250277]
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3GPP TSG-SA WG1 Meeting #111 S1-253088
Gothenburg, Sweden, 25-29 August 2025
CR-Form-v12.3
CHANGE REQUEST
22.280
CR
0178
rev
-
Current version:
20.0.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
X
Radio Access Network
Core Network
X
Title:
Availability status of a MC User
Source to WG:
Nokia, UIC
Source to TSG:
S1
Work item code:
FRMCS_Ph6-REQ
Date:
2025-08-14
Category:
C
Release:
Rel-20
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
3GPP SA1 has studied several use cases related to availability status of a MC User in Rel-20 FS_ FRMCS_Ph6. The corresponding requirements have been captured in TR 22.989.
It is proposed to introduce the corresponding requirements into TS 22.280.
Summary of change:
Provide the status of MC Service ID and Functional Alias
Consequences if not approved:
Missing requirements for downstream groups related to availability status of a MC User
Clauses affected:
6.7.2, 6.9, Annex A
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
Start of changes
6.7.2 General requirements
[R-6.7.2-001] The MCX Service should provide a mechanism for authorized MCX Users to query [[SUGGESTION_START]]the presence status of [[SUGGESTION_END]]a particular MCX User [[SUGGESTION_START]]based on [[SUGGESTION_END]][[SUGGESTION_START]]the status of [[SUGGESTION_END]][[SUGGESTION_START]]his/her[[SUGGESTION_END]][[SUGGESTION_START]] MC Service ID[[SUGGESTION_END]][[SUGGESTION_START]] or [[SUGGESTION_END]][[SUGGESTION_START]]activated [[SUGGESTION_END]][[SUGGESTION_START]]functional alias(es)[[SUGGESTION_END]].
[R-6.7.2-002] The MCX Service should provide a mechanism for an MCX Service Administrator to configure which MCX Users, within their authority, are authorized to place a Private Communication (without Floor control).
[R-6.7.2-003] The MCX Service should provide a mechanism for authorized MCX Users to query whether a particular MCX User is capable of participating in a Private Communication[[SUGGESTION_START]] based on [[SUGGESTION_END]][[SUGGESTION_START]]the presence status of [[SUGGESTION_END]][[SUGGESTION_START]]MC Service ID[[SUGGESTION_END]][[SUGGESTION_START]] or functional alias(es)[[SUGGESTION_END]].
[R-6.7.2-004] The MCX Service shall provide a mechanism by which an MCX User can make a Private Communication to the local dispatcher based on the MCX User's current Location.
[R-6.7.2-005] The MCX Service shall provide a mechanism for the Private Communication to be set up with the MCX UE designated by the receiving MCX User to be used for Private Communications when the receiving MCX User has signed on to the MCX Service with multiple MCX UEs.
[R-6.7.2-006] The MCX Service shall provide ability for the MCX Service Administrator to set up a Private Communications with a different MCX UE than the one designated by the receiving MCX User, who has signed on to the MCX Service with multiple MCX UEs.
Next change
6.9 IDs and aliases
[R-6.9-001] The MCX Service shall provide a mechanism for permanent and temporary assignment of IDs and aliases.
[R-6.9-002] The MCX Service shall provide a mechanism for the enforcement of uniqueness of IDs and aliases.
[R-6.9-003] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure IDs and aliases.
[R-6.9-004] The MCX Service shall provide the MCX Service User ID and /or associated aliases, the identity of the Selected MCX Service Group, and, if available, the identity of the Mission Critical Organization name of the transmitting MCX User to all MCX UEs that are receiving for display by each MCX UE.
[[SUGGESTION_START]][R-6.9-005] [[SUGGESTION_END]][[SUGGESTION_START]]The MCX Service shall provide a mechanism [[SUGGESTION_END]][[SUGGESTION_START]]to assign a presence status to the MC Service ID of an MCX User after successful log-in or to its activated Functional Alias[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]][R-6.9-006] The MCX Service shall enable the update of the presence status of the MC Service ID or Functional Alias based on the activity of the MCX user (e.g. ‘unavailable’ due to temporarily putting ‘on-hold’ a communication).[[SUGGESTION_END]]
Next change
Annex A (normative):
MCCoRe Requirements for MCPTT
Table A.1 provides an exhaustive list of those requirements in 3GPP TS 22.280 that are applicable to MCPTT.
Table A.1
5 MCX Service Requirements common for on the network and off the network
NA
5.1 General Group Communications Requirements
NA
5.1.1 General aspects
R-5.1.1-001
R-5.1.1-002
R-5.1.1-003
R-5.1.1-004
R-5.1.1-005
5.1.2 Group/status information
R-5.1.2-001
R-5.1.2-002
5.1.3 Group configuration
R-5.1.3-001
R5.1.3-002
5.1.4 Identification
R-5.1.4-001
5.1.5 Membership/affiliation
R-5.1.5-001
R-5.1.5-002
R-5.1.5-003
R-5.1.5-004
R-5.1.5-005
R-5.1.5-006
R-5.1.5-007
R-5.1.5-008
5.1.6 Group Communication administration
R-5.1.6-001
5.1.7 Prioritization
R-5.1.7-001
R-5.1.7-002
5.1.8 Charging requirements for MCX Service
R-5.1.8-001
R-5.1.8-003
R-5.1.8-004
R-5.1.8-005
R-5.1.8-006
R-5.1.8-007
R-5.1.8-008
R-5.1.8-009
R-5.1.8-010
R-5.1.8-011
5.1.9 MCX Service Emergency Alert triggered by location
NA
5.2 Broadcast Group
NA
5.2.1 General Broadcast Group Communication
R-5.2.1-001
R-5.2.1-002
5.2.2 Group-Broadcast Group (e.g., announcement group)
R-5.2.2-001
R-5.2.2-002
R-5.2.2-003
R-5.2.2-004
5.2.3 User-Broadcast Group (e.g., System Communication)
R-5.2.3-001
R-5.2.3-002
5.3 Late communication entry
R-5.3-001
R-5.3-002
R-5.3-003
R-5.3-004
R-5.3-005
5.4 Receiving from multiple MCX Service communications
5.4.1 Overview
NA
5.4.2 Requirements
R-5.4.2-001
R-5.4.2-002
R-5.4.2-003
R-5.4.2-004
R-5.4.2-004A
R-5.4.2-004B
R-5.4.2-005
R-5.4.2-006
R-5.4.2-007
R-5.4.2-007a
R-5.4.2-008
R-5.4.2-009
5.5 Private Communication
NA
5.5.1 Private Communication general requirements
NA
5.5.2 Charging requirement for MCX Service
R-5.5.2-001
5.6 MCX Service priority requirements
NA
5.6.1 Overview
NA
5.6.2 Communication types based on priorities
NA
5.6.2.1 MCX Service Emergency and Imminent Peril general requirements
NA
5.6.2.1.1 Overview
NA
5.6.2.1.2 Requirements
R-5.6.2.1.2-001
R-5.6.2.1.2-002
R-5.6.2.1.2-003
R-5.6.2.1.2-004
R-5.6.2.1.2-005
5.6.2.2 MCX Service Emergency Group Communication
NA
5.6.2.2.1 MCX Service Emergency Group Communication requirements
R-5.6.2.2.1-001
R-5.6.2.2.1-002
R-5.6.2.2.1-003
R-5.6.2.2.1-004
R-5.6.2.2.1-005
R-5.6.2.2.1-006
R-5.6.2.2.1-007
R-5.6.2.2.1-008
R-5.6.2.2.1-009
R-5.6.2.2.1-010
R-5.6.2.2.1-011
R-5.6.2.2.1-012
R-5.6.2.2.1-013
R-5.6.2.2.1-014
5.6.2.2.2 MCX Service Emergency Group Communication cancellation requirements
R-5.6.2.2.2-001
R-5.6.2.2.2-002
R-5.6.2.2.2-003
R-5.6.2.2.2-004
R-5.6.2.2.2-005
5.6.2.3 MCX Service Imminent Peril Group
NA
5.6.2.3.1 MCX Service Imminent Peril Group Communication requirements
R-5.6.2.3.1-001
R-5.6.2.3.1-002
R-5.6.2.3.1-003
R-5.6.2.3.1-004
R-5.6.2.3.1-005
R-5.6.2.3.1-006
R-5.6.2.3.1-007
R-5.6.2.3.1-008
R-5.6.2.3.1-009
5.6.2.3.2 MCX Service Imminent Peril Group Communications cancellation requirements
R-5.6.2.3.2-001
R-5.6.2.3.2-002
R-5.6.2.3.2-003
R-5.6.2.3.2-004
5.6.2.4 MCX Service Emergency Alert
NA
5.6.2.4.1 MCX Service Emergency Alert requirements
R-5.6.2.4.1-001
R-5.6.2.4.1-002
R-5.6.2.4.1-003
R-5.6.2.4.1-004
R-5.6.2.4.1-004a
R-5.6.2.4.1-005
R-5.6.2.4.1-006
R-5.6.2.4.1-007
R-5.6.2.4.1-008
R-5.6.2.4.1-009
R-5.6.2.4.1-010
R-5.6.2.4.1-011
R-5.6.2.4.1-012
R-5.6.2.4.1-013
5.6.2.4.2 MCX Service Emergency Alert cancellation requirements
R-5.6.2.4.2-001
R-5.6.2.4.2-002
R-5.6.2.4.2-003
5.7 MCX Service User ID
R-5.7-001
R-5.7-002
R-5.7-003
5.8 MCX UE Management
R-5.8-001
R-5.8-002
5.9 MCX Service User Profile
R-5.9-001
R-5.9-002
5.9A Functional alias
R-5.9a-001
R-5.9a-001a
R-5.9a-001b
R-5.9a-001c
R-5.9a-002
R-5.9a-002a
R-5.9a-003
R-5.9a-004
R-5.9a-005
R-5.9a-006
R-5.9a-007
R-5.9a-008
R-5.9a-009
R-5.9a-010
R-5.9a-011
R-5.9a-012
R-5.9a-013
R-5.9a-014
R-5.9a-015
R-5.9a-016
R-5.9a-017
R-5.9a-018
R-5.9a-019
R-5.9a-020
R-5.9a-021
R-5.9a-022
R-5.9a-023
[R-5.9a-024
R-5.9a-025
R-5.9a-026
R-5.9a-027
R-5.9a-028
R-5.9a-029
R-5.9a-030
R-5.9a-031
5.10 Support for multiple devices
R-5.10-001
R-5.10-001a
R-5.10-002
5.11 Location
R-5.11-001
R-5.11-002
R-5.11-002a
R-5.11-003
R-5.11-004
R-5.11-005
R-5.11-006
R-5.11-007
R-5.11-008
R-5.11-009
R-5.11-010
R-5.11-011
R-5.11-013
R-5.11-014
R-5.11-015
R-5.11-015
5.12 Security
R-5.12-001
R-5.12-002
R-5.12-003
R-5.12-004
R-5.12-005
R-5.12-006
R-5.12-007
R-5.12-008
R-5.12-009
R-5.12-010
R-5.12-011
R-5.12-012
R-5.12-013
R5-12-014
5.13 Media quality
R-5.13-001
5.14 Relay requirements
R-5.14-001
R-5.14-002
R-5.14-003
R-5.14-004
5.15 Gateway requirements
R-5.15-001
R-5.15-002
R-5.15-003
5.16 Control and management by Mission Critical Organizations
NA
5.16.1 Overview
NA
5.16.2 General requirements
R-5.16.2-001
R-5.16.2-002
R-5.16.2-003
R-5.16.2-004
R-5.16.2-005
5.16.3 Operational visibility for Mission Critical Organizations
R-5.16.3-001
5.17 General administrative – groups and users
R-5.17-001
R-5.17-002
R-5.17-003
R-5.17-004
R-5.17-005
R-5.17-006
R-5.17-007
R-5.17-008
5.18 Open interfaces for MCX services
NA
5.18.1 Overview
NA
5.18.2 Requirements
NA
5.19 Media forwarding
NA
5.19.1 Service description
NA
5.19.2 Requirements
NA
5.20 Receipt notification
NA
5.20.1 Service description
NA
5.20.2 Requirements
NA
5.21 Additional services for MCX Service communications
NA
5.21.1 Remotely initiated MCX Service communication
NA
5.21.1.1 Overview
NA
5.21.1.2 Requirements
NA
5.21.2 Remotely terminated MCX Service communication
NA
5.21.2.1 Requirements
R-5.21.2.1-001
6 MCX Service requirements specific to on-network use
NA
6.1 General administrative – groups and users
R-6.1-001
R-6.1-002
R-6.1-003
R-6.1-004
R-6.1-005
6.2 MCX Service communications
NA
6.2.1 Notification and acknowledgement for MCX Service Group Communications
NA
6.2.2 Queuing
R-6.2.2-001
R-6.2.2-002
R-6.2.2-003
R-6.2.2-004
R-6.2.2-005
R-6.2.2-006
6.3 General requirements
R-6.3-001
R-6.3-002
R-6.3-003
R-6.3-004
6.4 General group communication
NA
6.4.1 General aspects
R-6.4.1-001
6.4.2 Group status/information
R-6.4.2-005
R-6.4.2-001
R-6.4.2-002
R-6.4.2-003
R-6.4.2-004
R-6.4.2-006
R-6.4.2-007
6.4.3 Identification
R-6.4.3-001
R-6.4.3-002
6.4.4 Membership/affiliation
R-6.4.4-001
R-6.4.4-002
R-6.4.4-002a
R-6.4.4-003
R-6.4.4-004
6.4.5 Membership/affiliation list
R-6.4.5-001
R-6.4.5-002
R-6.4.5-003
R-6.4.5-003a
R-6.4.5-004
R-6.4.5-005
R-6.4.5-006
R-6.4.5-007
R-6.4.5-008
6.4.6 Authorized user remotely changes another MCX User’s affiliated and/or Selected MCX Service Group(s)
NA
6.4.6.1 Mandatory change
R-6.4.6.1-001
R-6.4.6.1-002
R-6.4.6.1-003
R-6.4.6.1-004
6.4.6.2 Negotiated change
R-6.4.6.2-001
R-6.4.6.2-002
R-6.4.6.2-003
R-6.4.6.2-004
R-6.4.6.2-005
R-6.4.6.2-006
6.4.7 Prioritization
R-6.4.7-001
R-6.4.7-002
R-6.4.7-003
R-6.4.7-004
6.4.8 Relay requirements
R-6.4.8-001
6.4.9 Administrative
R-6.4.9-001
R-6.4.9-002
R-6.4.9-003
R-6.4.9-004
R-6.4.9-005
R-6.4.9-006
6.5 Broadcast Group
NA
6.5.1 General Broadcast Group Communication
R-6.5.1-001
R-6.5.1-002
6.5.2 Group-Broadcast Group (e.g., announcement group)
R-6.5.2-001
6.5.3 User-Broadcast Group (e.g., system communication)
R-6.5.3-001
6.6 Dynamic group management (i.e., dynamic reporting)
NA
6.6.1 General dynamic regrouping
R-6.6.1-001
R-6.6.1-002
R-6.6.1-003
R-6.6.1-004
R-6.6.1-005
R-6.6.1-006
6.6.2 Group regrouping
NA
6.6.2.1 Service description
NA
6.6.2.2 Requirements
R-6.6.2.2-001
R-6.6.2.2-002
R-6.6.2.2-003
R-6.6.2.2-004
R-6.6.2.2-005
R-6.6.2.2-006
R-6.6.2.2-007
R-6.6.2.2-008
R-6.6.2.2-009
R-6.6.2.2-010
R-6.6.2.2-011
R-6.6.2.2-012
R-6.6.2.2-013
6.6.3 Temporary Broadcast Groups
R-6.6.3-001
R-6.6.3-001a
R-6.6.3-001b
R-6.6.3-002
6.6.4 User regrouping
NA
6.6.4.1 Service description
NA
6.6.4.2 Requirements
R-6.6.4.2-001
R-6.6.4.2-002
R-6.6.4.2-002a
R-6.6.4.2-002b
R-6.6.4.2-003
R-6.6.4.2-004
R-6.6.4.2-005
6.6.5 Dynamic Group Participation
NA
6.6.5.1 Service description
NA
6.6.5.2 Requirements
R-6.6.5.2-001
R-6.6.5.2-002
R-6.6.5.2-003
R-6.6.5.2-004
R-6.6.5.2-005
R-6.654.2-006
R-6.6.5.2-007
R-6.6.5.2-008
6.7 Private Communication
NA
6.7.1 Overview
NA
6.7.2 General requirements
R-6.7.2-001
R-6.7.2-002
R-6.7.2-003
R-6.7.2-004
R-6.7.2-005
R-6.7.2-006
6.7.3 Administrative
R-6.7.3-001
R-6.7.3-002
R-6.7.3-003
R-6.7.3-004
R-6.7.3-005
R-6.7.3-006
R-6.7.3-007
R-6.7.3-007a
R-6.7.3-008
6.7.4 Prioritization
R-6.7.4-001
R-6.7.4-002
R-6.7.4-003
R-6.7.4-004
R-6.7.4-005
R-6.7.4-006
R-6.7.4-007
6.7.5 Private Communication (without Floor control) commencement requirements
R-6.7.5-001
R-6.7.5-002
R-6.7.5-003
6.7.6 Private Communication (without Floor control) termination
R-6.7.6-001
R-6.7.6-002
6.8 MCX Service priority requirements
NA
6.8.1 General
R-6.8.1-001
R-6.8.1-002
R-6.8.1-003
R-6.8.1-004
R-6.8.1-005
R-6.8.1-006
R-6.8.1-007
R-6.8.1-008
R-6.8.1-009
R-6.8.1-010
R-6.8.1-011
R-6.8.1-012
R-6.8.1-013
R-6.8.1-014
R-6.8.1-015
R-6.8.1-016
6.8.2 3GPP system access controls
R-6.8.2-001
6.8.3 3GPP system admission controls
R-6.8.3-001
6.8.4 3GPP system scheduling controls
R-6.8.4-001
6.8.5 UE access controls
R-6.8.5-001
6.8.6 Mobility and load management
NA
6.8.6.1 Mission Critical mobility management according to priority
R-6.8.6.1-001
R-6.8.6.1-002
6.8.6.2 Load management
R-6.8.6.2-001
R-6.8.6.2-002
R-6.8.6.2-003
R-6.8.6.2-004
R-6.8.6.2-005
6.8.7 Application layer priorities
NA
6.8.7.1 Overview
NA
6.8.7.2 Requirements
R-6.8.7.2-001
R-6.8.7.2-002
R-6.8.7.2-003
R-6.8.7.2-004
R-6.8.7.2-005
R-6.8.7.2-006
R-6.8.7.2-007
R-6.8.7.2-008
R-6.8.7.2-009
R-6.8.7.2-010
6.8.8 Communication types based on priorities
NA
6.8.8.1 MCX Service Emergency Group Communication requirements
R-6.8.8.1-001
R-6.8.8.1-002
R-6.8.8.1-003
R-6.8.8.1-004
6.8.8.2 MCX Service Emergency Private Communication requirements
NA
6.8.8.3 Imminent Peril Group Communication requirements
R-6.8.8.3-001
R-6.8.8.3-002
R-6.8.8.3-003
6.8.8.4 MCX Service Emergency Alert
NA
6.8.8.4.1 Requirements
R-6.8.8.4.1-001
R-6.8.8.4.1-002
R-6.8.8.4.1-003
R-6.8.8.4.1-004
R-6.8.8.4.1-005
R-6.8.8.4.1-006
6.8.8.4.2 MCX Service Emergency Alert cancellation requirements
R-6.8.8.4.2-001
R-6.8.8.4.2-002
6.8.8.X Ad hoc Group Communication requirements
R-6.8.8.X-001
6.9 IDs and aliases
R-6.9-001
R-6.9-002
R-6.9-003
R-6.9-004
[[SUGGESTION_START]]R-6.9-005[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.9-006[[SUGGESTION_END]]
6.10 User Profile management
R-6.10-001
R-6.10-002
R-6.10-003
R-6.10-004
6.11 Support for multiple devices
R-6.11-001
R-6.11-002
R-6.11-003
6.12 Location
R-6.12-001
R-6.12-002
R-6.12-003
R-6.12-004
R-6.12-005
R-6.12-006
R-6.12-007
6.13 Security
NA
6.13.1 Overview
NA
6.13.2 Cryptographic protocols
R-6.13.2-001
R-6.13.2-002
R-6.13.2-003
6.13.3 Authentication
R-6.13.3-001
6.13.4 Access control
R-6.13.4-001
R-6.13.4-002
R-6.13.4-003
R-6.13.4-004
R-6.13.4-005
R-6.13.4-006
R-6.13.4-007
R-6.13.4-008
R-6.13.4-009
R-6.13.4-010
6.13.5 Regulatory issues
R-6.13.5-001
6.13.6 Storage control
NA
6.14 Interactions for MCX Service Group Communications and MCX Service Private Communications
R-6.14-001
R-6.14-002
6.15 Additional services for MCX Service communications
NA
6.15.1 Discreet listening capabilities
R-6.15.1-001a
R-6.15.1-001
R-6.15.1-002
R-6.15.1-002a
R-6.15.1-003
R-6.15.1-004
6.15.2 Ambient listening
NA
6.15.2.1 Overview of ambient listening
NA
6.15.2.2 Ambient listening requirements
NA
6.15.2.2.1 General ambient listening requirements
R-6.15.2.2.1-001
R-6.15.2.2.1-002
R-6.15.2.2.1-003
6.15.2.2.2 Remotely initiated ambient listening requirements
R-6.15.2.2.2-001
R-6.15.2.2.2-002
6.15.2.2.3 Locally initiated ambient listening requirements
R-6.15.2.2.3-001
R-6.15.2.2.3-002
6.15.3 Remotely initiated MCX Service Communication
NA
6.15.3.1 Overview
NA
6.15.3.2 Requirements
R-6.15.3.2-001
R-6.15.3.2-002
R-6.15.3.2-003
R-6.15.3.2-004
6.15.4 Recording and audit requirements
R-6.15.4-001
R-6.15.4-002
R-6.15.4-003
R-6.15.4-004
R-6.15.4-005
R-6.15.4-006
R-6.15.4-007
R-6.15.4-008
R-6.15.4-009
R-6.15.4-010
R-6.15.4-011
6.15.5 MCX Service Ad hoc Group Communication
NA
6.15.5.1 Overview
NA
6.15.5.2 General Aspects
R-6.15.5.2-001
R-6.15.5.2-001a
R-6.15.5.2-001b
R-6.15.5.2-001c
R-6.15.5.2-002
R-6.15.5.2-003
R-6.15.5.2-004
R-6.15.5.2-005
R-6.15.5.2-006
R-6.15.5.2-007
R-6.15.5.2-008
R-6.15.5.2-009
R-6.15.5.2-010
R-6.15.5.2-011
R-6.15.5.2-012
R-6.15.5.2-013
R-6.15.5.2-014
R-6.15.5.2-014a
R-6.15.5.2-015
R-6.15.5.2-016
R-6.15.5.2-017
6.15.5.3 Administrative
R-6.15.5.3-001
R-6.15.5.3-002
R-6.15.5.3-003
R-6.15.5.3-004
R-6.15.5.3-005
6.15.5.4 Notification and acknowledgement for MCX Service Ad hoc Group Communications
R-6.15.5.4-001
6.15.6 MCX Service Ad hoc Group Emergency Alert
NA
6.15.6.1 Overview
NA
6.15.6.2 General aspects
R-6.15.6.2-001
R-6.15.6.2-002
R-6.15.6.2-002a
R-6.15.6.2-003
R-6.15.6.2-004
R-6.15.6.2-005
R-6.15.6.2-005a
R-6.15.6.2-005b
R-6.15.6.2-006
R-6.15.6.2-007
R-6.15.6.2-008
6.15.6.3 Administrative
R-6.15.6.3-001
R-6.15.6.3-002
R-6.15.6.3-003
R-6.15.6.3-004
R-6.15.6.3-005
6.16 Interaction with telephony services
R-6.16-001
R-6.16-002
6.17 Interworking
NA
6.17.1 Non-3GPP access
R-6.17.1-001
6.17.2 Interworking between MCX Service systems
R-6.17.2-001
R-6.17.2-002
R-6.17.2-003
R-6.17.2-004
R-6.17.2-005
R-6.17.2-006
R-6.17.2-007
R-6.17.2-008
6.17.3 Interworking with non-MCX Service systems
NA
6.17.3.1 GSM-R
R-6.17.3.1-001
R-6.17.3.1-002
R-6.17.3.1-003
R-6.17.3.1-004
R-6.17.3.1-005
6.17.3.2 External systems
R.6.17.3.2-001
R.6.17.3.2-002
6.18 MCX Service coverage extension using ProSe UE-to-Network Relays
R-6.18-001
R-6.18-002
R-6.18-003
R-6.18-004
R-6.18-005
R-6.18-006
6.19 Additional MCX Service requirements
NA
6.19.1 Communication rejection and queuing
NA
6.19.1.1 Requirements
R-6.19.1.1-001
R-6.19.1.1-002
R-6.19.1.1-003
R-6.19.1.1-004
R-6.19.1.1-005
R-6.19.1.1-006
R-6.19.1.1-007
7 MCX Service requirements specific to off-network use
NA
7.1 Off-network communications overview
NA
7.2 General off-network MCX Service requirements
R-7.2-001
R-7.2-002
R-7.2-003
R-7.2-004
R-7.2-005
7.3 Admission control
NA
7.3.1 General aspects
R-7.3.1-001
R-7.3.1-002
R-7.3.1-003
7.3.2 Communication initiation
R-7.3.2-001
R-7.3.2-002
R-7.3.2-003
R-7.3.2-004
R-7.3.2-005
7.4 Communication termination
R-7.4-001
R-7.4-002
R-7.4-003
R-7.4-004
7.5 Broadcast Group
R-7.5-001
R-7.5-002
7.6 MCX Service priority requirements
R-7.6-001
R-7.6-002
R-7.6-003
R-7.6-004
R-7.6-005
R-7.6-006
R-7.6-007
R-7.6-008
R-7.6-009
7.7 Communication types based on priorities
NA
7.7.1 MCX Service Emergency Group Communication requirements
R-7.7.1-001
R-7.7.1-002
R-7.7.1-003
7.7.2 MCX Service Emergency Group Communication cancellation requirements
R-7.7.2-001
7.7.3 Imminent Peril Communication
NA
7.7.3.1 Imminent Peril Group Communication requirements
R-7.7.3.1-001
R-7.7.3.1-002
R-7.7.3.1-003
R-7.7.3.1-004
R-7.7.3.1-005
7.7.3.2 Imminent Peril Group Communication cancellation requirements
R-7.7.3.2-001
R-7.7.3.2-002
7.8 Location
R-7.8-001
R-7.8-002
R-7.8-003
7.9 Security
R-7.9-001
R-7.9-002
7.10 Off-network MCX Service operations
R-7.10-001
R-7.10-002
R-7.10-003
7.11 Off-network UE functionality
R-7.11-001
R-7.11-002
R-7.11-003
7.12 Streaming for ProSe UE-to-UE Relay and UE-to-Network Relay
NA
7.12.1 UE-to-Network Relay for all data types
R-7.12.1-001
R-7.12.1-002
R-7.12.1-003
R-7.12.1-004
7.12.2 UE-to-UE Relay streaming
R-7.12.2-001
R-7.12.2-002
R-7.12.2-003
7.12.3 Off-Network streaming
R-7.12.3-001
R-7.12.3-002
R-7.12.3-003
7.13 Switching to off-network MCX Service
R-7.13-001
R-7.13-002
R-7.13-003
R-7.13-004
R-7.13-005
7.14 Off-network recording and audit requirements
R-7.14-001
R-7.14-001a
R-7.14-002
R-7.14-002a
7.15 Off-network UE-to-UE relay
NA
7.15.1 Private Communications
R-7.15.1-001
R-7.15.1-002
R-7.15.1-003
7.15.2 Group Communications
R-7.15.2-001
R-7.15.2-002
7.16 Off-network Ad hoc Group Communication
R-7.16-001
8 Inter-MCX Service interworking
NA
8.1 Inter-MCX Service interworking overview
NA
8.2 Concurrent operation of different MCX Services
NA
8.2.1 Overview
NA
8.2.2 Requirements
R-8.2.2-001
R-8.2.2-002
R-8.2.2-003
R-8.2.2-004
R-8.2.2-005
R-8.2.2-006
R-8.2.2-007
8.3 Use of unsharable resources within a UE
R-8.3-001
R-8.3-002
R-8.3-003
R-8.3-004
R-8.3-005
R-8.3-006
8.4 Single group with multiple MCX Services
NA
8.4.1 Overview
NA
8.4.2 Requirements
R-8.4.2-001
R-8.4.2-002
R-8.4.2-003
R-8.4.2-004
R-8.4.2-005
8.4.3 Compatibility of UE
NA
8.4.3.1 Advertising service capabilities required
R-8.4.3.1-001
R-8.4.3.1-002
R-8.4.3.1-003
R-8.4.3.1-004
8.4.3.2 Conversion between capabilities
R-8.4.3.2-001
8.4.4 Individual permissions for service access
R-8.4.4-001
8.4.5 Common alias and user identities or mappable
R-8.4.5-001
8.4.6 Single location message
R-8.4.6-001
R-8.4.6-002
R-8.4.6-003
8.5 Priority between services
NA
8.5.1 Overview
NA
8.5.2 Requirements
R-8.5.2-001
R-8.5.2-002
R-8.5.2-003
R-8.5.2-004
R-8.5.2-005
9 Air Ground Air Communications
NA
9.1 Service description
NA
9.2 Requirements
R-9.2-001
10 MCX Service in IOPS mode
R-10-001
End of changes
|
S1-253088.zip
|
2026-01-13T17:29:24.058228
|
S1-253252
|
SA1
|
TSGS1_111_Goteborg
|
CR
|
revised
|
FRMCS_Ph6 – Normative [SP-250277]
|
3GPP TSG-SA WG1 Meeting #111 S1-253252
Gothenburg, Sweden, 25-29 August 2025 (revision of S1-xxxxxx)
CR-Form-v12.3
CHANGE REQUEST
22.280
CR
0179
rev
-
Current version:
20.0.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
X
Radio Access Network
Core Network
X
Title:
1.Addition of Functional Aliases in the participants list and in the notifications of AHGC
2.Authorizations for combining Ad hoc Group calls
Source to WG:
UIC, Nokia
Source to TSG:
S1
Work item code:
FRMCS_Ph6-REQ
Date:
2025-08-14
Category:
C
Release:
Rel-20
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
Enhance alignement with functional requirements of UIC specifications (FU-7120 v2.1.2) related to the usage of functional aliases of participants of an AHGC, based on criteria and authorizations of MCX users to combine AHGC.
Summary of change:
Addition of the functional aliases in the participants list
Addition of the functional alias in the response of the receiving participant
Addition of the functional aliases of receiving participants in the notifications
Configure authorizations for MCX users to combine AHGC
Consequences if not approved:
Misalignement with functional requirements of the UIC specifications
Missing stage-1 requirements for downstream groups to include the relevant features
Clauses affected:
6.15.5.2, 6.15.5.3, 6.15.5.4, Annexe A
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
Start of changes
6.15.5.2 General aspects
[R-6.15.5.2-001] The MCX Service shall provide a mechanism for an authorized MCX User to combine an ad hoc multiplicity of MCX Users into a MCX Service Ad hoc Group Communication.[[SUGGESTION_START]] The multiplicity of MCX Users may be identified by their MCX Service ID or by their Functional [[SUGGESTION_END]][[SUGGESTION_START]]A[[SUGGESTION_END]][[SUGGESTION_START]]lias.[[SUGGESTION_END]]
NOTE: Selection of the list of MCX Users can be manual, or automatic based on certain criteria (e.g., location). This is left for implementation.
[R-6.15.5.2-001a] The MCX Service shall provide the reason for denial to an MCX user who was not authorised to initiate an MCX Service Ad hoc Group Communication.
[R-6.15.5.2-001b] The participant of an MCX Ad hoc Group Communication who has activated several Functional Alias(es) shall receive only one communication based on the unique MCX Service ID.
[R-6.15.5.2-001c] The MCX Service shall provide a mechanism for the participant of an Ad hoc Group Communication to determine the Functional Alias, through which this participant is addressed by the communication.
[[SUGGESTION_START]][R-6.15.5.2-001d] The MCX Service shall [[SUGGESTION_END]][[SUGGESTION_START]]enable the [[SUGGESTION_END]][[SUGGESTION_START]]receiving participant [[SUGGESTION_END]][[SUGGESTION_START]]of an Ad hoc Group Communication to [[SUGGESTION_END]][[SUGGESTION_START]]respond [[SUGGESTION_END]][[SUGGESTION_START]]including[[SUGGESTION_END]][[SUGGESTION_START]] the Functional Alias, [[SUGGESTION_END]][[SUGGESTION_START]]he[[SUGGESTION_END]][[SUGGESTION_START]]/she [[SUGGESTION_END]][[SUGGESTION_START]]has activated[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[R-6.15.5.2-002] An MCX Service Ad hoc Group Communication is a type of MCX Service Group communication and shall support MCX Service Group Communication mechanisms for call processing (e.g., transmit request queuing, hang time, broadcast mode).
[R-6.15.5.2-003] MCX Service Ad hoc Group Communications shall be terminated by an authorised user or using the same mechanisms as MCX Service Group communications (e.g., initiator release, server release, hang time expiration).
[R-6.15.5.2-004] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure additional conditions under which MCX Service Ad hoc Group Communication shall be terminated (e.g., last Participant leaving, second last Participant leaving, initiator leaving).
[R-6.15.5.2-005] When an MCX Service Ad hoc Group Communication is terminated the group shall not persist.
[R-6.15.5.2-006] The MCX Service shall provide a mechanism for the initiator of a MCX Service Ad hoc Group Communication to indicate which MCX Users have to mandatorily acknowledge the setup request before the media transmission proceeds.
[R-6.15.5.2-007] The MCX Service shall provide a mechanism for an authorized initiator of a MCX Service Ad hoc Group Communication to define the communication parameters for the Ad hoc Group Communication (e.g. priority, hang time, broadcast/non-broadcast)
[R-6.15.5.2-008] MCX Service Ad hoc Group Communications shall be able to support the same urgency as MCX Service Group communication (e.g., general group, emergency, imminent peril).
[R-6.15.5.2-009] MCX Service Ad hoc Group Communications shall support the applicable security requirements as identified in sub-clause 5.12.
[R-6.15.5.2-010] The MCX Service shall provide a mechanism for the initiator of an MCX Service Ad hoc Group Communication to request that the list of participants is suppressed.
[R-6.15.5.2-011] The MCX Service shall provide a mechanism for authorized MCX Users to create a permanent MCX Service Group from the members of the MCX Service Ad hoc Group communication.
[R-6.15.5.2-012] The MCX Service shall provide a mechanism for the initiator and/or an authorised user to add or remove participants during an in progress MCX Service Ad hoc Group communication.
[R-6.15.5.2-013] The MCX Service shall provide a mechanism for a participant to join an in progress MCX Service Ad hoc Group communication.
[R-6.15.5.2-014] The MCX Service shall provide a mechanism for the initiator of an MCX Service Ad hoc Group Communication to request that the list of participants [[SUGGESTION_START]]with their corresponding Functional Aliases[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]] be determined and updated by the MCX Service system using pre-defined criteria.
[R-6.15.5.2-014a] When the list of participants is determined or updated by the MCX Service system, the MCX Service shall provide a mechanism that monitors and ensures that the participants list is applied for an MCX Service Ad hoc Group communication, performing retries when needed.
[R-6.15.5.2-015] The MCX Service shall provide a mechanism for a participant of an MCX Service Ad hoc Group Communication to provide his location information to an authorised participant of the same group.
[R-6.15.5.2-016] The MCX Service shall provide a mechanism for an authorized MCX User to combine multiple Ad hoc Group communications.
[R-6.15.5.2-017] The MCX Service shall provide notifications to the relevant participants and authorized users of the combined Ad hoc Group communication.
[[SUGGESTION_START]][R-6.15.5.2-018] The MCX Service shall provide to the participants of the combined Ad hoc Group communication, the same MCX User authorizations, as in the previous [[SUGGESTION_END]][[SUGGESTION_START]]original[[SUGGESTION_END]][[SUGGESTION_START]] Ad hoc Group communications.[[SUGGESTION_END]]
Next change
6.15.5.3 Administrative
[R-6.15.5.3-001] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users, within their authority, are authorized to initiate and/or release a MCX Service Ad hoc Group Communication.
[R-6.15.5.3-002] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure the maximum number of MCX Users who can participate in a MCX Service Ad hoc Group Communication.
[R-6.15.5.3-003] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users are authorized to participate in a MCX Service Ad hoc Group Communication.
[R-6.15.5.3-004] The MCX Service shall provide a mechanism for an MCX Service Administrator to define the default parameters for MCX Service Ad hoc Group communication (e.g., priority, hang time, broadcast mode).
[R-6.15.5.3-005] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure whether MCX Service Ad hoc Group Communication is allowed on the MCX system regardless of individual MCX User authorizations.
[[SUGGESTION_START]][R-6.15.5.3-006] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users, are authorized to combine Ad hoc Group Communications. [[SUGGESTION_END]]
Next change
6.15.5.4 Notification and acknowledgement for MCX Service Ad hoc Group Communications
[R-6.15.5.4-001] The MCX Service shall provide mechanisms for notification and acknowledgement of MCX Service Ad hoc Group Communications as defined in section 6.2.1.
[[SUGGESTION_START]][R-6.15.5.4-001a] [[SUGGESTION_END]][[SUGGESTION_START]]The notification and acknowledgement [[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]]hall include [[SUGGESTION_END]][[SUGGESTION_START]]the corresponding Functional Alias[[SUGGESTION_END]] [[SUGGESTION_START]]for each MCPTT ID, if available[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
NOTE: For MCX Service Ad hoc Group Communications a participant is considered an affiliated member of the group during communication setup.
[[SUGGESTION_START]][R-6.15.5.4-002] When the list of participants is determined and updated by the MCX Service system, the MCX Service shall provide notifications to authorized users[[SUGGESTION_END]][[SUGGESTION_START]], [[SUGGESTION_END]][[SUGGESTION_START]]including,[[SUGGESTION_END]][[SUGGESTION_START]] if [[SUGGESTION_END]][[SUGGESTION_START]]available[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] the [[SUGGESTION_END]][[SUGGESTION_START]]corresponding [[SUGGESTION_END]][[SUGGESTION_START]]Functional Aliases[[SUGGESTION_END]][[SUGGESTION_START]] of t[[SUGGESTION_END]][[SUGGESTION_START]]he[[SUGGESTION_END]][[SUGGESTION_START]] determined[[SUGGESTION_END]][[SUGGESTION_START]] participants[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
Next change
Annex A (normative):
MCCoRe Requirements for MCPTT
Table A.1 provides an exhaustive list of those requirements in 3GPP TS 22.280 that are applicable to MCPTT.
Table A.1
5 MCX Service Requirements common for on the network and off the network
NA
5.1 General Group Communications Requirements
NA
5.1.1 General aspects
R-5.1.1-001
R-5.1.1-002
R-5.1.1-003
R-5.1.1-004
R-5.1.1-005
5.1.2 Group/status information
R-5.1.2-001
R-5.1.2-002
5.1.3 Group configuration
R-5.1.3-001
R5.1.3-002
5.1.4 Identification
R-5.1.4-001
5.1.5 Membership/affiliation
R-5.1.5-001
R-5.1.5-002
R-5.1.5-003
R-5.1.5-004
R-5.1.5-005
R-5.1.5-006
R-5.1.5-007
R-5.1.5-008
5.1.6 Group Communication administration
R-5.1.6-001
5.1.7 Prioritization
R-5.1.7-001
R-5.1.7-002
5.1.8 Charging requirements for MCX Service
R-5.1.8-001
R-5.1.8-003
R-5.1.8-004
R-5.1.8-005
R-5.1.8-006
R-5.1.8-007
R-5.1.8-008
R-5.1.8-009
R-5.1.8-010
R-5.1.8-011
5.1.9 MCX Service Emergency Alert triggered by location
NA
5.2 Broadcast Group
NA
5.2.1 General Broadcast Group Communication
R-5.2.1-001
R-5.2.1-002
5.2.2 Group-Broadcast Group (e.g., announcement group)
R-5.2.2-001
R-5.2.2-002
R-5.2.2-003
R-5.2.2-004
5.2.3 User-Broadcast Group (e.g., System Communication)
R-5.2.3-001
R-5.2.3-002
5.3 Late communication entry
R-5.3-001
R-5.3-002
R-5.3-003
R-5.3-004
R-5.3-005
5.4 Receiving from multiple MCX Service communications
5.4.1 Overview
NA
5.4.2 Requirements
R-5.4.2-001
R-5.4.2-002
R-5.4.2-003
R-5.4.2-004
R-5.4.2-004A
R-5.4.2-004B
R-5.4.2-005
R-5.4.2-006
R-5.4.2-007
R-5.4.2-007a
R-5.4.2-008
R-5.4.2-009
5.5 Private Communication
NA
5.5.1 Private Communication general requirements
NA
5.5.2 Charging requirement for MCX Service
R-5.5.2-001
5.6 MCX Service priority requirements
NA
5.6.1 Overview
NA
5.6.2 Communication types based on priorities
NA
5.6.2.1 MCX Service Emergency and Imminent Peril general requirements
NA
5.6.2.1.1 Overview
NA
5.6.2.1.2 Requirements
R-5.6.2.1.2-001
R-5.6.2.1.2-002
R-5.6.2.1.2-003
R-5.6.2.1.2-004
R-5.6.2.1.2-005
5.6.2.2 MCX Service Emergency Group Communication
NA
5.6.2.2.1 MCX Service Emergency Group Communication requirements
R-5.6.2.2.1-001
R-5.6.2.2.1-002
R-5.6.2.2.1-003
R-5.6.2.2.1-004
R-5.6.2.2.1-005
R-5.6.2.2.1-006
R-5.6.2.2.1-007
R-5.6.2.2.1-008
R-5.6.2.2.1-009
R-5.6.2.2.1-010
R-5.6.2.2.1-011
R-5.6.2.2.1-012
R-5.6.2.2.1-013
R-5.6.2.2.1-014
5.6.2.2.2 MCX Service Emergency Group Communication cancellation requirements
R-5.6.2.2.2-001
R-5.6.2.2.2-002
R-5.6.2.2.2-003
R-5.6.2.2.2-004
R-5.6.2.2.2-005
5.6.2.3 MCX Service Imminent Peril Group
NA
5.6.2.3.1 MCX Service Imminent Peril Group Communication requirements
R-5.6.2.3.1-001
R-5.6.2.3.1-002
R-5.6.2.3.1-003
R-5.6.2.3.1-004
R-5.6.2.3.1-005
R-5.6.2.3.1-006
R-5.6.2.3.1-007
R-5.6.2.3.1-008
R-5.6.2.3.1-009
5.6.2.3.2 MCX Service Imminent Peril Group Communications cancellation requirements
R-5.6.2.3.2-001
R-5.6.2.3.2-002
R-5.6.2.3.2-003
R-5.6.2.3.2-004
5.6.2.4 MCX Service Emergency Alert
NA
5.6.2.4.1 MCX Service Emergency Alert requirements
R-5.6.2.4.1-001
R-5.6.2.4.1-002
R-5.6.2.4.1-003
R-5.6.2.4.1-004
R-5.6.2.4.1-004a
R-5.6.2.4.1-005
R-5.6.2.4.1-006
R-5.6.2.4.1-007
R-5.6.2.4.1-008
R-5.6.2.4.1-009
R-5.6.2.4.1-010
R-5.6.2.4.1-011
R-5.6.2.4.1-012
R-5.6.2.4.1-013
5.6.2.4.2 MCX Service Emergency Alert cancellation requirements
R-5.6.2.4.2-001
R-5.6.2.4.2-002
R-5.6.2.4.2-003
5.7 MCX Service User ID
R-5.7-001
R-5.7-002
R-5.7-003
5.8 MCX UE Management
R-5.8-001
R-5.8-002
5.9 MCX Service User Profile
R-5.9-001
R-5.9-002
5.9A Functional alias
R-5.9a-001
R-5.9a-001a
R-5.9a-001b
R-5.9a-001c
R-5.9a-002
R-5.9a-002a
R-5.9a-003
R-5.9a-004
R-5.9a-005
R-5.9a-006
R-5.9a-007
R-5.9a-008
R-5.9a-009
R-5.9a-010
R-5.9a-011
R-5.9a-012
R-5.9a-013
R-5.9a-014
R-5.9a-015
R-5.9a-016
R-5.9a-017
R-5.9a-018
R-5.9a-019
R-5.9a-020
R-5.9a-021
R-5.9a-022
R-5.9a-023
[R-5.9a-024
R-5.9a-025
R-5.9a-026
R-5.9a-027
R-5.9a-028
R-5.9a-029
R-5.9a-030
R-5.9a-031
5.10 Support for multiple devices
R-5.10-001
R-5.10-001a
R-5.10-002
5.11 Location
R-5.11-001
R-5.11-002
R-5.11-002a
R-5.11-003
R-5.11-004
R-5.11-005
R-5.11-006
R-5.11-007
R-5.11-008
R-5.11-009
R-5.11-010
R-5.11-011
R-5.11-013
R-5.11-014
R-5.11-015
R-5.11-015
5.12 Security
R-5.12-001
R-5.12-002
R-5.12-003
R-5.12-004
R-5.12-005
R-5.12-006
R-5.12-007
R-5.12-008
R-5.12-009
R-5.12-010
R-5.12-011
R-5.12-012
R-5.12-013
R5-12-014
5.13 Media quality
R-5.13-001
5.14 Relay requirements
R-5.14-001
R-5.14-002
R-5.14-003
R-5.14-004
5.15 Gateway requirements
R-5.15-001
R-5.15-002
R-5.15-003
5.16 Control and management by Mission Critical Organizations
NA
5.16.1 Overview
NA
5.16.2 General requirements
R-5.16.2-001
R-5.16.2-002
R-5.16.2-003
R-5.16.2-004
R-5.16.2-005
5.16.3 Operational visibility for Mission Critical Organizations
R-5.16.3-001
5.17 General administrative – groups and users
R-5.17-001
R-5.17-002
R-5.17-003
R-5.17-004
R-5.17-005
R-5.17-006
R-5.17-007
R-5.17-008
5.18 Open interfaces for MCX services
NA
5.18.1 Overview
NA
5.18.2 Requirements
NA
5.19 Media forwarding
NA
5.19.1 Service description
NA
5.19.2 Requirements
NA
5.20 Receipt notification
NA
5.20.1 Service description
NA
5.20.2 Requirements
NA
5.21 Additional services for MCX Service communications
NA
5.21.1 Remotely initiated MCX Service communication
NA
5.21.1.1 Overview
NA
5.21.1.2 Requirements
NA
5.21.2 Remotely terminated MCX Service communication
NA
5.21.2.1 Requirements
R-5.21.2.1-001
6 MCX Service requirements specific to on-network use
NA
6.1 General administrative – groups and users
R-6.1-001
R-6.1-002
R-6.1-003
R-6.1-004
R-6.1-005
6.2 MCX Service communications
NA
6.2.1 Notification and acknowledgement for MCX Service Group Communications
NA
6.2.2 Queuing
R-6.2.2-001
R-6.2.2-002
R-6.2.2-003
R-6.2.2-004
R-6.2.2-005
R-6.2.2-006
6.3 General requirements
R-6.3-001
R-6.3-002
R-6.3-003
R-6.3-004
6.4 General group communication
NA
6.4.1 General aspects
R-6.4.1-001
6.4.2 Group status/information
R-6.4.2-005
R-6.4.2-001
R-6.4.2-002
R-6.4.2-003
R-6.4.2-004
R-6.4.2-006
R-6.4.2-007
6.4.3 Identification
R-6.4.3-001
R-6.4.3-002
6.4.4 Membership/affiliation
R-6.4.4-001
R-6.4.4-002
R-6.4.4-002a
R-6.4.4-003
R-6.4.4-004
6.4.5 Membership/affiliation list
R-6.4.5-001
R-6.4.5-002
R-6.4.5-003
R-6.4.5-003a
R-6.4.5-004
R-6.4.5-005
R-6.4.5-006
R-6.4.5-007
R-6.4.5-008
6.4.6 Authorized user remotely changes another MCX User’s affiliated and/or Selected MCX Service Group(s)
NA
6.4.6.1 Mandatory change
R-6.4.6.1-001
R-6.4.6.1-002
R-6.4.6.1-003
R-6.4.6.1-004
6.4.6.2 Negotiated change
R-6.4.6.2-001
R-6.4.6.2-002
R-6.4.6.2-003
R-6.4.6.2-004
R-6.4.6.2-005
R-6.4.6.2-006
6.4.7 Prioritization
R-6.4.7-001
R-6.4.7-002
R-6.4.7-003
R-6.4.7-004
6.4.8 Relay requirements
R-6.4.8-001
6.4.9 Administrative
R-6.4.9-001
R-6.4.9-002
R-6.4.9-003
R-6.4.9-004
R-6.4.9-005
R-6.4.9-006
6.5 Broadcast Group
NA
6.5.1 General Broadcast Group Communication
R-6.5.1-001
R-6.5.1-002
6.5.2 Group-Broadcast Group (e.g., announcement group)
R-6.5.2-001
6.5.3 User-Broadcast Group (e.g., system communication)
R-6.5.3-001
6.6 Dynamic group management (i.e., dynamic reporting)
NA
6.6.1 General dynamic regrouping
R-6.6.1-001
R-6.6.1-002
R-6.6.1-003
R-6.6.1-004
R-6.6.1-005
R-6.6.1-006
6.6.2 Group regrouping
NA
6.6.2.1 Service description
NA
6.6.2.2 Requirements
R-6.6.2.2-001
R-6.6.2.2-002
R-6.6.2.2-003
R-6.6.2.2-004
R-6.6.2.2-005
R-6.6.2.2-006
R-6.6.2.2-007
R-6.6.2.2-008
R-6.6.2.2-009
R-6.6.2.2-010
R-6.6.2.2-011
R-6.6.2.2-012
R-6.6.2.2-013
6.6.3 Temporary Broadcast Groups
R-6.6.3-001
R-6.6.3-001a
R-6.6.3-001b
R-6.6.3-002
6.6.4 User regrouping
NA
6.6.4.1 Service description
NA
6.6.4.2 Requirements
R-6.6.4.2-001
R-6.6.4.2-002
R-6.6.4.2-002a
R-6.6.4.2-002b
R-6.6.4.2-003
R-6.6.4.2-004
R-6.6.4.2-005
6.6.5 Dynamic Group Participation
NA
6.6.5.1 Service description
NA
6.6.5.2 Requirements
R-6.6.5.2-001
R-6.6.5.2-002
R-6.6.5.2-003
R-6.6.5.2-004
R-6.6.5.2-005
R-6.654.2-006
R-6.6.5.2-007
R-6.6.5.2-008
6.7 Private Communication
NA
6.7.1 Overview
NA
6.7.2 General requirements
R-6.7.2-001
R-6.7.2-002
R-6.7.2-003
R-6.7.2-004
R-6.7.2-005
R-6.7.2-006
6.7.3 Administrative
R-6.7.3-001
R-6.7.3-002
R-6.7.3-003
R-6.7.3-004
R-6.7.3-005
R-6.7.3-006
R-6.7.3-007
R-6.7.3-007a
R-6.7.3-008
6.7.4 Prioritization
R-6.7.4-001
R-6.7.4-002
R-6.7.4-003
R-6.7.4-004
R-6.7.4-005
R-6.7.4-006
R-6.7.4-007
6.7.5 Private Communication (without Floor control) commencement requirements
R-6.7.5-001
R-6.7.5-002
R-6.7.5-003
6.7.6 Private Communication (without Floor control) termination
R-6.7.6-001
R-6.7.6-002
6.8 MCX Service priority requirements
NA
6.8.1 General
R-6.8.1-001
R-6.8.1-002
R-6.8.1-003
R-6.8.1-004
R-6.8.1-005
R-6.8.1-006
R-6.8.1-007
R-6.8.1-008
R-6.8.1-009
R-6.8.1-010
R-6.8.1-011
R-6.8.1-012
R-6.8.1-013
R-6.8.1-014
R-6.8.1-015
R-6.8.1-016
6.8.2 3GPP system access controls
R-6.8.2-001
6.8.3 3GPP system admission controls
R-6.8.3-001
6.8.4 3GPP system scheduling controls
R-6.8.4-001
6.8.5 UE access controls
R-6.8.5-001
6.8.6 Mobility and load management
NA
6.8.6.1 Mission Critical mobility management according to priority
R-6.8.6.1-001
R-6.8.6.1-002
6.8.6.2 Load management
R-6.8.6.2-001
R-6.8.6.2-002
R-6.8.6.2-003
R-6.8.6.2-004
R-6.8.6.2-005
6.8.7 Application layer priorities
NA
6.8.7.1 Overview
NA
6.8.7.2 Requirements
R-6.8.7.2-001
R-6.8.7.2-002
R-6.8.7.2-003
R-6.8.7.2-004
R-6.8.7.2-005
R-6.8.7.2-006
R-6.8.7.2-007
R-6.8.7.2-008
R-6.8.7.2-009
R-6.8.7.2-010
6.8.8 Communication types based on priorities
NA
6.8.8.1 MCX Service Emergency Group Communication requirements
R-6.8.8.1-001
R-6.8.8.1-002
R-6.8.8.1-003
R-6.8.8.1-004
6.8.8.2 MCX Service Emergency Private Communication requirements
NA
6.8.8.3 Imminent Peril Group Communication requirements
R-6.8.8.3-001
R-6.8.8.3-002
R-6.8.8.3-003
6.8.8.4 MCX Service Emergency Alert
NA
6.8.8.4.1 Requirements
R-6.8.8.4.1-001
R-6.8.8.4.1-002
R-6.8.8.4.1-003
R-6.8.8.4.1-004
R-6.8.8.4.1-005
R-6.8.8.4.1-006
6.8.8.4.2 MCX Service Emergency Alert cancellation requirements
R-6.8.8.4.2-001
R-6.8.8.4.2-002
6.8.8.X Ad hoc Group Communication requirements
R-6.8.8.X-001
6.9 IDs and aliases
R-6.9-001
R-6.9-002
R-6.9-003
R-6.9-004
6.10 User Profile management
R-6.10-001
R-6.10-002
R-6.10-003
R-6.10-004
6.11 Support for multiple devices
R-6.11-001
R-6.11-002
R-6.11-003
6.12 Location
R-6.12-001
R-6.12-002
R-6.12-003
R-6.12-004
R-6.12-005
R-6.12-006
R-6.12-007
6.13 Security
NA
6.13.1 Overview
NA
6.13.2 Cryptographic protocols
R-6.13.2-001
R-6.13.2-002
R-6.13.2-003
6.13.3 Authentication
R-6.13.3-001
6.13.4 Access control
R-6.13.4-001
R-6.13.4-002
R-6.13.4-003
R-6.13.4-004
R-6.13.4-005
R-6.13.4-006
R-6.13.4-007
R-6.13.4-008
R-6.13.4-009
R-6.13.4-010
6.13.5 Regulatory issues
R-6.13.5-001
6.13.6 Storage control
NA
6.14 Interactions for MCX Service Group Communications and MCX Service Private Communications
R-6.14-001
R-6.14-002
6.15 Additional services for MCX Service communications
NA
6.15.1 Discreet listening capabilities
R-6.15.1-001a
R-6.15.1-001
R-6.15.1-002
R-6.15.1-002a
R-6.15.1-003
R-6.15.1-004
6.15.2 Ambient listening
NA
6.15.2.1 Overview of ambient listening
NA
6.15.2.2 Ambient listening requirements
NA
6.15.2.2.1 General ambient listening requirements
R-6.15.2.2.1-001
R-6.15.2.2.1-002
R-6.15.2.2.1-003
6.15.2.2.2 Remotely initiated ambient listening requirements
R-6.15.2.2.2-001
R-6.15.2.2.2-002
6.15.2.2.3 Locally initiated ambient listening requirements
R-6.15.2.2.3-001
R-6.15.2.2.3-002
6.15.3 Remotely initiated MCX Service Communication
NA
6.15.3.1 Overview
NA
6.15.3.2 Requirements
R-6.15.3.2-001
R-6.15.3.2-002
R-6.15.3.2-003
R-6.15.3.2-004
6.15.4 Recording and audit requirements
R-6.15.4-001
R-6.15.4-002
R-6.15.4-003
R-6.15.4-004
R-6.15.4-005
R-6.15.4-006
R-6.15.4-007
R-6.15.4-008
R-6.15.4-009
R-6.15.4-010
R-6.15.4-011
6.15.5 MCX Service Ad hoc Group Communication
NA
6.15.5.1 Overview
NA
6.15.5.2 General Aspects
R-6.15.5.2-001
R-6.15.5.2-001a
R-6.15.5.2-001b
R-6.15.5.2-001c
[[SUGGESTION_START]]R-6.15.5.2-001d [[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]2[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]3[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]4[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]5[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]6[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]7[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]8[[SUGGESTION_END]]
R-6.15.5.2-0[[SUGGESTION_START]]09[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]0[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]1[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]2[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]3[[SUGGESTION_END]]
R-6.15.5.2-014
R-6.15.5.2-01[[SUGGESTION_START]]4a[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]5[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]6[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.5.2-017[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.5.2-018[[SUGGESTION_END]]
6.15.5.3 Administrative
R-6.15.5.3-001
R-6.15.5.3-002
R-6.15.5.3-003
R-6.15.5.3-004
R-6.15.5.3-005
[[SUGGESTION_START]]R-[[SUGGESTION_END]][[SUGGESTION_START]]6.15.5.3-006[[SUGGESTION_END]]
6.15.5.4 Notification and acknowledgement for MCX Service Ad hoc Group Communications
R-6.15.5.4-001
[[SUGGESTION_START]]R-6.15.5.4-001a[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.5.4-002[[SUGGESTION_END]]
6.15.6 MCX Service Ad hoc Group Emergency Alert
NA
6.15.6.1 Overview
NA
6.15.6.2 General aspects
R-6.15.6.2-001
R-6.15.6.2-002
R-6.15.6.2-002a
R-6.15.6.2-003
R-6.15.6.2-004
R-6.15.6.2-005
R-6.15.6.2-005a
R-6.15.6.2-005b
R-6.15.6.2-006
R-6.15.6.2-007
R-6.15.6.2-008
6.15.6.3 Administrative
R-6.15.6.3-001
R-6.15.6.3-002
R-6.15.6.3-003
R-6.15.6.3-004
R-6.15.6.3-005
6.16 Interaction with telephony services
R-6.16-001
R-6.16-002
6.17 Interworking
NA
6.17.1 Non-3GPP access
R-6.17.1-001
6.17.2 Interworking between MCX Service systems
R-6.17.2-001
R-6.17.2-002
R-6.17.2-003
R-6.17.2-004
R-6.17.2-005
R-6.17.2-006
R-6.17.2-007
R-6.17.2-008
6.17.3 Interworking with non-MCX Service systems
NA
6.17.3.1 GSM-R
R-6.17.3.1-001
R-6.17.3.1-002
R-6.17.3.1-003
R-6.17.3.1-004
R-6.17.3.1-005
6.17.3.2 External systems
R.6.17.3.2-001
R.6.17.3.2-002
6.18 MCX Service coverage extension using ProSe UE-to-Network Relays
R-6.18-001
R-6.18-002
R-6.18-003
R-6.18-004
R-6.18-005
R-6.18-006
6.19 Additional MCX Service requirements
NA
6.19.1 Communication rejection and queuing
NA
6.19.1.1 Requirements
R-6.19.1.1-001
R-6.19.1.1-002
R-6.19.1.1-003
R-6.19.1.1-004
R-6.19.1.1-005
R-6.19.1.1-006
R-6.19.1.1-007
7 MCX Service requirements specific to off-network use
NA
7.1 Off-network communications overview
NA
7.2 General off-network MCX Service requirements
R-7.2-001
R-7.2-002
R-7.2-003
R-7.2-004
R-7.2-005
7.3 Admission control
NA
7.3.1 General aspects
R-7.3.1-001
R-7.3.1-002
R-7.3.1-003
7.3.2 Communication initiation
R-7.3.2-001
R-7.3.2-002
R-7.3.2-003
R-7.3.2-004
R-7.3.2-005
7.4 Communication termination
R-7.4-001
R-7.4-002
R-7.4-003
R-7.4-004
7.5 Broadcast Group
R-7.5-001
R-7.5-002
7.6 MCX Service priority requirements
R-7.6-001
R-7.6-002
R-7.6-003
R-7.6-004
R-7.6-005
R-7.6-006
R-7.6-007
R-7.6-008
R-7.6-009
7.7 Communication types based on priorities
NA
7.7.1 MCX Service Emergency Group Communication requirements
R-7.7.1-001
R-7.7.1-002
R-7.7.1-003
7.7.2 MCX Service Emergency Group Communication cancellation requirements
R-7.7.2-001
7.7.3 Imminent Peril Communication
NA
7.7.3.1 Imminent Peril Group Communication requirements
R-7.7.3.1-001
R-7.7.3.1-002
R-7.7.3.1-003
R-7.7.3.1-004
R-7.7.3.1-005
7.7.3.2 Imminent Peril Group Communication cancellation requirements
R-7.7.3.2-001
R-7.7.3.2-002
7.8 Location
R-7.8-001
R-7.8-002
R-7.8-003
7.9 Security
R-7.9-001
R-7.9-002
7.10 Off-network MCX Service operations
R-7.10-001
R-7.10-002
R-7.10-003
7.11 Off-network UE functionality
R-7.11-001
R-7.11-002
R-7.11-003
7.12 Streaming for ProSe UE-to-UE Relay and UE-to-Network Relay
NA
7.12.1 UE-to-Network Relay for all data types
R-7.12.1-001
R-7.12.1-002
R-7.12.1-003
R-7.12.1-004
7.12.2 UE-to-UE Relay streaming
R-7.12.2-001
R-7.12.2-002
R-7.12.2-003
7.12.3 Off-Network streaming
R-7.12.3-001
R-7.12.3-002
R-7.12.3-003
7.13 Switching to off-network MCX Service
R-7.13-001
R-7.13-002
R-7.13-003
R-7.13-004
R-7.13-005
7.14 Off-network recording and audit requirements
R-7.14-001
R-7.14-001a
R-7.14-002
R-7.14-002a
7.15 Off-network UE-to-UE relay
NA
7.15.1 Private Communications
R-7.15.1-001
R-7.15.1-002
R-7.15.1-003
7.15.2 Group Communications
R-7.15.2-001
R-7.15.2-002
7.16 Off-network Ad hoc Group Communication
R-7.16-001
8 Inter-MCX Service interworking
NA
8.1 Inter-MCX Service interworking overview
NA
8.2 Concurrent operation of different MCX Services
NA
8.2.1 Overview
NA
8.2.2 Requirements
R-8.2.2-001
R-8.2.2-002
R-8.2.2-003
R-8.2.2-004
R-8.2.2-005
R-8.2.2-006
R-8.2.2-007
8.3 Use of unsharable resources within a UE
R-8.3-001
R-8.3-002
R-8.3-003
R-8.3-004
R-8.3-005
R-8.3-006
8.4 Single group with multiple MCX Services
NA
8.4.1 Overview
NA
8.4.2 Requirements
R-8.4.2-001
R-8.4.2-002
R-8.4.2-003
R-8.4.2-004
R-8.4.2-005
8.4.3 Compatibility of UE
NA
8.4.3.1 Advertising service capabilities required
R-8.4.3.1-001
R-8.4.3.1-002
R-8.4.3.1-003
R-8.4.3.1-004
8.4.3.2 Conversion between capabilities
R-8.4.3.2-001
8.4.4 Individual permissions for service access
R-8.4.4-001
8.4.5 Common alias and user identities or mappable
R-8.4.5-001
8.4.6 Single location message
R-8.4.6-001
R-8.4.6-002
R-8.4.6-003
8.5 Priority between services
NA
8.5.1 Overview
NA
8.5.2 Requirements
R-8.5.2-001
R-8.5.2-002
R-8.5.2-003
R-8.5.2-004
R-8.5.2-005
9 Air Ground Air Communications
NA
9.1 Service description
NA
9.2 Requirements
R-9.2-001
10 MCX Service in IOPS mode
R-10-001
End of changes
|
S1-253252.zip
|
2026-01-13T17:29:55.675113
|
S1-253263
|
SA1
|
TSGS1_111_Goteborg
|
CR
|
revised
|
FRMCS_Ph6 – Normative [SP-250277]
|
3GPP TSG-SA WG1 Meeting #111 S1-253263
Gothenburg, Sweden, 25-29 August 2025 (revision of S1-xxxxxx)
CR-Form-v12.3
CHANGE REQUEST
22.280
CR
0180
rev
-
Current version:
20.0.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
X
Radio Access Network
Core Network
X
Title:
Addition of Functional Aliases in the notifications of AHG emergency alert
Combining AHG emergency alerts
Source to WG:
UIC, Nokia
Source to TSG:
S1
Work item code:
FRMCS_Ph6-REQ
Date:
2025-08-14
Category:
C
Release:
Rel-20
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
Enhance alignement with functional requirements of UIC specifications (FU-7120 v2.1.2) related to the usage of functional aliases of participants of an AHG emergency alert, based on criteria and combining of AHG emergency allerts.
Summary of change:
Addition of the functional aliases of the receiving participants in the notifications of the AHG emergency alert
Combining AHG emergency alerts (authorizations, configurations)
Consequences if not approved:
Misalignement with functional requirements of the UIC specifications
Missing stage-1 requirements for downstream groups to include the relevant features
Clauses affected:
6.15.6.2, 6.15.6.3, 6.15.6.4, Annex A
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
Start of changes
6.15.6.2 General aspects
[R-6.15.6.2-001] The MCX Service shall support an MCX Service Ad hoc Group Emergency Alert capability, which on initiation by an MCX User causes that MCX UE to send an MCX Service Ad hoc Group Emergency Alert and may put that MCX User into the MCX Service Emergency State.
[R-6.15.6.2-002] The MCX Service shall provide a means for an authorized user to be able to activate the MCX Service Ad hoc Group Emergency Alert capability.
[R-6.15.6.2-002a] The MCX Service shall provide the reason for denial to an MCX user who was not authorised to activate the MCX Service Ad hoc Group Emergency Alert.
[R-6.15.6.2-003] The MCX Service Emergency Alert shall contain the following information: Location information, MCX Service User ID, Functional Alias, Criteria for determining list of participants, available MCX Services, additional information related to the alert, and the user's Mission Critical Organization name.
[R-6.15.6.2-004] The MCX Service shall provide a mechanism for the initiator of an MCX Service Ad hoc Group Emergency Alert to request that the list of participants is to be determined and updated by the MCX Service system using pre-defined criteria.
[R-6.15.6.2-004a] The MCX Service shall provide a mechanism for an authorised user to update the pre-defined criteria during an on-going Ad hoc Group Emergency Alert.
[R-6.15.6.2-005] The MCX Service Ad hoc Group Emergency Alert shall be distributed to the list of participants determined by the MCX Service system.
[R-6.15.6.2-005a] [[SUGGESTION_START]]Void [[SUGGESTION_END]]
[R-6.15.6.2-005b] When the list of participants is determined or updated by the MCX Service system, the MCX Service shall provide a mechanism that monitors and ensures that the participants list is applied for MCX Service Ad hoc Group Emergency Alert, performing retries when needed.
[R-6.15.6.2-006] The MCX Service shall support MCX Service Ad hoc Group Emergency Alert cancellation by authorized MCX Users.
[R-6.15.6.2-007] The MCX Service shall provide a mechanism for an authorised user which is participant of an active MCX Service Ad hoc Group Emergency Alert to set up group communications using the ad hoc group.
[R-6.15.6.2-008] When an MCX Service Ad hoc Group Emergency Alert is cancelled and ongoing calls in the ad hoc group are terminated the ad hoc group shall not persist.
[[SUGGESTION_START]][R-6.15.6.2-009] The MCX Service shall provide a mechanism for an authorised user, to combine [[SUGGESTION_END]][[SUGGESTION_START]]multiple[[SUGGESTION_END]][[SUGGESTION_START]] ongoing Ad hoc Group Emergency Alerts.[[SUGGESTION_END]]
[[SUGGESTION_START]][R-6.15.6.2-010] If there are ongoing Ad hoc Group communications, associated to the combined Ad hoc Group Emergency Alerts, the MCX Service shall provide a mechanism for an authorised user to combine them.[[SUGGESTION_END]]
Next change
6.15.6.3 Administrative
[R-6.15.6.3-001] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users, within their authority, are authorized to initiate a MCX Service Ad hoc Group Emergency Alert.
[R-6.15.6.3-002] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure the maximum number of MCX Users who can participate in a MCX Service Ad hoc Group Emergency Alert.
[R-6.15.6.3-003] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users are authorized to participate in a MCX Service Ad hoc Group Emergency Alert.
[R-6.15.6.3-004] The MCX Service shall provide a mechanism for an MCX Service Administrator to define the default parameters for the ad hoc group resulting from the MCX Service Ad hoc Group Emergency Alert (e.g., priority, hang time, broadcast mode).
[R-6.15.6.3-005] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure whether MCX Service Ad hoc Group Emergency Alert is allowed on the MCX system regardless of individual MCX User authorizations.
[[SUGGESTION_START]][R-6.15.6.3-006] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users, within their authority, are authorized to combine Ad hoc Group Emergency Alerts. [[SUGGESTION_END]]
Next change
[[SUGGESTION_START]]6.15.6.4 [[SUGGESTION_END]][[SUGGESTION_START]]Notification and acknowledgement for MCX Service Ad hoc Group Emergency Alert[[SUGGESTION_END]]
[[SUGGESTION_START]][R-6.15.6.4-001] The MCX Service shall provide mechanisms for notification and acknowledgement of MCX Service Ad hoc Group Emergency Alert, including for each MCPTT ID the corresponding Functional Alias, if available.[[SUGGESTION_END]]
[[SUGGESTION_START]][R-6.15.6.[[SUGGESTION_END]][[SUGGESTION_START]]4[[SUGGESTION_END]][[SUGGESTION_START]]-00[[SUGGESTION_END]][[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]]] When the list of participants is determined and updated by the MCX Service system, the MCX Service shall provide notifications to the relevant participants and authorized users, including, if possible, the Functional aliases of the receiving participants.[[SUGGESTION_END]]
Next change
Annex A (normative):
MCCoRe Requirements for MCPTT
Table A.1 provides an exhaustive list of those requirements in 3GPP TS 22.280 that are applicable to MCPTT.
Table A.1
5 MCX Service Requirements common for on the network and off the network
NA
5.1 General Group Communications Requirements
NA
5.1.1 General aspects
R-5.1.1-001
R-5.1.1-002
R-5.1.1-003
R-5.1.1-004
R-5.1.1-005
5.1.2 Group/status information
R-5.1.2-001
R-5.1.2-002
5.1.3 Group configuration
R-5.1.3-001
R5.1.3-002
5.1.4 Identification
R-5.1.4-001
5.1.5 Membership/affiliation
R-5.1.5-001
R-5.1.5-002
R-5.1.5-003
R-5.1.5-004
R-5.1.5-005
R-5.1.5-006
R-5.1.5-007
R-5.1.5-008
5.1.6 Group Communication administration
R-5.1.6-001
5.1.7 Prioritization
R-5.1.7-001
R-5.1.7-002
5.1.8 Charging requirements for MCX Service
R-5.1.8-001
R-5.1.8-003
R-5.1.8-004
R-5.1.8-005
R-5.1.8-006
R-5.1.8-007
R-5.1.8-008
R-5.1.8-009
R-5.1.8-010
R-5.1.8-011
5.1.9 MCX Service Emergency Alert triggered by location
NA
5.2 Broadcast Group
NA
5.2.1 General Broadcast Group Communication
R-5.2.1-001
R-5.2.1-002
5.2.2 Group-Broadcast Group (e.g., announcement group)
R-5.2.2-001
R-5.2.2-002
R-5.2.2-003
R-5.2.2-004
5.2.3 User-Broadcast Group (e.g., System Communication)
R-5.2.3-001
R-5.2.3-002
5.3 Late communication entry
R-5.3-001
R-5.3-002
R-5.3-003
R-5.3-004
R-5.3-005
5.4 Receiving from multiple MCX Service communications
5.4.1 Overview
NA
5.4.2 Requirements
R-5.4.2-001
R-5.4.2-002
R-5.4.2-003
R-5.4.2-004
R-5.4.2-004A
R-5.4.2-004B
R-5.4.2-005
R-5.4.2-006
R-5.4.2-007
R-5.4.2-007a
R-5.4.2-008
R-5.4.2-009
5.5 Private Communication
NA
5.5.1 Private Communication general requirements
NA
5.5.2 Charging requirement for MCX Service
R-5.5.2-001
5.6 MCX Service priority requirements
NA
5.6.1 Overview
NA
5.6.2 Communication types based on priorities
NA
5.6.2.1 MCX Service Emergency and Imminent Peril general requirements
NA
5.6.2.1.1 Overview
NA
5.6.2.1.2 Requirements
R-5.6.2.1.2-001
R-5.6.2.1.2-002
R-5.6.2.1.2-003
R-5.6.2.1.2-004
R-5.6.2.1.2-005
5.6.2.2 MCX Service Emergency Group Communication
NA
5.6.2.2.1 MCX Service Emergency Group Communication requirements
R-5.6.2.2.1-001
R-5.6.2.2.1-002
R-5.6.2.2.1-003
R-5.6.2.2.1-004
R-5.6.2.2.1-005
R-5.6.2.2.1-006
R-5.6.2.2.1-007
R-5.6.2.2.1-008
R-5.6.2.2.1-009
R-5.6.2.2.1-010
R-5.6.2.2.1-011
R-5.6.2.2.1-012
R-5.6.2.2.1-013
R-5.6.2.2.1-014
5.6.2.2.2 MCX Service Emergency Group Communication cancellation requirements
R-5.6.2.2.2-001
R-5.6.2.2.2-002
R-5.6.2.2.2-003
R-5.6.2.2.2-004
R-5.6.2.2.2-005
5.6.2.3 MCX Service Imminent Peril Group
NA
5.6.2.3.1 MCX Service Imminent Peril Group Communication requirements
R-5.6.2.3.1-001
R-5.6.2.3.1-002
R-5.6.2.3.1-003
R-5.6.2.3.1-004
R-5.6.2.3.1-005
R-5.6.2.3.1-006
R-5.6.2.3.1-007
R-5.6.2.3.1-008
R-5.6.2.3.1-009
5.6.2.3.2 MCX Service Imminent Peril Group Communications cancellation requirements
R-5.6.2.3.2-001
R-5.6.2.3.2-002
R-5.6.2.3.2-003
R-5.6.2.3.2-004
5.6.2.4 MCX Service Emergency Alert
NA
5.6.2.4.1 MCX Service Emergency Alert requirements
R-5.6.2.4.1-001
R-5.6.2.4.1-002
R-5.6.2.4.1-003
R-5.6.2.4.1-004
R-5.6.2.4.1-004a
R-5.6.2.4.1-005
R-5.6.2.4.1-006
R-5.6.2.4.1-007
R-5.6.2.4.1-008
R-5.6.2.4.1-009
R-5.6.2.4.1-010
R-5.6.2.4.1-011
R-5.6.2.4.1-012
R-5.6.2.4.1-013
5.6.2.4.2 MCX Service Emergency Alert cancellation requirements
R-5.6.2.4.2-001
R-5.6.2.4.2-002
R-5.6.2.4.2-003
5.7 MCX Service User ID
R-5.7-001
R-5.7-002
R-5.7-003
5.8 MCX UE Management
R-5.8-001
R-5.8-002
5.9 MCX Service User Profile
R-5.9-001
R-5.9-002
5.9A Functional alias
R-5.9a-001
R-5.9a-001a
R-5.9a-001b
R-5.9a-001c
R-5.9a-002
R-5.9a-002a
R-5.9a-003
R-5.9a-004
R-5.9a-005
R-5.9a-006
R-5.9a-007
R-5.9a-008
R-5.9a-009
R-5.9a-010
R-5.9a-011
R-5.9a-012
R-5.9a-013
R-5.9a-014
R-5.9a-015
R-5.9a-016
R-5.9a-017
R-5.9a-018
R-5.9a-019
R-5.9a-020
R-5.9a-021
R-5.9a-022
R-5.9a-023
[R-5.9a-024
R-5.9a-025
R-5.9a-026
R-5.9a-027
R-5.9a-028
R-5.9a-029
R-5.9a-030
R-5.9a-031
5.10 Support for multiple devices
R-5.10-001
R-5.10-001a
R-5.10-002
5.11 Location
R-5.11-001
R-5.11-002
R-5.11-002a
R-5.11-003
R-5.11-004
R-5.11-005
R-5.11-006
R-5.11-007
R-5.11-008
R-5.11-009
R-5.11-010
R-5.11-011
R-5.11-013
R-5.11-014
R-5.11-015
R-5.11-015
5.12 Security
R-5.12-001
R-5.12-002
R-5.12-003
R-5.12-004
R-5.12-005
R-5.12-006
R-5.12-007
R-5.12-008
R-5.12-009
R-5.12-010
R-5.12-011
R-5.12-012
R-5.12-013
R5-12-014
5.13 Media quality
R-5.13-001
5.14 Relay requirements
R-5.14-001
R-5.14-002
R-5.14-003
R-5.14-004
5.15 Gateway requirements
R-5.15-001
R-5.15-002
R-5.15-003
5.16 Control and management by Mission Critical Organizations
NA
5.16.1 Overview
NA
5.16.2 General requirements
R-5.16.2-001
R-5.16.2-002
R-5.16.2-003
R-5.16.2-004
R-5.16.2-005
5.16.3 Operational visibility for Mission Critical Organizations
R-5.16.3-001
5.17 General administrative – groups and users
R-5.17-001
R-5.17-002
R-5.17-003
R-5.17-004
R-5.17-005
R-5.17-006
R-5.17-007
R-5.17-008
5.18 Open interfaces for MCX services
NA
5.18.1 Overview
NA
5.18.2 Requirements
NA
5.19 Media forwarding
NA
5.19.1 Service description
NA
5.19.2 Requirements
NA
5.20 Receipt notification
NA
5.20.1 Service description
NA
5.20.2 Requirements
NA
5.21 Additional services for MCX Service communications
NA
5.21.1 Remotely initiated MCX Service communication
NA
5.21.1.1 Overview
NA
5.21.1.2 Requirements
NA
5.21.2 Remotely terminated MCX Service communication
NA
5.21.2.1 Requirements
R-5.21.2.1-001
6 MCX Service requirements specific to on-network use
NA
6.1 General administrative – groups and users
R-6.1-001
R-6.1-002
R-6.1-003
R-6.1-004
R-6.1-005
6.2 MCX Service communications
NA
6.2.1 Notification and acknowledgement for MCX Service Group Communications
NA
6.2.2 Queuing
R-6.2.2-001
R-6.2.2-002
R-6.2.2-003
R-6.2.2-004
R-6.2.2-005
R-6.2.2-006
6.3 General requirements
R-6.3-001
R-6.3-002
R-6.3-003
R-6.3-004
6.4 General group communication
NA
6.4.1 General aspects
R-6.4.1-001
6.4.2 Group status/information
R-6.4.2-005
R-6.4.2-001
R-6.4.2-002
R-6.4.2-003
R-6.4.2-004
R-6.4.2-006
R-6.4.2-007
6.4.3 Identification
R-6.4.3-001
R-6.4.3-002
6.4.4 Membership/affiliation
R-6.4.4-001
R-6.4.4-002
R-6.4.4-002a
R-6.4.4-003
R-6.4.4-004
6.4.5 Membership/affiliation list
R-6.4.5-001
R-6.4.5-002
R-6.4.5-003
R-6.4.5-003a
R-6.4.5-004
R-6.4.5-005
R-6.4.5-006
R-6.4.5-007
R-6.4.5-008
6.4.6 Authorized user remotely changes another MCX User’s affiliated and/or Selected MCX Service Group(s)
NA
6.4.6.1 Mandatory change
R-6.4.6.1-001
R-6.4.6.1-002
R-6.4.6.1-003
R-6.4.6.1-004
6.4.6.2 Negotiated change
R-6.4.6.2-001
R-6.4.6.2-002
R-6.4.6.2-003
R-6.4.6.2-004
R-6.4.6.2-005
R-6.4.6.2-006
6.4.7 Prioritization
R-6.4.7-001
R-6.4.7-002
R-6.4.7-003
R-6.4.7-004
6.4.8 Relay requirements
R-6.4.8-001
6.4.9 Administrative
R-6.4.9-001
R-6.4.9-002
R-6.4.9-003
R-6.4.9-004
R-6.4.9-005
R-6.4.9-006
6.5 Broadcast Group
NA
6.5.1 General Broadcast Group Communication
R-6.5.1-001
R-6.5.1-002
6.5.2 Group-Broadcast Group (e.g., announcement group)
R-6.5.2-001
6.5.3 User-Broadcast Group (e.g., system communication)
R-6.5.3-001
6.6 Dynamic group management (i.e., dynamic reporting)
NA
6.6.1 General dynamic regrouping
R-6.6.1-001
R-6.6.1-002
R-6.6.1-003
R-6.6.1-004
R-6.6.1-005
R-6.6.1-006
6.6.2 Group regrouping
NA
6.6.2.1 Service description
NA
6.6.2.2 Requirements
R-6.6.2.2-001
R-6.6.2.2-002
R-6.6.2.2-003
R-6.6.2.2-004
R-6.6.2.2-005
R-6.6.2.2-006
R-6.6.2.2-007
R-6.6.2.2-008
R-6.6.2.2-009
R-6.6.2.2-010
R-6.6.2.2-011
R-6.6.2.2-012
R-6.6.2.2-013
6.6.3 Temporary Broadcast Groups
R-6.6.3-001
R-6.6.3-001a
R-6.6.3-001b
R-6.6.3-002
6.6.4 User regrouping
NA
6.6.4.1 Service description
NA
6.6.4.2 Requirements
R-6.6.4.2-001
R-6.6.4.2-002
R-6.6.4.2-002a
R-6.6.4.2-002b
R-6.6.4.2-003
R-6.6.4.2-004
R-6.6.4.2-005
6.6.5 Dynamic Group Participation
NA
6.6.5.1 Service description
NA
6.6.5.2 Requirements
R-6.6.5.2-001
R-6.6.5.2-002
R-6.6.5.2-003
R-6.6.5.2-004
R-6.6.5.2-005
R-6.654.2-006
R-6.6.5.2-007
R-6.6.5.2-008
6.7 Private Communication
NA
6.7.1 Overview
NA
6.7.2 General requirements
R-6.7.2-001
R-6.7.2-002
R-6.7.2-003
R-6.7.2-004
R-6.7.2-005
R-6.7.2-006
6.7.3 Administrative
R-6.7.3-001
R-6.7.3-002
R-6.7.3-003
R-6.7.3-004
R-6.7.3-005
R-6.7.3-006
R-6.7.3-007
R-6.7.3-007a
R-6.7.3-008
6.7.4 Prioritization
R-6.7.4-001
R-6.7.4-002
R-6.7.4-003
R-6.7.4-004
R-6.7.4-005
R-6.7.4-006
R-6.7.4-007
6.7.5 Private Communication (without Floor control) commencement requirements
R-6.7.5-001
R-6.7.5-002
R-6.7.5-003
6.7.6 Private Communication (without Floor control) termination
R-6.7.6-001
R-6.7.6-002
6.8 MCX Service priority requirements
NA
6.8.1 General
R-6.8.1-001
R-6.8.1-002
R-6.8.1-003
R-6.8.1-004
R-6.8.1-005
R-6.8.1-006
R-6.8.1-007
R-6.8.1-008
R-6.8.1-009
R-6.8.1-010
R-6.8.1-011
R-6.8.1-012
R-6.8.1-013
R-6.8.1-014
R-6.8.1-015
R-6.8.1-016
6.8.2 3GPP system access controls
R-6.8.2-001
6.8.3 3GPP system admission controls
R-6.8.3-001
6.8.4 3GPP system scheduling controls
R-6.8.4-001
6.8.5 UE access controls
R-6.8.5-001
6.8.6 Mobility and load management
NA
6.8.6.1 Mission Critical mobility management according to priority
R-6.8.6.1-001
R-6.8.6.1-002
6.8.6.2 Load management
R-6.8.6.2-001
R-6.8.6.2-002
R-6.8.6.2-003
R-6.8.6.2-004
R-6.8.6.2-005
6.8.7 Application layer priorities
NA
6.8.7.1 Overview
NA
6.8.7.2 Requirements
R-6.8.7.2-001
R-6.8.7.2-002
R-6.8.7.2-003
R-6.8.7.2-004
R-6.8.7.2-005
R-6.8.7.2-006
R-6.8.7.2-007
R-6.8.7.2-008
R-6.8.7.2-009
R-6.8.7.2-010
6.8.8 Communication types based on priorities
NA
6.8.8.1 MCX Service Emergency Group Communication requirements
R-6.8.8.1-001
R-6.8.8.1-002
R-6.8.8.1-003
R-6.8.8.1-004
6.8.8.2 MCX Service Emergency Private Communication requirements
NA
6.8.8.3 Imminent Peril Group Communication requirements
R-6.8.8.3-001
R-6.8.8.3-002
R-6.8.8.3-003
6.8.8.4 MCX Service Emergency Alert
NA
6.8.8.4.1 Requirements
R-6.8.8.4.1-001
R-6.8.8.4.1-002
R-6.8.8.4.1-003
R-6.8.8.4.1-004
R-6.8.8.4.1-005
R-6.8.8.4.1-006
6.8.8.4.2 MCX Service Emergency Alert cancellation requirements
R-6.8.8.4.2-001
R-6.8.8.4.2-002
6.8.8.X Ad hoc Group Communication requirements
R-6.8.8.X-001
6.9 IDs and aliases
R-6.9-001
R-6.9-002
R-6.9-003
R-6.9-004
6.10 User Profile management
R-6.10-001
R-6.10-002
R-6.10-003
R-6.10-004
6.11 Support for multiple devices
R-6.11-001
R-6.11-002
R-6.11-003
6.12 Location
R-6.12-001
R-6.12-002
R-6.12-003
R-6.12-004
R-6.12-005
R-6.12-006
R-6.12-007
6.13 Security
NA
6.13.1 Overview
NA
6.13.2 Cryptographic protocols
R-6.13.2-001
R-6.13.2-002
R-6.13.2-003
6.13.3 Authentication
R-6.13.3-001
6.13.4 Access control
R-6.13.4-001
R-6.13.4-002
R-6.13.4-003
R-6.13.4-004
R-6.13.4-005
R-6.13.4-006
R-6.13.4-007
R-6.13.4-008
R-6.13.4-009
R-6.13.4-010
6.13.5 Regulatory issues
R-6.13.5-001
6.13.6 Storage control
NA
6.14 Interactions for MCX Service Group Communications and MCX Service Private Communications
R-6.14-001
R-6.14-002
6.15 Additional services for MCX Service communications
NA
6.15.1 Discreet listening capabilities
R-6.15.1-001a
R-6.15.1-001
R-6.15.1-002
R-6.15.1-002a
R-6.15.1-003
R-6.15.1-004
6.15.2 Ambient listening
NA
6.15.2.1 Overview of ambient listening
NA
6.15.2.2 Ambient listening requirements
NA
6.15.2.2.1 General ambient listening requirements
R-6.15.2.2.1-001
R-6.15.2.2.1-002
R-6.15.2.2.1-003
6.15.2.2.2 Remotely initiated ambient listening requirements
R-6.15.2.2.2-001
R-6.15.2.2.2-002
6.15.2.2.3 Locally initiated ambient listening requirements
R-6.15.2.2.3-001
R-6.15.2.2.3-002
6.15.3 Remotely initiated MCX Service Communication
NA
6.15.3.1 Overview
NA
6.15.3.2 Requirements
R-6.15.3.2-001
R-6.15.3.2-002
R-6.15.3.2-003
R-6.15.3.2-004
6.15.4 Recording and audit requirements
R-6.15.4-001
R-6.15.4-002
R-6.15.4-003
R-6.15.4-004
R-6.15.4-005
R-6.15.4-006
R-6.15.4-007
R-6.15.4-008
R-6.15.4-009
R-6.15.4-010
R-6.15.4-011
6.15.5 MCX Service Ad hoc Group Communication
NA
6.15.5.1 Overview
NA
6.15.5.2 General Aspects
R-6.15.5.2-001
R-6.15.5.2-001a
R-6.15.5.2-001b
R-6.15.5.2-001c
R-6.15.5.2-002
R-6.15.5.2-003
R-6.15.5.2-004
R-6.15.5.2-005
R-6.15.5.2-006
R-6.15.5.2-007
R-6.15.5.2-008
R-6.15.5.2-009
R-6.15.5.2-010
R-6.15.5.2-011
R-6.15.5.2-012
R-6.15.5.2-013
R-6.15.5.2-014
R-6.15.5.2-014a
R-6.15.5.2-015
R-6.15.5.2-016
R-6.15.5.2-017
6.15.5.3 Administrative
R-6.15.5.3-001
R-6.15.5.3-002
R-6.15.5.3-003
R-6.15.5.3-004
R-6.15.5.3-005
6.15.5.4 Notification and acknowledgement for MCX Service Ad hoc Group Communications
R-6.15.5.4-001
6.15.6 MCX Service Ad hoc Group Emergency Alert
NA
6.15.6.1 Overview
NA
6.15.6.2 General aspects
R-6.15.6.2-001
R-6.15.6.2-002
R-6.15.6.2-002a
R-6.15.6.2-003
R-6.15.6.2-004
R-6.15.6.2-005
[[SUGGESTION_START]] R-6.15.6.2-005b[[SUGGESTION_END]]
R-6.15.6.2-00[[SUGGESTION_START]]6[[SUGGESTION_END]]
R-6.15.6.2-00[[SUGGESTION_START]]7[[SUGGESTION_END]]
R-6.15.6.2-00[[SUGGESTION_START]]8[[SUGGESTION_END]]
R-6.15.6.2-00[[SUGGESTION_START]]9[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.6.2-010[[SUGGESTION_END]]
6.15.6.3 Administrative
R-6.15.6.3-001
R-6.15.6.3-002
R-6.15.6.3-003
R-6.15.6.3-004
R-6.15.6.3-005
[[SUGGESTION_START]]R-6.15.6.3-006[[SUGGESTION_END]]
[[SUGGESTION_START]]6.15.6.4 Notification and acknowledgement for MCX Service Ad hoc Group Emergency Alert[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.6.4-001[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.6.4-002[[SUGGESTION_END]]
6.16 Interaction with telephony services
R-6.16-001
R-6.16-002
6.17 Interworking
NA
6.17.1 Non-3GPP access
R-6.17.1-001
6.17.2 Interworking between MCX Service systems
R-6.17.2-001
R-6.17.2-002
R-6.17.2-003
R-6.17.2-004
R-6.17.2-005
R-6.17.2-006
R-6.17.2-007
R-6.17.2-008
6.17.3 Interworking with non-MCX Service systems
NA
6.17.3.1 GSM-R
R-6.17.3.1-001
R-6.17.3.1-002
R-6.17.3.1-003
R-6.17.3.1-004
R-6.17.3.1-005
6.17.3.2 External systems
R.6.17.3.2-001
R.6.17.3.2-002
6.18 MCX Service coverage extension using ProSe UE-to-Network Relays
R-6.18-001
R-6.18-002
R-6.18-003
R-6.18-004
R-6.18-005
R-6.18-006
6.19 Additional MCX Service requirements
NA
6.19.1 Communication rejection and queuing
NA
6.19.1.1 Requirements
R-6.19.1.1-001
R-6.19.1.1-002
R-6.19.1.1-003
R-6.19.1.1-004
R-6.19.1.1-005
R-6.19.1.1-006
R-6.19.1.1-007
7 MCX Service requirements specific to off-network use
NA
7.1 Off-network communications overview
NA
7.2 General off-network MCX Service requirements
R-7.2-001
R-7.2-002
R-7.2-003
R-7.2-004
R-7.2-005
7.3 Admission control
NA
7.3.1 General aspects
R-7.3.1-001
R-7.3.1-002
R-7.3.1-003
7.3.2 Communication initiation
R-7.3.2-001
R-7.3.2-002
R-7.3.2-003
R-7.3.2-004
R-7.3.2-005
7.4 Communication termination
R-7.4-001
R-7.4-002
R-7.4-003
R-7.4-004
7.5 Broadcast Group
R-7.5-001
R-7.5-002
7.6 MCX Service priority requirements
R-7.6-001
R-7.6-002
R-7.6-003
R-7.6-004
R-7.6-005
R-7.6-006
R-7.6-007
R-7.6-008
R-7.6-009
7.7 Communication types based on priorities
NA
7.7.1 MCX Service Emergency Group Communication requirements
R-7.7.1-001
R-7.7.1-002
R-7.7.1-003
7.7.2 MCX Service Emergency Group Communication cancellation requirements
R-7.7.2-001
7.7.3 Imminent Peril Communication
NA
7.7.3.1 Imminent Peril Group Communication requirements
R-7.7.3.1-001
R-7.7.3.1-002
R-7.7.3.1-003
R-7.7.3.1-004
R-7.7.3.1-005
7.7.3.2 Imminent Peril Group Communication cancellation requirements
R-7.7.3.2-001
R-7.7.3.2-002
7.8 Location
R-7.8-001
R-7.8-002
R-7.8-003
7.9 Security
R-7.9-001
R-7.9-002
7.10 Off-network MCX Service operations
R-7.10-001
R-7.10-002
R-7.10-003
7.11 Off-network UE functionality
R-7.11-001
R-7.11-002
R-7.11-003
7.12 Streaming for ProSe UE-to-UE Relay and UE-to-Network Relay
NA
7.12.1 UE-to-Network Relay for all data types
R-7.12.1-001
R-7.12.1-002
R-7.12.1-003
R-7.12.1-004
7.12.2 UE-to-UE Relay streaming
R-7.12.2-001
R-7.12.2-002
R-7.12.2-003
7.12.3 Off-Network streaming
R-7.12.3-001
R-7.12.3-002
R-7.12.3-003
7.13 Switching to off-network MCX Service
R-7.13-001
R-7.13-002
R-7.13-003
R-7.13-004
R-7.13-005
7.14 Off-network recording and audit requirements
R-7.14-001
R-7.14-001a
R-7.14-002
R-7.14-002a
7.15 Off-network UE-to-UE relay
NA
7.15.1 Private Communications
R-7.15.1-001
R-7.15.1-002
R-7.15.1-003
7.15.2 Group Communications
R-7.15.2-001
R-7.15.2-002
7.16 Off-network Ad hoc Group Communication
R-7.16-001
8 Inter-MCX Service interworking
NA
8.1 Inter-MCX Service interworking overview
NA
8.2 Concurrent operation of different MCX Services
NA
8.2.1 Overview
NA
8.2.2 Requirements
R-8.2.2-001
R-8.2.2-002
R-8.2.2-003
R-8.2.2-004
R-8.2.2-005
R-8.2.2-006
R-8.2.2-007
8.3 Use of unsharable resources within a UE
R-8.3-001
R-8.3-002
R-8.3-003
R-8.3-004
R-8.3-005
R-8.3-006
8.4 Single group with multiple MCX Services
NA
8.4.1 Overview
NA
8.4.2 Requirements
R-8.4.2-001
R-8.4.2-002
R-8.4.2-003
R-8.4.2-004
R-8.4.2-005
8.4.3 Compatibility of UE
NA
8.4.3.1 Advertising service capabilities required
R-8.4.3.1-001
R-8.4.3.1-002
R-8.4.3.1-003
R-8.4.3.1-004
8.4.3.2 Conversion between capabilities
R-8.4.3.2-001
8.4.4 Individual permissions for service access
R-8.4.4-001
8.4.5 Common alias and user identities or mappable
R-8.4.5-001
8.4.6 Single location message
R-8.4.6-001
R-8.4.6-002
R-8.4.6-003
8.5 Priority between services
NA
8.5.1 Overview
NA
8.5.2 Requirements
R-8.5.2-001
R-8.5.2-002
R-8.5.2-003
R-8.5.2-004
R-8.5.2-005
9 Air Ground Air Communications
NA
9.1 Service description
NA
9.2 Requirements
R-9.2-001
10 MCX Service in IOPS mode
R-10-001
End of changes
|
S1-253263.zip
|
2026-01-13T17:32:52.449536
|
S1-253380
|
SA1
|
TSGS1_111_Goteborg
|
CR
|
agreed
|
FRMCS_Ph6 – Normative [SP-250277]
|
3GPP TSG-SA WG1 Meeting #111 S1-253380
Gothenburg, Sweden, 25-29 August 2025 (revision of S1-253252)
CR-Form-v12.3
CHANGE REQUEST
22.280
CR
0179
rev
1
Current version:
20.0.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
X
Radio Access Network
Core Network
X
Title:
Addition of Functional Aliases in the participants list and in the notifications of AHGC
and authorizations for combining Ad hoc Group calls
Source to WG:
UIC, Nokia
Source to TSG:
S1
Work item code:
FRMCS_Ph6-REQ
Date:
2025-08-28
Category:
C
Release:
Rel-20
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
Enhance alignement with functional requirements of UIC specifications (FU-7120 v2.1.2) related to the usage of functional aliases of participants of an AHGC, based on criteria and authorizations of MCX users to combine AHGC.
Summary of change:
Addition of the functional aliases in the participants list
Addition of the functional alias in the response of the receiving participant
Addition of the functional aliases of receiving participants in the notifications
Configure authorizations for MCX users to combine AHGC
Consequences if not approved:
Misalignement with functional requirements of the UIC specifications
Missing stage-1 requirements for downstream groups to include the relevant features
Clauses affected:
6.15.5.2, 6.15.5.3, 6.15.5.4, Annex A, Annex B, Annex C
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
Start of changes
6.15.5.2 General aspects
[R-6.15.5.2-001] The MCX Service shall provide a mechanism for an authorized MCX User to combine an ad hoc multiplicity of MCX Users into a MCX Service Ad hoc Group Communication.[[SUGGESTION_START]] The MCX Users [[SUGGESTION_END]][[SUGGESTION_START]]in th[[SUGGESTION_END]][[SUGGESTION_START]]e participants list [[SUGGESTION_END]][[SUGGESTION_START]]may be identified by their MCX Service ID or by their Functional [[SUGGESTION_END]][[SUGGESTION_START]]A[[SUGGESTION_END]][[SUGGESTION_START]]lias.[[SUGGESTION_END]]
NOTE: Selection of the list of MCX Users can be manual, or automatic based on certain criteria (e.g., location). This is left for implementation.
[R-6.15.5.2-001a] The MCX Service shall provide the reason for denial to an MCX user who was not authorised to initiate an MCX Service Ad hoc Group Communication.
[R-6.15.5.2-001b] The participant of an MCX Ad hoc Group Communication who has activated several Functional Alias(es) shall receive only one communication based on the unique MCX Service ID.
[R-6.15.5.2-001c] The MCX Service shall provide a mechanism for the participant of an Ad hoc Group Communication to determine the Functional Alias, through which this participant is addressed by the communication.
[[SUGGESTION_START]][R-6.15.5.2-001d] The MCX Service shall [[SUGGESTION_END]][[SUGGESTION_START]]enable the [[SUGGESTION_END]][[SUGGESTION_START]]receiving participant [[SUGGESTION_END]][[SUGGESTION_START]]of an Ad hoc Group Communication to [[SUGGESTION_END]][[SUGGESTION_START]]respond [[SUGGESTION_END]][[SUGGESTION_START]]including[[SUGGESTION_END]][[SUGGESTION_START]] the Functional Alias[[SUGGESTION_END]].
[R-6.15.5.2-002] An MCX Service Ad hoc Group Communication is a type of MCX Service Group communication and shall support MCX Service Group Communication mechanisms for call processing (e.g., transmit request queuing, hang time, broadcast mode).
[R-6.15.5.2-003] MCX Service Ad hoc Group Communications shall be terminated by an authorised user or using the same mechanisms as MCX Service Group communications (e.g., initiator release, server release, hang time expiration).
[R-6.15.5.2-004] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure additional conditions under which MCX Service Ad hoc Group Communication shall be terminated (e.g., last Participant leaving, second last Participant leaving, initiator leaving).
[R-6.15.5.2-005] When an MCX Service Ad hoc Group Communication is terminated the group shall not persist.
[R-6.15.5.2-006] The MCX Service shall provide a mechanism for the initiator of a MCX Service Ad hoc Group Communication to indicate which MCX Users have to mandatorily acknowledge the setup request before the media transmission proceeds.
[R-6.15.5.2-007] The MCX Service shall provide a mechanism for an authorized initiator of a MCX Service Ad hoc Group Communication to define the communication parameters for the Ad hoc Group Communication (e.g. priority, hang time, broadcast/non-broadcast)
[R-6.15.5.2-008] MCX Service Ad hoc Group Communications shall be able to support the same urgency as MCX Service Group communication (e.g., general group, emergency, imminent peril).
[R-6.15.5.2-009] MCX Service Ad hoc Group Communications shall support the applicable security requirements as identified in sub-clause 5.12.
[R-6.15.5.2-010] The MCX Service shall provide a mechanism for the initiator of an MCX Service Ad hoc Group Communication to request that the list of participants is suppressed.
[R-6.15.5.2-011] The MCX Service shall provide a mechanism for authorized MCX Users to create a permanent MCX Service Group from the members of the MCX Service Ad hoc Group communication.
[R-6.15.5.2-012] The MCX Service shall provide a mechanism for the initiator and/or an authorised user to add or remove participants during an in progress MCX Service Ad hoc Group communication.
[R-6.15.5.2-013] The MCX Service shall provide a mechanism for a participant to join an in progress MCX Service Ad hoc Group communication.
[R-6.15.5.2-014] The MCX Service shall provide a mechanism for the initiator of an MCX Service Ad hoc Group Communication to request that the list of participants [[SUGGESTION_START]]with their corresponding Functional Aliases[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]] be determined and updated by the MCX Service system using pre-defined criteria.
[R-6.15.5.2-014a] When the list of participants is determined or updated by the MCX Service system, the MCX Service shall provide a mechanism that monitors and ensures that the participants list is applied for an MCX Service Ad hoc Group communication, performing retries when needed.
[R-6.15.5.2-015] The MCX Service shall provide a mechanism for a participant of an MCX Service Ad hoc Group Communication to provide his location information to an authorised participant of the same group.
[R-6.15.5.2-016] The MCX Service shall provide a mechanism for an authorized MCX User to combine multiple Ad hoc Group communications.
[R-6.15.5.2-017] The MCX Service shall provide notifications to the relevant participants and authorized users of the combined Ad hoc Group communication.
[[SUGGESTION_START]][R-6.15.5.2-018] The MCX Service shall provide to the participants of the combined Ad hoc Group communication, the same MCX User authorizations[[SUGGESTION_END]][[SUGGESTION_START]] for modifying or terminatin[[SUGGESTION_END]][[SUGGESTION_START]]g a call[[SUGGESTION_END]][[SUGGESTION_START]], as in the previous [[SUGGESTION_END]][[SUGGESTION_START]]original[[SUGGESTION_END]][[SUGGESTION_START]] Ad hoc Group communications.[[SUGGESTION_END]]
Next change
6.15.5.3 Administrative
[R-6.15.5.3-001] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users, within their authority, are authorized to initiate and/or release a MCX Service Ad hoc Group Communication.
[R-6.15.5.3-002] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure the maximum number of MCX Users who can participate in a MCX Service Ad hoc Group Communication.
[R-6.15.5.3-003] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users are authorized to participate in a MCX Service Ad hoc Group Communication.
[R-6.15.5.3-004] The MCX Service shall provide a mechanism for an MCX Service Administrator to define the default parameters for MCX Service Ad hoc Group communication (e.g., priority, hang time, broadcast mode).
[R-6.15.5.3-005] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure whether MCX Service Ad hoc Group Communication is allowed on the MCX system regardless of individual MCX User authorizations.
[[SUGGESTION_START]][R-6.15.5.3-006] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users[[SUGGESTION_END]] [[SUGGESTION_START]]are authorized to combine Ad hoc Group Communications. [[SUGGESTION_END]]
Next change
6.15.5.4 Notification and acknowledgement for MCX Service Ad hoc Group Communications
[R-6.15.5.4-001] The MCX Service shall provide mechanisms for notification and acknowledgement of MCX Service Ad hoc Group Communications as defined in section 6.2.1.
[[SUGGESTION_START]][R-6.15.5.4-001a] [[SUGGESTION_END]][[SUGGESTION_START]]The notification and acknowledgement [[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]]hall include [[SUGGESTION_END]][[SUGGESTION_START]]the corresponding Functional Alias[[SUGGESTION_END]] [[SUGGESTION_START]]for each[[SUGGESTION_END]] [[SUGGESTION_START]]MC[[SUGGESTION_END]] [[SUGGESTION_START]]Service [[SUGGESTION_END]][[SUGGESTION_START]]ID[[SUGGESTION_END]][[SUGGESTION_START]], if available[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
NOTE: For MCX Service Ad hoc Group Communications a participant is considered an affiliated member of the group during communication setup.
[[SUGGESTION_START]][R-6.15.5.4-002] [[SUGGESTION_END]][[SUGGESTION_START]]T[[SUGGESTION_END]][[SUGGESTION_START]]he MCX Service shall provide notifications to authorized users[[SUGGESTION_END]][[SUGGESTION_START]], [[SUGGESTION_END]][[SUGGESTION_START]]including,[[SUGGESTION_END]][[SUGGESTION_START]] if [[SUGGESTION_END]][[SUGGESTION_START]]available[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] the [[SUGGESTION_END]][[SUGGESTION_START]]corresponding [[SUGGESTION_END]][[SUGGESTION_START]]Functional Aliases[[SUGGESTION_END]][[SUGGESTION_START]] of t[[SUGGESTION_END]][[SUGGESTION_START]]he[[SUGGESTION_END]][[SUGGESTION_START]] determined[[SUGGESTION_END]] [[SUGGESTION_START]]or updated [[SUGGESTION_END]][[SUGGESTION_START]]list of [[SUGGESTION_END]][[SUGGESTION_START]]participants[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
Next change
Annex A (normative):
MCCoRe Requirements for MCPTT
Table A.1 provides an exhaustive list of those requirements in 3GPP TS 22.280 that are applicable to MCPTT.
Table A.1
5 MCX Service Requirements common for on the network and off the network
NA
5.1 General Group Communications Requirements
NA
5.1.1 General aspects
R-5.1.1-001
R-5.1.1-002
R-5.1.1-003
R-5.1.1-004
R-5.1.1-005
5.1.2 Group/status information
R-5.1.2-001
R-5.1.2-002
5.1.3 Group configuration
R-5.1.3-001
R5.1.3-002
5.1.4 Identification
R-5.1.4-001
5.1.5 Membership/affiliation
R-5.1.5-001
R-5.1.5-002
R-5.1.5-003
R-5.1.5-004
R-5.1.5-005
R-5.1.5-006
R-5.1.5-007
R-5.1.5-008
5.1.6 Group Communication administration
R-5.1.6-001
5.1.7 Prioritization
R-5.1.7-001
R-5.1.7-002
5.1.8 Charging requirements for MCX Service
R-5.1.8-001
R-5.1.8-003
R-5.1.8-004
R-5.1.8-005
R-5.1.8-006
R-5.1.8-007
R-5.1.8-008
R-5.1.8-009
R-5.1.8-010
R-5.1.8-011
5.1.9 MCX Service Emergency Alert triggered by location
NA
5.2 Broadcast Group
NA
5.2.1 General Broadcast Group Communication
R-5.2.1-001
R-5.2.1-002
5.2.2 Group-Broadcast Group (e.g., announcement group)
R-5.2.2-001
R-5.2.2-002
R-5.2.2-003
R-5.2.2-004
5.2.3 User-Broadcast Group (e.g., System Communication)
R-5.2.3-001
R-5.2.3-002
5.3 Late communication entry
R-5.3-001
R-5.3-002
R-5.3-003
R-5.3-004
R-5.3-005
5.4 Receiving from multiple MCX Service communications
5.4.1 Overview
NA
5.4.2 Requirements
R-5.4.2-001
R-5.4.2-002
R-5.4.2-003
R-5.4.2-004
R-5.4.2-004A
R-5.4.2-004B
R-5.4.2-005
R-5.4.2-006
R-5.4.2-007
R-5.4.2-007a
R-5.4.2-008
R-5.4.2-009
5.5 Private Communication
NA
5.5.1 Private Communication general requirements
NA
5.5.2 Charging requirement for MCX Service
R-5.5.2-001
5.6 MCX Service priority requirements
NA
5.6.1 Overview
NA
5.6.2 Communication types based on priorities
NA
5.6.2.1 MCX Service Emergency and Imminent Peril general requirements
NA
5.6.2.1.1 Overview
NA
5.6.2.1.2 Requirements
R-5.6.2.1.2-001
R-5.6.2.1.2-002
R-5.6.2.1.2-003
R-5.6.2.1.2-004
R-5.6.2.1.2-005
5.6.2.2 MCX Service Emergency Group Communication
NA
5.6.2.2.1 MCX Service Emergency Group Communication requirements
R-5.6.2.2.1-001
R-5.6.2.2.1-002
R-5.6.2.2.1-003
R-5.6.2.2.1-004
R-5.6.2.2.1-005
R-5.6.2.2.1-006
R-5.6.2.2.1-007
R-5.6.2.2.1-008
R-5.6.2.2.1-009
R-5.6.2.2.1-010
R-5.6.2.2.1-011
R-5.6.2.2.1-012
R-5.6.2.2.1-013
R-5.6.2.2.1-014
5.6.2.2.2 MCX Service Emergency Group Communication cancellation requirements
R-5.6.2.2.2-001
R-5.6.2.2.2-002
R-5.6.2.2.2-003
R-5.6.2.2.2-004
R-5.6.2.2.2-005
5.6.2.3 MCX Service Imminent Peril Group
NA
5.6.2.3.1 MCX Service Imminent Peril Group Communication requirements
R-5.6.2.3.1-001
R-5.6.2.3.1-002
R-5.6.2.3.1-003
R-5.6.2.3.1-004
R-5.6.2.3.1-005
R-5.6.2.3.1-006
R-5.6.2.3.1-007
R-5.6.2.3.1-008
R-5.6.2.3.1-009
5.6.2.3.2 MCX Service Imminent Peril Group Communications cancellation requirements
R-5.6.2.3.2-001
R-5.6.2.3.2-002
R-5.6.2.3.2-003
R-5.6.2.3.2-004
5.6.2.4 MCX Service Emergency Alert
NA
5.6.2.4.1 MCX Service Emergency Alert requirements
R-5.6.2.4.1-001
R-5.6.2.4.1-002
R-5.6.2.4.1-003
R-5.6.2.4.1-004
R-5.6.2.4.1-004a
R-5.6.2.4.1-005
R-5.6.2.4.1-006
R-5.6.2.4.1-007
R-5.6.2.4.1-008
R-5.6.2.4.1-009
R-5.6.2.4.1-010
R-5.6.2.4.1-011
R-5.6.2.4.1-012
R-5.6.2.4.1-013
5.6.2.4.2 MCX Service Emergency Alert cancellation requirements
R-5.6.2.4.2-001
R-5.6.2.4.2-002
R-5.6.2.4.2-003
5.7 MCX Service User ID
R-5.7-001
R-5.7-002
R-5.7-003
5.8 MCX UE Management
R-5.8-001
R-5.8-002
5.9 MCX Service User Profile
R-5.9-001
R-5.9-002
5.9A Functional alias
R-5.9a-001
R-5.9a-001a
R-5.9a-001b
R-5.9a-001c
R-5.9a-002
R-5.9a-002a
R-5.9a-003
R-5.9a-004
R-5.9a-005
R-5.9a-006
R-5.9a-007
R-5.9a-008
R-5.9a-009
R-5.9a-010
R-5.9a-011
R-5.9a-012
R-5.9a-013
R-5.9a-014
R-5.9a-015
R-5.9a-016
R-5.9a-017
R-5.9a-018
R-5.9a-019
R-5.9a-020
R-5.9a-021
R-5.9a-022
R-5.9a-023
[R-5.9a-024
R-5.9a-025
R-5.9a-026
R-5.9a-027
R-5.9a-028
R-5.9a-029
R-5.9a-030
R-5.9a-031
5.10 Support for multiple devices
R-5.10-001
R-5.10-001a
R-5.10-002
5.11 Location
R-5.11-001
R-5.11-002
R-5.11-002a
R-5.11-003
R-5.11-004
R-5.11-005
R-5.11-006
R-5.11-007
R-5.11-008
R-5.11-009
R-5.11-010
R-5.11-011
R-5.11-013
R-5.11-014
R-5.11-015
R-5.11-015
5.12 Security
R-5.12-001
R-5.12-002
R-5.12-003
R-5.12-004
R-5.12-005
R-5.12-006
R-5.12-007
R-5.12-008
R-5.12-009
R-5.12-010
R-5.12-011
R-5.12-012
R-5.12-013
R5-12-014
5.13 Media quality
R-5.13-001
5.14 Relay requirements
R-5.14-001
R-5.14-002
R-5.14-003
R-5.14-004
5.15 Gateway requirements
R-5.15-001
R-5.15-002
R-5.15-003
5.16 Control and management by Mission Critical Organizations
NA
5.16.1 Overview
NA
5.16.2 General requirements
R-5.16.2-001
R-5.16.2-002
R-5.16.2-003
R-5.16.2-004
R-5.16.2-005
5.16.3 Operational visibility for Mission Critical Organizations
R-5.16.3-001
5.17 General administrative – groups and users
R-5.17-001
R-5.17-002
R-5.17-003
R-5.17-004
R-5.17-005
R-5.17-006
R-5.17-007
R-5.17-008
5.18 Open interfaces for MCX services
NA
5.18.1 Overview
NA
5.18.2 Requirements
NA
5.19 Media forwarding
NA
5.19.1 Service description
NA
5.19.2 Requirements
NA
5.20 Receipt notification
NA
5.20.1 Service description
NA
5.20.2 Requirements
NA
5.21 Additional services for MCX Service communications
NA
5.21.1 Remotely initiated MCX Service communication
NA
5.21.1.1 Overview
NA
5.21.1.2 Requirements
NA
5.21.2 Remotely terminated MCX Service communication
NA
5.21.2.1 Requirements
R-5.21.2.1-001
6 MCX Service requirements specific to on-network use
NA
6.1 General administrative – groups and users
R-6.1-001
R-6.1-002
R-6.1-003
R-6.1-004
R-6.1-005
6.2 MCX Service communications
NA
6.2.1 Notification and acknowledgement for MCX Service Group Communications
NA
6.2.2 Queuing
R-6.2.2-001
R-6.2.2-002
R-6.2.2-003
R-6.2.2-004
R-6.2.2-005
R-6.2.2-006
6.3 General requirements
R-6.3-001
R-6.3-002
R-6.3-003
R-6.3-004
6.4 General group communication
NA
6.4.1 General aspects
R-6.4.1-001
6.4.2 Group status/information
R-6.4.2-005
R-6.4.2-001
R-6.4.2-002
R-6.4.2-003
R-6.4.2-004
R-6.4.2-006
R-6.4.2-007
6.4.3 Identification
R-6.4.3-001
R-6.4.3-002
6.4.4 Membership/affiliation
R-6.4.4-001
R-6.4.4-002
R-6.4.4-002a
R-6.4.4-003
R-6.4.4-004
6.4.5 Membership/affiliation list
R-6.4.5-001
R-6.4.5-002
R-6.4.5-003
R-6.4.5-003a
R-6.4.5-004
R-6.4.5-005
R-6.4.5-006
R-6.4.5-007
R-6.4.5-008
6.4.6 Authorized user remotely changes another MCX User’s affiliated and/or Selected MCX Service Group(s)
NA
6.4.6.1 Mandatory change
R-6.4.6.1-001
R-6.4.6.1-002
R-6.4.6.1-003
R-6.4.6.1-004
6.4.6.2 Negotiated change
R-6.4.6.2-001
R-6.4.6.2-002
R-6.4.6.2-003
R-6.4.6.2-004
R-6.4.6.2-005
R-6.4.6.2-006
6.4.7 Prioritization
R-6.4.7-001
R-6.4.7-002
R-6.4.7-003
R-6.4.7-004
6.4.8 Relay requirements
R-6.4.8-001
6.4.9 Administrative
R-6.4.9-001
R-6.4.9-002
R-6.4.9-003
R-6.4.9-004
R-6.4.9-005
R-6.4.9-006
6.5 Broadcast Group
NA
6.5.1 General Broadcast Group Communication
R-6.5.1-001
R-6.5.1-002
6.5.2 Group-Broadcast Group (e.g., announcement group)
R-6.5.2-001
6.5.3 User-Broadcast Group (e.g., system communication)
R-6.5.3-001
6.6 Dynamic group management (i.e., dynamic reporting)
NA
6.6.1 General dynamic regrouping
R-6.6.1-001
R-6.6.1-002
R-6.6.1-003
R-6.6.1-004
R-6.6.1-005
R-6.6.1-006
6.6.2 Group regrouping
NA
6.6.2.1 Service description
NA
6.6.2.2 Requirements
R-6.6.2.2-001
R-6.6.2.2-002
R-6.6.2.2-003
R-6.6.2.2-004
R-6.6.2.2-005
R-6.6.2.2-006
R-6.6.2.2-007
R-6.6.2.2-008
R-6.6.2.2-009
R-6.6.2.2-010
R-6.6.2.2-011
R-6.6.2.2-012
R-6.6.2.2-013
6.6.3 Temporary Broadcast Groups
R-6.6.3-001
R-6.6.3-001a
R-6.6.3-001b
R-6.6.3-002
6.6.4 User regrouping
NA
6.6.4.1 Service description
NA
6.6.4.2 Requirements
R-6.6.4.2-001
R-6.6.4.2-002
R-6.6.4.2-002a
R-6.6.4.2-002b
R-6.6.4.2-003
R-6.6.4.2-004
R-6.6.4.2-005
6.6.5 Dynamic Group Participation
NA
6.6.5.1 Service description
NA
6.6.5.2 Requirements
R-6.6.5.2-001
R-6.6.5.2-002
R-6.6.5.2-003
R-6.6.5.2-004
R-6.6.5.2-005
R-6.654.2-006
R-6.6.5.2-007
R-6.6.5.2-008
6.7 Private Communication
NA
6.7.1 Overview
NA
6.7.2 General requirements
R-6.7.2-001
R-6.7.2-002
R-6.7.2-003
R-6.7.2-004
R-6.7.2-005
R-6.7.2-006
6.7.3 Administrative
R-6.7.3-001
R-6.7.3-002
R-6.7.3-003
R-6.7.3-004
R-6.7.3-005
R-6.7.3-006
R-6.7.3-007
R-6.7.3-007a
R-6.7.3-008
6.7.4 Prioritization
R-6.7.4-001
R-6.7.4-002
R-6.7.4-003
R-6.7.4-004
R-6.7.4-005
R-6.7.4-006
R-6.7.4-007
6.7.5 Private Communication (without Floor control) commencement requirements
R-6.7.5-001
R-6.7.5-002
R-6.7.5-003
6.7.6 Private Communication (without Floor control) termination
R-6.7.6-001
R-6.7.6-002
6.8 MCX Service priority requirements
NA
6.8.1 General
R-6.8.1-001
R-6.8.1-002
R-6.8.1-003
R-6.8.1-004
R-6.8.1-005
R-6.8.1-006
R-6.8.1-007
R-6.8.1-008
R-6.8.1-009
R-6.8.1-010
R-6.8.1-011
R-6.8.1-012
R-6.8.1-013
R-6.8.1-014
R-6.8.1-015
R-6.8.1-016
6.8.2 3GPP system access controls
R-6.8.2-001
6.8.3 3GPP system admission controls
R-6.8.3-001
6.8.4 3GPP system scheduling controls
R-6.8.4-001
6.8.5 UE access controls
R-6.8.5-001
6.8.6 Mobility and load management
NA
6.8.6.1 Mission Critical mobility management according to priority
R-6.8.6.1-001
R-6.8.6.1-002
6.8.6.2 Load management
R-6.8.6.2-001
R-6.8.6.2-002
R-6.8.6.2-003
R-6.8.6.2-004
R-6.8.6.2-005
6.8.7 Application layer priorities
NA
6.8.7.1 Overview
NA
6.8.7.2 Requirements
R-6.8.7.2-001
R-6.8.7.2-002
R-6.8.7.2-003
R-6.8.7.2-004
R-6.8.7.2-005
R-6.8.7.2-006
R-6.8.7.2-007
R-6.8.7.2-008
R-6.8.7.2-009
R-6.8.7.2-010
6.8.8 Communication types based on priorities
NA
6.8.8.1 MCX Service Emergency Group Communication requirements
R-6.8.8.1-001
R-6.8.8.1-002
R-6.8.8.1-003
R-6.8.8.1-004
6.8.8.2 MCX Service Emergency Private Communication requirements
NA
6.8.8.3 Imminent Peril Group Communication requirements
R-6.8.8.3-001
R-6.8.8.3-002
R-6.8.8.3-003
6.8.8.4 MCX Service Emergency Alert
NA
6.8.8.4.1 Requirements
R-6.8.8.4.1-001
R-6.8.8.4.1-002
R-6.8.8.4.1-003
R-6.8.8.4.1-004
R-6.8.8.4.1-005
R-6.8.8.4.1-006
6.8.8.4.2 MCX Service Emergency Alert cancellation requirements
R-6.8.8.4.2-001
R-6.8.8.4.2-002
6.8.8.X Ad hoc Group Communication requirements
R-6.8.8.X-001
6.9 IDs and aliases
R-6.9-001
R-6.9-002
R-6.9-003
R-6.9-004
6.10 User Profile management
R-6.10-001
R-6.10-002
R-6.10-003
R-6.10-004
6.11 Support for multiple devices
R-6.11-001
R-6.11-002
R-6.11-003
6.12 Location
R-6.12-001
R-6.12-002
R-6.12-003
R-6.12-004
R-6.12-005
R-6.12-006
R-6.12-007
6.13 Security
NA
6.13.1 Overview
NA
6.13.2 Cryptographic protocols
R-6.13.2-001
R-6.13.2-002
R-6.13.2-003
6.13.3 Authentication
R-6.13.3-001
6.13.4 Access control
R-6.13.4-001
R-6.13.4-002
R-6.13.4-003
R-6.13.4-004
R-6.13.4-005
R-6.13.4-006
R-6.13.4-007
R-6.13.4-008
R-6.13.4-009
R-6.13.4-010
6.13.5 Regulatory issues
R-6.13.5-001
6.13.6 Storage control
NA
6.14 Interactions for MCX Service Group Communications and MCX Service Private Communications
R-6.14-001
R-6.14-002
6.15 Additional services for MCX Service communications
NA
6.15.1 Discreet listening capabilities
R-6.15.1-001a
R-6.15.1-001
R-6.15.1-002
R-6.15.1-002a
R-6.15.1-003
R-6.15.1-004
6.15.2 Ambient listening
NA
6.15.2.1 Overview of ambient listening
NA
6.15.2.2 Ambient listening requirements
NA
6.15.2.2.1 General ambient listening requirements
R-6.15.2.2.1-001
R-6.15.2.2.1-002
R-6.15.2.2.1-003
6.15.2.2.2 Remotely initiated ambient listening requirements
R-6.15.2.2.2-001
R-6.15.2.2.2-002
6.15.2.2.3 Locally initiated ambient listening requirements
R-6.15.2.2.3-001
R-6.15.2.2.3-002
6.15.3 Remotely initiated MCX Service Communication
NA
6.15.3.1 Overview
NA
6.15.3.2 Requirements
R-6.15.3.2-001
R-6.15.3.2-002
R-6.15.3.2-003
R-6.15.3.2-004
6.15.4 Recording and audit requirements
R-6.15.4-001
R-6.15.4-002
R-6.15.4-003
R-6.15.4-004
R-6.15.4-005
R-6.15.4-006
R-6.15.4-007
R-6.15.4-008
R-6.15.4-009
R-6.15.4-010
R-6.15.4-011
6.15.5 MCX Service Ad hoc Group Communication
NA
6.15.5.1 Overview
NA
6.15.5.2 General Aspects
R-6.15.5.2-001
R-6.15.5.2-001a
R-6.15.5.2-001b
R-6.15.5.2-001c
[[SUGGESTION_START]]R-6.15.5.2-001d [[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]2[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]3[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]4[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]5[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]6[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]7[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]8[[SUGGESTION_END]]
R-6.15.5.2-0[[SUGGESTION_START]]09[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]0[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]1[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]2[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]3[[SUGGESTION_END]]
R-6.15.5.2-014
R-6.15.5.2-01[[SUGGESTION_START]]4a[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]5[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]6[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.5.2-017[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.5.2-018[[SUGGESTION_END]]
6.15.5.3 Administrative
R-6.15.5.3-001
R-6.15.5.3-002
R-6.15.5.3-003
R-6.15.5.3-004
R-6.15.5.3-005
[[SUGGESTION_START]]R-[[SUGGESTION_END]][[SUGGESTION_START]]6.15.5.3-006[[SUGGESTION_END]]
6.15.5.4 Notification and acknowledgement for MCX Service Ad hoc Group Communications
R-6.15.5.4-001
[[SUGGESTION_START]]R-6.15.5.4-001a[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.5.4-002[[SUGGESTION_END]]
6.15.6 MCX Service Ad hoc Group Emergency Alert
NA
6.15.6.1 Overview
NA
6.15.6.2 General aspects
R-6.15.6.2-001
R-6.15.6.2-002
R-6.15.6.2-002a
R-6.15.6.2-003
R-6.15.6.2-004
R-6.15.6.2-005
R-6.15.6.2-005a
R-6.15.6.2-005b
R-6.15.6.2-006
R-6.15.6.2-007
R-6.15.6.2-008
6.15.6.3 Administrative
R-6.15.6.3-001
R-6.15.6.3-002
R-6.15.6.3-003
R-6.15.6.3-004
R-6.15.6.3-005
6.16 Interaction with telephony services
R-6.16-001
R-6.16-002
6.17 Interworking
NA
6.17.1 Non-3GPP access
R-6.17.1-001
6.17.2 Interworking between MCX Service systems
R-6.17.2-001
R-6.17.2-002
R-6.17.2-003
R-6.17.2-004
R-6.17.2-005
R-6.17.2-006
R-6.17.2-007
R-6.17.2-008
6.17.3 Interworking with non-MCX Service systems
NA
6.17.3.1 GSM-R
R-6.17.3.1-001
R-6.17.3.1-002
R-6.17.3.1-003
R-6.17.3.1-004
R-6.17.3.1-005
6.17.3.2 External systems
R.6.17.3.2-001
R.6.17.3.2-002
6.18 MCX Service coverage extension using ProSe UE-to-Network Relays
R-6.18-001
R-6.18-002
R-6.18-003
R-6.18-004
R-6.18-005
R-6.18-006
6.19 Additional MCX Service requirements
NA
6.19.1 Communication rejection and queuing
NA
6.19.1.1 Requirements
R-6.19.1.1-001
R-6.19.1.1-002
R-6.19.1.1-003
R-6.19.1.1-004
R-6.19.1.1-005
R-6.19.1.1-006
R-6.19.1.1-007
7 MCX Service requirements specific to off-network use
NA
7.1 Off-network communications overview
NA
7.2 General off-network MCX Service requirements
R-7.2-001
R-7.2-002
R-7.2-003
R-7.2-004
R-7.2-005
7.3 Admission control
NA
7.3.1 General aspects
R-7.3.1-001
R-7.3.1-002
R-7.3.1-003
7.3.2 Communication initiation
R-7.3.2-001
R-7.3.2-002
R-7.3.2-003
R-7.3.2-004
R-7.3.2-005
7.4 Communication termination
R-7.4-001
R-7.4-002
R-7.4-003
R-7.4-004
7.5 Broadcast Group
R-7.5-001
R-7.5-002
7.6 MCX Service priority requirements
R-7.6-001
R-7.6-002
R-7.6-003
R-7.6-004
R-7.6-005
R-7.6-006
R-7.6-007
R-7.6-008
R-7.6-009
7.7 Communication types based on priorities
NA
7.7.1 MCX Service Emergency Group Communication requirements
R-7.7.1-001
R-7.7.1-002
R-7.7.1-003
7.7.2 MCX Service Emergency Group Communication cancellation requirements
R-7.7.2-001
7.7.3 Imminent Peril Communication
NA
7.7.3.1 Imminent Peril Group Communication requirements
R-7.7.3.1-001
R-7.7.3.1-002
R-7.7.3.1-003
R-7.7.3.1-004
R-7.7.3.1-005
7.7.3.2 Imminent Peril Group Communication cancellation requirements
R-7.7.3.2-001
R-7.7.3.2-002
7.8 Location
R-7.8-001
R-7.8-002
R-7.8-003
7.9 Security
R-7.9-001
R-7.9-002
7.10 Off-network MCX Service operations
R-7.10-001
R-7.10-002
R-7.10-003
7.11 Off-network UE functionality
R-7.11-001
R-7.11-002
R-7.11-003
7.12 Streaming for ProSe UE-to-UE Relay and UE-to-Network Relay
NA
7.12.1 UE-to-Network Relay for all data types
R-7.12.1-001
R-7.12.1-002
R-7.12.1-003
R-7.12.1-004
7.12.2 UE-to-UE Relay streaming
R-7.12.2-001
R-7.12.2-002
R-7.12.2-003
7.12.3 Off-Network streaming
R-7.12.3-001
R-7.12.3-002
R-7.12.3-003
7.13 Switching to off-network MCX Service
R-7.13-001
R-7.13-002
R-7.13-003
R-7.13-004
R-7.13-005
7.14 Off-network recording and audit requirements
R-7.14-001
R-7.14-001a
R-7.14-002
R-7.14-002a
7.15 Off-network UE-to-UE relay
NA
7.15.1 Private Communications
R-7.15.1-001
R-7.15.1-002
R-7.15.1-003
7.15.2 Group Communications
R-7.15.2-001
R-7.15.2-002
7.16 Off-network Ad hoc Group Communication
R-7.16-001
8 Inter-MCX Service interworking
NA
8.1 Inter-MCX Service interworking overview
NA
8.2 Concurrent operation of different MCX Services
NA
8.2.1 Overview
NA
8.2.2 Requirements
R-8.2.2-001
R-8.2.2-002
R-8.2.2-003
R-8.2.2-004
R-8.2.2-005
R-8.2.2-006
R-8.2.2-007
8.3 Use of unsharable resources within a UE
R-8.3-001
R-8.3-002
R-8.3-003
R-8.3-004
R-8.3-005
R-8.3-006
8.4 Single group with multiple MCX Services
NA
8.4.1 Overview
NA
8.4.2 Requirements
R-8.4.2-001
R-8.4.2-002
R-8.4.2-003
R-8.4.2-004
R-8.4.2-005
8.4.3 Compatibility of UE
NA
8.4.3.1 Advertising service capabilities required
R-8.4.3.1-001
R-8.4.3.1-002
R-8.4.3.1-003
R-8.4.3.1-004
8.4.3.2 Conversion between capabilities
R-8.4.3.2-001
8.4.4 Individual permissions for service access
R-8.4.4-001
8.4.5 Common alias and user identities or mappable
R-8.4.5-001
8.4.6 Single location message
R-8.4.6-001
R-8.4.6-002
R-8.4.6-003
8.5 Priority between services
NA
8.5.1 Overview
NA
8.5.2 Requirements
R-8.5.2-001
R-8.5.2-002
R-8.5.2-003
R-8.5.2-004
R-8.5.2-005
9 Air Ground Air Communications
NA
9.1 Service description
NA
9.2 Requirements
R-9.2-001
10 MCX Service in IOPS mode
R-10-001
Annex B (normative):
MCCoRe Requirements for MCVideo
Table B.1 provides an exhaustive list of those requirements in 3GPP TS 22.280 that are applicable to MCVideo.
Table B.1
5 MCX Service Requirements common for on the network and off the network
NA
5.1 General Group Communications requirements
NA
5.1.1 General aspects
R-5.1.1-001
R-5.1.1-002
R-5.1.1-003
R-5.1.1-004
R-5.1.1-005
R-5.1.1-006
5.1.2 Group/status information
R-5.1.2-001
R-5.1.2-002
5.1.3 Group configuration
R-5.1.3-001
R5.1.3-002
5.1.4 Identification
R-5.1.4-001
5.1.5 Membership/affiliation
R-5.1.5-001
R-5.1.5-002
R-5.1.5-003
R-5.1.5-004
R-5.1.5-005
R-5.1.5-006
R-5.1.5-007
R-5.1.5-008
5.1.6 Group Communication administration
R-5.1.6-001
5.1.7 Prioritization
R-5.1.7-001
R-5.1.7-002
5.1.8 Charging requirements for MCX Service
R-5.1.8-001
R-5.1.8-003
R-5.1.8-004
R-5.1.8-005
R-5.1.8-006
R-5.1.8-007
R-5.1.8-008
R-5.1.8-009
R-5.1.8-010
R-5.1.8-011
5.1.9 MCX Service Emergency Alert triggered by location
R-5.1.9-001
R-5.1.9-002
5.2 Broadcast Group
NA
5.2.1 General Broadcast Group Communication
R-5.2.1-001
R-5.2.1-002
5.2.2 Group-Broadcast Group (e.g., announcement group)
R-5.2.2-001
R-5.2.2-002
R-5.2.2-003
R-5.2.2-004
5.2.3 User-Broadcast Group (e.g., System Communication)
R-5.2.3-001
R-5.2.3-002
5.3 Late communication entry
R-5.3-001
R-5.3-002
R-5.3-003
R-5.3-004
R-5.3-005
5.4 Receiving from multiple MCX Service communications
5.4.1 Overview
NA
5.4.2 Requirements
R-5.4.2-001
R-5.4.2-002
R-5.4.2-003
R-5.4.2-004
R-5.4.2-004A
R-5.4.2-004B
R-5.4.2-005
R-5.4.2-006
R-5.4.2-007
R-5.4.2-007a
R-5.4.2-008
R-5.4.2-009
5.5 Private Communication
NA
5.5.1 Private Communication general requirements
R-5.5.1-001
5.5.2 Charging requirement for MCX Service
R-5.5.2-001
5.6 MCX Service priority requirements
NA
5.6.1 Overview
NA
5.6.2 Communication types based on priorities
NA
5.6.2.1 MCX Service Emergency and Imminent Peril general requirements
NA
5.6.2.1.1 Overview
NA
5.6.2.1.2 Requirements
R-5.6.2.1.2-001
R-5.6.2.1.2-002
R-5.6.2.1.2-003
R-5.6.2.1.2-004
R-5.6.2.1.2-005
5.6.2.2 MCX Service Emergency Group Communication
NA
5.6.2.2.1 MCX Service Emergency Group Communication requirements
R-5.6.2.2.1-001
R-5.6.2.2.1-002
R-5.6.2.2.1-003
R-5.6.2.2.1-004
R-5.6.2.2.1-005
R-5.6.2.2.1-006
R-5.6.2.2.1-007
R-5.6.2.2.1-008
R-5.6.2.2.1-009
R-5.6.2.2.1-010
R-5.6.2.2.1-011
R-5.6.2.2.1-012
R-5.6.2.2.1-013
R-5.6.2.2.1-014
5.6.2.2.2 MCX Service Emergency Group Communication cancellation requirements
R-5.6.2.2.2-001
R-5.6.2.2.2-002
R-5.6.2.2.2-003
R-5.6.2.2.2-004
R-5.6.2.2.2-005
5.6.2.3 MCX Service Imminent Peril Group
NA
5.6.2.3.1 MCX Service Imminent Peril Group Communication requirements
R-5.6.2.3.1-001
R-5.6.2.3.1-002
R-5.6.2.3.1-003
R-5.6.2.3.1-004
R-5.6.2.3.1-005
R-5.6.2.3.1-006
R-5.6.2.3.1-007
R-5.6.2.3.1-008
R-5.6.2.3.1-009
5.6.2.3.2 MCX Service Imminent Peril Group Communications cancellation requirements
R-5.6.2.3.2-001
R-5.6.2.3.2-002
R-5.6.2.3.2-003
R-5.6.2.3.2-004
5.6.2.4 MCX Service Emergency Alert
NA
5.6.2.4.1 MCX Service Emergency Alert requirements
R-5.6.2.4.1-001
R-5.6.2.4.1-002
R-5.6.2.4.1-003
R-5.6.2.4.1-004
R-5.6.2.4.1-004a
R-5.6.2.4.1-005
R-5.6.2.4.1-006
R-5.6.2.4.1-007
R-5.6.2.4.1-008
R-5.6.2.4.1-009
R-5.6.2.4.1-010
R-5.6.2.4.1-011
R-5.6.2.4.1-012
R-5.6.2.4.1-013
5.6.2.4.2 MCX Service Emergency Alert cancellation requirements
R-5.6.2.4.2-001
R-5.6.2.4.2-002
R-5.6.2.4.2-003
5.7 MCX Service User ID
R-5.7-001
R-5.7-002
R-5.7-003
5.8 MCX UE Management
R-5.8-001
R-5.8-002
5.9 MCX Service User Profile
R-5.9-001
R-5.9-002
5.9A Functional alias
R-5.9a-001
R-5.9a-001a
R-5.9a-001b
R-5.9a-001c
R-5.9a-002
R-5.9a-002a
R-5.9a-003
R-5.9a-004
R-5.9a-005
R-5.9a-006
R-5.9a-007
R-5.9a-008
R-5.9a-009
R-5.9a-010
R-5.9a-011
R-5.9a-012
R-5.9a-013
R-5.9a-014
R-5.9a-015
R-5.9a-016
R-5.9a-017
R-5.9a-018
R-5.9a-019
R-5.9a-020
R-5.9a-021
R-5.9a-022
R-5.9a-023
R-5.9a-024
R-5.9a-025
R-5.9a-026
R-5.9a-027
R-5.9a-028
R-5.9a-029
R-5.9a-030
R-5.9a-031
5.10 Support for multiple devices
R-5.10-001
R-5.10-001a
R-5.10-002
5.11 Location
R-5.11-001
R-5.11-002
R-5.11-002a
R-5.11-003
R-5.11-004
R-5.11-005
R-5.11-006
R-5.11-007
R-5.11-008
R-5.11-009
R-5.11-010
R-5.11-011
R-5.11-012
R-5.11-013
R-5.11-014
R-5.11-015
5.12 Security
R-5.12-001
R-5.12-002
R-5.12-003
R-5.12-004
R-5.12-005
R-5.12-006
R-5.12-007
R-5.12-008
R-5.12-009
R-5.12-010
R-5.12-011
R-5.12-012
R-5.12-013
R-5.12-014
5.13 Media quality
R-5.13-001
5.14 Relay requirements
R-5.14-001
R-5.14-002
R-5.14-003
R-5.14-004
5.15 Gateway requirements
R-5.15-001
R-5.15-002
R-5.15-003
5.16 Control and management by Mission Critical Organizations
NA
5.16.1 Overview
NA
5.16.2 General requirements
R-5.16.2-001
R-5.16.2-002
R-5.16.2-003
R-5.16.2-004
R-5.16.2-005
5.16.3 Operational visibility for Mission Critical Organizations
R-5.16.3-001
5.17 General administrative – groups and users
R-5.17-001
R-5.17-002
R-5.17-003
R-5.17-004
R-5.17-005
R-5.17-006
R-5.17-007
R-5.17-008
5.18 Open interfaces for MCX services
NA
5.18.1 Overview
NA
5.18.2 Requirements
R-5.18.2-001
R-5.18.2-002
R-5.18.2-003
R-5.18.2-004
5.19 Media forwarding
NA
5.19.1 Service description
NA
5.19.2 Requirements
R-5.19.2-001
R-5.19.2-002
R-5.19.2-003
5.20 Receipt notification
NA
5.20.1 Service description
NA
5.20.2 Requirements
R-5.20.2-001
5.21 Additional services for MCX Service communications
NA
5.21.1 Remotely initiated MCX Service communication
NA
5.21.1.1 Overview
NA
5.21.1.2 Requirements
R-5.21.1.2-001
R-5.21.1.2-002
R-5.21.1.2-003
R-5.21.1.2-004
5.21.2 Remotely terminated MCX Service communication
NA
5.21.2.1 Requirements
R-5.21.2.1-001
6 MCX Service requirements specific to on-network use
NA
6.1 General administrative – groups and users
R-6.1-001
R-6.1-002
R-6.1-003
R-6.1-004
R-6.1-005
6.2 MCX Service communications
NA
6.2.1 Notification and acknowledgement for MCX Service Group Communications
R-6.2.1-001
R-6.2.1-002
R-6.2.1-003
R-6.2.1-004
R-6.2.1-005
6.2.2 Queuing
R-6.2.2-001
R-6.2.2-002
R-6.2.2-003
R-6.2.2-004
R-6.2.2-005
R-6.2.2-006
6.3 General requirements
R-6.3-001
R-6.3-002
R-6.3-003
R-6.3-004
6.4 General group communication
NA
6.4.1 General aspects
R-6.4.1-001
6.4.2 Group status/information
R-6.4.2-005
R-6.4.2-001
R-6.4.2-002
R-6.4.2-003
R-6.4.2-004
R-6.4.2-006
R-6.4.2-007
6.4.3 Identification
R-6.4.3-001
R-6.4.3-002
6.4.4 Membership/affiliation
R-6.4.4-001
R-6.4.4-002
R-6.4.4-002a
R-6.4.4-003
R-6.4.4-004
6.4.5 Membership/affiliation list
R-6.4.5-001
R-6.4.5-002
R-6.4.5-003
R-6.4.5-003a
R-6.4.5-004
R-6.4.5-005
R-6.4.5-006
R-6.4.5-007
R-6.4.5-008
6.4.6 Authorized user remotely changes another MCX User’s affiliated and/or Selected MCX Service Group(s)
NA
6.4.6.1 Mandatory change
R-6.4.6.1-001
R-6.4.6.1-002
R-6.4.6.1-003
R-6.4.6.1-004
6.4.6.2 Negotiated change
R-6.4.6.2-001
R-6.4.6.2-002
R-6.4.6.2-003
R-6.4.6.2-004
R-6.4.6.2-005
R-6.4.6.2-006
6.4.7 Prioritization
R-6.4.7-001
R-6.4.7-002
R-6.4.7-003
R-6.4.7-004
6.4.8 Relay requirements
R-6.4.8-001
6.4.9 Administrative
R-6.4.9-001
R-6.4.9-002
R-6.4.9-003
R-6.4.9-004
R-6.4.9-005
R-6.4.9-006
6.5 Broadcast Group
NA
6.5.1 General Broadcast Group Communication
R-6.5.1-001
R-6.5.1-002
6.5.2 Group-Broadcast Group (e.g., announcement group)
R-6.5.2-001
6.5.3 User-Broadcast Group (e.g., system communication)
R-6.5.3-001
6.6 Dynamic group management (i.e., dynamic reporting)
NA
6.6.1 General dynamic regrouping
R-6.6.1-001
R-6.6.1-002
R-6.6.1-003
R-6.6.1-004
R-6.6.1-005
R-6.6.1-006
6.6.2 Group regrouping
NA
6.6.2.1 Service description
NA
6.6.2.2 Requirements
R-6.6.2.2-001
R-6.6.2.2-002
R-6.6.2.2-003
R-6.6.2.2-004
R-6.6.2.2-005
R-6.6.2.2-006
R-6.6.2.2-007
R-6.6.2.2-008
R-6.6.2.2-009
R-6.6.2.2-011
R-6.6.2.2-012
R-6.6.2.2-013
6.6.3 Temporary Broadcast Groups
R-6.6.3-001
R-6.6.3-001a
R-6.6.3-001b
R-6.6.3-002
6.6.4 User regrouping
NA
6.6.4.1 Service description
NA
6.6.4.2 Requirements
R-6.6.4.2-001
R-6.6.4.2-002
R-6.6.4.2-002a
R-6.6.4.2-002b
R-6.6.4.2-003
R-6.6.4.2-004
R-6.6.4.2-005
6.6.5 Dynamic Group Participation
NA
6.6.5.1 Service description
NA
6.6.5.2 Requirements
R-6.6.5.2-001
R-6.6.5.2-002
R-6.6.5.2-003
R-6.6.5.2-004
R-6.6.5.2-005
R-6.6.5.2-006
R-6.6.5.2-007
R-6.6.5.2-008
6.7 Private Communication
NA
6.7.1 Overview
NA
6.7.2 General requirements
R-6.7.2-001
R-6.7.2-002
R-6.7.2-003
R-6.7.2-004
R-6.7.2-005
R-6.7.2-006
6.7.3 Administrative
R-6.7.3-001
R-6.7.3-002
R-6.7.3-003
R-6.7.3-004
R-6.7.3-005
R-6.7.3-006
R-6.7.3-007
R-6.7.3-007a
R-6.7.3-008
6.7.4 Prioritization
R-6.7.4-001
R-6.7.4-002
R-6.7.4-003
R-6.7.4-004
R-6.7.4-005
R-6.7.4-006
R-6.7.4-007
6.7.5 Private Communication (without Floor control) commencement requirements
R-6.7.5-001
R-6.7.5-002
R-6.7.5-003
6.7.6 Private Communication (without Floor control) termination
R-6.7.6-001
R-6.7.6-002
6.8 MCX Service priority requirements
NA
6.8.1 General
R-6.8.1-001
R-6.8.1-002
R-6.8.1-003
R-6.8.1-004
R-6.8.1-005
R-6.8.1-006
R-6.8.1-007
R-6.8.1-008
R-6.8.1-009
R-6.8.1-010
R-6.8.1-011
R-6.8.1-012
R-6.8.1-013
R-6.8.1-014
R-6.8.1-015
R-6.8.1-016
6.8.2 3GPP system access controls
R-6.8.2-001
6.8.3 3GPP system admission controls
R-6.8.3-001
6.8.4 3GPP system scheduling controls
R-6.8.4-001
6.8.5 UE access controls
R-6.8.5-001
6.8.6 Mobility and load management
NA
6.8.6.1 Mission Critical mobility management according to priority
R-6.8.6.1-001
R-6.8.6.1-002
6.8.6.2 Load management
R-6.8.6.2-001
R-6.8.6.2-002
R-6.8.6.2-003
R-6.8.6.2-004
R-6.8.6.2-005
6.8.7 Application layer priorities
NA
6.8.7.1 Overview
NA
6.8.7.2 Requirements
R-6.8.7.2-001
R-6.8.7.2-002
R-6.8.7.2-003
R-6.8.7.2-004
R-6.8.7.2-005
R-6.8.7.2-006
R-6.8.7.2-007
R-6.8.7.2-008
R-6.8.7.2-009
R-6.8.7.2-010
6.8.8 Communication types based on priorities
NA
6.8.8.1 MCX Service Emergency Group Communication requirements
R-6.8.8.1-001
R-6.8.8.1-002
R-6.8.8.1-003
R-6.8.8.1-004
6.8.8.2 MCX Service Emergency Private Communication requirements
R-6.8.8.2-001
R-6.8.8.2-002
R-6.8.8.2-003
R-6.8.8.2-004
6.8.8.3 Imminent Peril Group Communication requirements
R-6.8.8.3-001
R-6.8.8.3-002
R-6.8.8.3-003
6.8.8.4 MCX Service Emergency Alert
NA
6.8.8.4.1 Requirements
R-6.8.8.4.1-001
R-6.8.8.4.1-002
R-6.8.8.4.1-003
R-6.8.8.4.1-004
R-6.8.8.4.1-005
R-6.8.8.4.1-006
6.8.8.4.2 MCX Service Emergency Alert cancellation requirements
R-6.8.8.4.2-001
R-6.8.8.4.2-002
6.8.8.9 Ad hoc Group Communication requirements
R-6.8.8.9-001
6.9 IDs and aliases
R-6.9-001
R-6.9-002
R-6.9-003
R-6.9-004
6.10 User Profile management
R-6.10-001
R-6.10-002
R-6.10-003
R-6.10-004
6.11 Support for multiple devices
R-6.11-001
R-6.11-002
R-6.11-003
6.12 Location
R-6.12-001
R-6.12-002
R-6.12-003
R-6.12-004
R-6.12-005
R-6.12-006
R-6.12-007
6.13 Security
NA
6.13.1 Overview
NA
6.13.2 Cryptographic protocols
R-6.13.2-001
R-6.13.2-002
R-6.13.2-003
6.13.3 Authentication
R-6.13.3-001
6.13.4 Access control
R-6.13.4-001
R-6.13.4-002
R-6.13.4-003
R-6.13.4-004
R-6.13.4-005
R-6.13.4-006
R-6.13.4-007
R-6.13.4-008
R-6.13.4-009
R-6.13.4-010
6.13.5 Regulatory issues
R-6.13.5-001
6.13.6 Storage control
R-6.13.6-001
6.14 Interactions for MCX Service Group Communications and MCX Service Private Communications
R-6.14-001
R-6.14-002
6.15 Additional services for MCX Service communications
NA
6.15.1 Discreet listening capabilities
R-6.15.1-001a
R-6.15.1-001
R-6.15.1-002
R-6.15.1-002a
R-6.15.1-003
R-6.15.1-004
6.15.2 Ambient listening
NA
6.15.2.1 Overview of ambient listening
NA
6.15.2.2 Ambient listening requirements
NA
6.15.2.2.1 General ambient listening requirements
R-6.15.2.2.1-001
R-6.15.2.2.1-002
R-6.15.2.2.1-003
6.15.2.2.2 Remotely initiated ambient listening requirements
R-6.15.2.2.2-001
R-6.15.2.2.2-002
6.15.2.2.3 Locally initiated ambient listening requirements
R-6.15.2.2.3-001
R-6.15.2.2.3-002
6.15.3 Remotely initiated MCX Service Communication
NA
6.15.3.1 Overview
NA
6.15.3.2 Requirements
R-6.15.3.2-001
R-6.15.3.2-002
R-6.15.3.2-003
R-6.15.3.2-004
6.15.4 Recording and audit requirements
R-6.15.4-001
R-6.15.4-002
R-6.15.4-003
R-6.15.4-004
R-6.15.4-005
R-6.15.4-006
R-6.15.4-007
R-6.15.4-008
R-6.15.4-009
R-6.15.4-010
R-6.15.4-011
6.15.5 MCX Service Ad hoc Group Communication
NA
6.15.5.1 Overview
NA
6.15.5.2 General Aspects
R-6.15.5.2-001
R-6.15.5.2-001a
R-6.15.5.2-001b
R-6.15.5.2-001c
[[SUGGESTION_START]]R-6.15.5.2-001[[SUGGESTION_END]][[SUGGESTION_START]]d [[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]2[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]3[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]4[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]5[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]6[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]7[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]8[[SUGGESTION_END]]
R-6.15.5.2-0[[SUGGESTION_START]]09[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]0[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]1[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]2[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]3[[SUGGESTION_END]]
R-6.15.5.2-014
R-6.15.5.2-01[[SUGGESTION_START]]4a[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.5.2-015[[SUGGESTION_END]]
6.15.5.3 Administrative
R-6.15.5.3-001
R-6.15.5.3-002
R-6.15.5.3-003
R-6.15.5.3-004
R-6.15.5.3-005
6.15.5.4 Notification and acknowledgement for MCX Service Ad hoc Group Communications
R-6.15.5.4-001
[[SUGGESTION_START]]R-6.15.5.4-001a[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.5.4-002[[SUGGESTION_END]]
6.15.6 MCX Service Ad hoc Group Emergency Alert
NA
6.15.6.1 Overview
NA
6.15.6.2 General Aspects
R-6.15.6.2-001
R-6.15.6.2-002
R-6.15.6.2-002a
R-6.15.6.2-003
R-6.15.6.2-004
R-6.15.6.2-005
R-6.15.6.2-005a
R-6.15.6.2-005b
R-6.15.6.2-006
R-6.15.6.2-007
R-6.15.6.2-008
6.15.6.3 Administrative
R-6.15.6.3-001
R-6.15.6.3-002
R-6.15.6.3-003
R-6.15.6.3-004
R-6.15.6.3-005
6.16 Interaction with telephony services
R-6.16-001
R-6.16-002
6.17 Interworking
NA
6.17.1 Non-3GPP access
R-6.17.1-001
6.17.2 Interworking between MCX Service systems
R-6.17.2-001
R-6.17.2-002
R-6.17.2-003
R-6.17.2-004
R-6.17.2-005
R-6.17.2-006
R-6.17.2-007
R-6.17.2-008
6.17.3 Interworking with non-MCX Service systems
NA
6.17.3.1 GSM-R
R-6.17.3.1-001
R-6.17.3.1-002
R-6.17.3.1-003
R-6.17.3.1-004
R-6.17.3.1-005
6.17.3.2 External systems
R.6.17.3.2-001
R.6.17.3.2-002
6.18 MCX Service coverage extension using ProSe UE-to-Network Relays
R-6.18-001
R-6.18-002
R-6.18-003
R-6.18-004
R-6.18-005
R-6.18-006
6.19 Additional MCX Service requirements
NA
6.19.1 Communication rejection and queuing
NA
6.19.1.1 Requirements
R-6.19.1.1-001
R-6.19.1.1-002
R-6.19.1.1-003
R-6.19.1.1-004
R-6.19.1.1-005
R-6.19.1.1-006
R-6.19.1.1-007
7 MCX Service requirements specific to off-network use
NA
7.1 Off-network communications overview
NA
7.2 General off-network MCX Service requirements
R-7.2-001
R-7.2-002
R-7.2-003
R-7.2-004
R-7.2-005
7.3 Admission control
NA
7.3.1 General aspects
R-7.3.1-001
R-7.3.1-002
R-7.3.1-003
7.3.2 Communication initiation
R-7.3.2-001
R-7.3.2-002
R-7.3.2-003
R-7.3.2-004
R-7.3.2-005
7.4 Communication termination
R-7.4-001
R-7.4-002
R-7.4-003
R-7.4-004
7.5 Broadcast Group
R-7.5-001
R-7.5-002
7.6 MCX Service priority requirements
R-7.6-001
R-7.6-002
R-7.6-003
R-7.6-004
R-7.6-005
R-7.6-006
R-7.6-007
R-7.6-008
R-7.6-009
7.7 Communication types based on priorities
NA
7.7.1 MCX Service Emergency Group Communication requirements
R-7.7.1-001
R-7.7.1-002
R-7.7.1-003
7.7.2 MCX Service Emergency Group Communication cancellation requirements
R-7.7.2-001
7.7.3 Imminent Peril Communication
NA
7.7.3.1 Imminent Peril Group Communication requirements
R-7.7.3.1-001
R-7.7.3.1-002
R-7.7.3.1-003
R-7.7.3.1-004
R-7.7.3.1-005
7.7.3.2 Imminent Peril Group Communication cancellation requirements
R-7.7.3.2-001
R-7.7.3.2-002
7.8 Location
R-7.8-001
R-7.8-002
R-7.8-003
7.9 Security
R-7.9-001
R-7.9-002
7.10 Off-network MCX Service operations
R-7.10-001
R-7.10-002
R-7.10-003
7.11 Off-network UE functionality
R-7.11-001
R-7.11-002
R-7.11-003
7.12 Streaming for ProSe UE-to-UE Relay and UE-to-Network Relay
NA
7.12.1 UE-to-Network Relay for all data types
R-7.12.1-001
R-7.12.1-002
R-7.12.1-003
R-7.12.1-004
7.12.2 UE-to-UE Relay streaming
R-7.12.2-001
R-7.12.2-002
R-7.12.2-003
7.12.3 Off-Network streaming
R-7.12.3-001
R-7.12.3-002
R-7.12.3-003
7.13 Switching to off-network MCX Service
R-7.13-001
R-7.13-002
R-7.13-003
R-7.13-004
R-7.13-005
7.14 Off-network recording and audit requirements
R-7.14-001
R-7.14-001a
R-7.14-002
R-7.14-002a
7.15 Off-network UE-to-UE relay
NA
7.15.1 Private Communications
R-7.15.1-001
R-7.15.1-002
R-7.15.1-003
7.15.2 Group Communications
R-7.15.2-001
R-7.15.2-002
7.16 Off-network Ad hoc Group Communication
R-7.16-001
8 Inter-MCX Service interworking
NA
8.1 Inter-MCX Service interworking overview
NA
8.2 Concurrent Operation of Different MCX Services
NA
8.2.1 Overview
NA
8.2.2 Requirements
R-8.2.2-001
R-8.2.2-002
R-8.2.2-003
R-8.2.2-004
R-8.2.2-005
R-8.2.2-006
R-8.2.2-007
8.3 Use of unsharable resources within a UE
R-8.3-001
R-8.3-002
R-8.3-003
R-8.3-004
R-8.3-005
R-8.3-006
8.4 Single Group with multiple MCX Services
NA
8.4.1 Overview
NA
8.4.2 Requirements
R-8.4.2-001
R-8.4.2-002
R-8.4.2-003
R-8.4.2-004
R-8.4.2-005
8.4.3 Compatibility of UE
NA
8.4.3.1 Advertising service capabilities required
R-8.4.3.1-001
R-8.4.3.1-002
R-8.4.3.1-003
R-8.4.3.1-004
8.4.3.2 Conversion between capabilities
R-8.4.3.2-001
8.4.4 Individual permissions for service access
R-8.4.4-001
8.4.5 Common alias and user identities or mappable
R-8.4.5-001
8.4.6 Single location message
R-8.4.6-001
R-8.4.6-002
R-8.4.6-003
8.5 Priority between services
NA
8.5.1 Overview
NA
8.5.2 Requirements
R-8.5.2-001
R-8.5.2-002
R-8.5.2-003
R-8.5.2-004
R-8.5.2-005
9 Air Ground Air Communications
NA
9.1 Service description
NA
9.2 Requirements
R-9.2-001
10 MCX Service in IOPS mode
R-10-001
Annex C (normative):
MCCoRe Requirements for MCData
Table C.1 provides an exhaustive list of those requirements in 3GPP TS 22.280 that are applicable to MCData.
Table C.1
5 MCX Service requirements common for on the network and off the network
NA
5.1 General Group Communications requirements
NA
5.1.1 General aspects
R-5.1.1-001
R-5.1.1-002
R-5.1.1-003
R-5.1.1-006
5.1.2 Group/status information
NA
5.1.3 Group configuration
R-5.1.3-001
R5.1.3-002
5.1.4 Identification
R-5.1.4-001
5.1.5 Membership/affiliation
R-5.1.5-001
R-5.1.5-002
R-5.1.5-003
R-5.1.5-005
R-5.1.5-007
R-5.1.5-008
5.1.6 Group Communication administration
NA
5.1.7 Prioritization
R-5.1.7-001
R-5.1.7-002
5.1.8 Charging requirements for MCX Service
R-5.1.8-001
R-5.1.8-003
R-5.1.8-004
R-5.1.8-005
R-5.1.8-006
R-5.1.8-007
R-5.1.8-008
R-5.1.8-009
R-5.1.8-010
R-5.1.8-011
5.1.9 MCX Service Emergency Alert triggered by location
R-5.1.9-001
R-5.1.9-002
5.2 Broadcast Group
NA
5.2.1 General Broadcast Group Communication
R-5.2.1-002
5.2.2 Group-Broadcast Group (e.g., announcement group)
R-5.2.2-001
R-5.2.2-002
R-5.2.2-003
R-5.2.2-004
5.2.3 User-Broadcast Group (e.g., System Communication)
R-5.2.3-001
R-5.2.3-002
5.3 Late communication entry
NA
5.4 Receiving from multiple MCX Service communications
5.4.1 Overview
NA
5.4.2 Requirements
R-5.4.2-001
R-5.4.2-004
R-5.4.2-004A
R-5.4.2-004B
R-5.4.2-005
R-5.4.2-006
R-5.4.2-007
R-5.4.2-007a
R-5.4.2-008
R-5.4.2-009
5.5 Private Communication
NA
5.5.1 Private Communication general requirements
R-5.5.1-001
5.5.2 Charging requirement for MCX Service
R-5.5.2-001
5.6 MCX Service priority requirements
NA
5.6.1 Overview
NA
5.6.2 Communication types based on priorities
NA
5.6.2.1 MCX Service Emergency and Imminent Peril general requirements
NA
5.6.2.1.1 Overview
NA
5.6.2.1.2 Requirements
R-5.6.2.1.2-001
R-5.6.2.1.2-002
R-5.6.2.1.2-003
R-5.6.2.1.2-004
R-5.6.2.1.2-005
5.6.2.2 MCX Service Emergency Group Communication
NA
5.6.2.2.1 MCX Service Emergency Group Communication requirements
R-5.6.2.2.1-001
R-5.6.2.2.1-002
R-5.6.2.2.1-003
R-5.6.2.2.1-004
R-5.6.2.2.1-005
R-5.6.2.2.1-006
R-5.6.2.2.1-007
R-5.6.2.2.1-008
R-5.6.2.2.1-009
R-5.6.2.2.1-010
R-5.6.2.2.1-011
R-5.6.2.2.1-012
R-5.6.2.2.1-013
R-5.6.2.2.1-014
5.6.2.2.2 MCX Service Emergency Group Communication cancellation requirements
R-5.6.2.2.2-001
R-5.6.2.2.2-002
R-5.6.2.2.2-003
R-5.6.2.2.2-004
R-5.6.2.2.2-005
5.6.2.3 MCX Service Imminent Peril Group
NA
5.6.2.3.1 MCX Service Imminent Peril Group Communication requirements
R-5.6.2.3.1-001
R-5.6.2.3.1-002
R-5.6.2.3.1-003
R-5.6.2.3.1-004
R-5.6.2.3.1-005
R-5.6.2.3.1-006
R-5.6.2.3.1-007
R-5.6.2.3.1-008
R-5.6.2.3.1-009
5.6.2.3.2 MCX Service Imminent Peril Group Communications cancellation requirements
R-5.6.2.3.2-001
R-5.6.2.3.2-002
R-5.6.2.3.2-003
R-5.6.2.3.2-004
5.6.2.4 MCX Service Emergency Alert
NA
5.6.2.4.1 MCX Service Emergency Alert requirements
R-5.6.2.4.1-001
R-5.6.2.4.1-002
R-5.6.2.4.1-003
R-5.6.2.4.1-004
R-5.6.2.4.1-004a
R-5.6.2.4.1-005
R-5.6.2.4.1-006
R-5.6.2.4.1-007
R-5.6.2.4.1-008
R-5.6.2.4.1-009
R-5.6.2.4.1-010
R-5.6.2.4.1-011
R-5.6.2.4.1-012
R-5.6.2.4.1-013
5.6.2.4.2 MCX Service Emergency Alert cancellation requirements
R-5.6.2.4.2-001
R-5.6.2.4.2-002
R-5.6.2.4.2-003
5.7 MCX Service User ID
R-5.7-001
R-5.7-002
R-5.7-003
5.8 MCX UE Management
R-5.8-001
R-5.8-002
5.9 MCX Service User Profile
R-5.9-001
R-5.9-002
5.9A Functional alias
R-5.9a-001
R-5.9a-001a
R-5.9a-001b
R-5.9a-001c
R-5.9a-002
R-5.9a-002a
R-5.9a-003
R-5.9a-004
R-5.9a-005
R-5.9a-006
R-5.9a-007
R-5.9a-008
R-5.9a-009
R-5.9a-010
R-5.9a-011
R-5.9a-012
R-5.9a-013
R-5.9a-014
R-5.9a-015
R-5.9a-016
R-5.9a-017
R-5.9a-018
R-5.9a-019
R-5.9a-020
R-5.9a-021
R-5.9a-022
R-5.9a-023
R-5.9a-024
R-5.9a-025
R-5.9a-026
R-5.9a-027
R-5.9a-028
R-5.9a-029
R-5.9a-030
R-5.9a-031
5.10 Support for multiple devices
R-5.10-001
R-5.10-001a
R-5.10-002
5.11 Location
R-5.11-001
R-5.11-002
R-5.11-002a
R-5.11-003
R-5.11-004
R-5.11-005
R-5.11-006
R-5.11-007
R-5.11-008
R-5.11-009
R-5.11-010
R-5.11-011
R-5.11-012
R-5.11-013
R-5.11-014
R-5.11-015
5.12 Security
R-5.12-001
R-5.12-002
R-5.12-003
R-5.12-004
R-5.12-005
R-5.12-006
R-5.12-007
R-5.12-008
R-5.12-009
R-5.12-010
R-5.12-011
R-5.12-012
R-5.12-013
R-5.12-014
5.13 Media quality
NA
5.14 Relay requirements
R-5.14-001
R-5.14-002
R-5.14-003
R-5.14-004
5.15 Gateway requirements
R-5.15-001
R-5.15-002
R-5.15-003
5.16 Control and management by Mission Critical Organizations
NA
5.16.1 Overview
NA
5.16.2 General requirements
R-5.16.2-001
R-5.16.2-002
R-5.16.2-003
R-5.16.2-004
R-5.16.2-005
5.16.3 Operational visibility for Mission Critical Organizations
R-5.16.3-001
5.17 General administrative – groups and users
R-5.17-001
R-5.17-002
R-5.17-003
R-5.17-004
R-5.17-005
R-5.17-006
R-5.17-007
R-5.17-008
5.18 Open interfaces for MCX services
NA
5.18.1 Overview
NA
5.18.2 Requirements
R-5.18.2-001
R-5.18.2-002
R-5.18.2-003
R-5.18.2-004
5.19 Media forwarding
NA
5.19.1 Service description
NA
5.19.2 Requirements
R-5.19.2-001
R-5.19.2-002
R-5.19.2-003
5.20 Receipt notification
NA
5.20.1 Service description
NA
5.20.2 Requirements
R-5.20.2-001
5.21 Additional services for MCX Service communications
NA
5.21.1 Remotely initiated MCX Service communication
NA
5.21.1.1 Overview
NA
5.21.1.2 Requirements
R-5.21.1.2-001
R-5.21.1.2-002
R-5.21.1.2-003
R-5.21.1.2-004
5.21.2 Remotely terminated MCX Service communication
NA
5.21.2.1 Requirements
R-5.21.2.1-001
6 MCX Service requirements specific to on-network use
NA
6.1 General administrative – groups and users
R-6.1-001
R-6.1-002
R-6.1-003
R-6.1-004
R-6.1-005
6.2 MCX Service communications
NA
6.2.1 Notification and acknowledgement for MCX Service Group Communications
R-6.2.1-001
R-6.2.1-002
R-6.2.1-003
R-6.2.1-004
R-6.2.1-005
6.2.2 Queuing
NA
6.3 General requirements
R-6.3-001
R-6.3-002
R-6.3-003
R-6.3-004
6.4 General group communication
NA
6.4.1 General aspects
R-6.4.1-001
6.4.2 Group status/information
R-6.4.2-005
R-6.4.2-001
R-6.4.2-002
R-6.4.2-003
R-6.4.2-004
R-6.4.2-006
R-6.4.2-007
6.4.3 Identification
R-6.4.3-001
R-6.4.3-002
6.4.4 Membership/affiliation
R-6.4.4-001
R-6.4.4-002
R-6.4.4-002a
R-6.4.4-003
R-6.4.4-004
6.4.5 Membership/affiliation list
R-6.4.5-001
R-6.4.5-002
R-6.4.5-003
R-6.4.5-003a
R-6.4.5-004
R-6.4.5-005
R-6.4.5-006
R-6.4.5-007
R-6.4.5-008
6.4.6 Authorized user remotely changes another MCX User’s affiliated and/or Selected MCX Service Group(s)
NA
6.4.6.1 Mandatory change
R-6.4.6.1-001
R-6.4.6.1-002
R-6.4.6.1-003
R-6.4.6.1-004
6.4.6.2 Negotiated change
R-6.4.6.2-001
R-6.4.6.2-002
R-6.4.6.2-003
R-6.4.6.2-004
R-6.4.6.2-005
R-6.4.6.2-006
6.4.7 Prioritization
R-6.4.7-001
R-6.4.7-002
R-6.4.7-003
R-6.4.7-004
6.4.8 Relay requirements
R-6.4.8-001
6.4.9 Administrative
R-6.4.9-001
R-6.4.9-004
R-6.4.9-006
6.5 Broadcast Group
NA
6.5.1 General Broadcast Group Communication
NA
6.5.2 Group-Broadcast Group (e.g., announcement group)
NA
6.5.3 User-Broadcast Group (e.g., system communication)
NA
6.6 Dynamic group management (i.e., dynamic reporting)
NA
6.6.1 General dynamic regrouping
R-6.6.1-001
R-6.6.1-002
R-6.6.1-003
R-6.6.1-004
R-6.6.1-005
R-6.6.1-006
6.6.2 Group regrouping
NA
6.6.2.1 Service description
NA
6.6.2.2 Requirements
R-6.6.2.2-001
R-6.6.2.2-002
R-6.6.2.2-003
R-6.6.2.2-004
R-6.6.2.2-005
R-6.6.2.2-006
R-6.6.2.2-007
R-6.6.2.2-008
R-6.6.2.2-009
R-6.6.2.2-011
R-6.6.2.2-012
R-6.6.2.2-013
6.6.3 Temporary Broadcast Groups
R-6.6.3-001
R-6.6.3-001a
R-6.6.3-001b
R-6.6.3-002
6.6.4 User regrouping
NA
6.6.4.1 Service description
NA
6.6.4.2 Requirements
R-6.6.4.2-001
R-6.6.4.2-002
R-6.6.4.2-002a
R-6.6.4.2-002b
R-6.6.4.2-003
R-6.6.4.2-004
R-6.6.4.2-005
6.6.5 Dynamic Group Participation
NA
6.6.5.1 Service description
NA
6.6.5.2 Requirements
R-6.6.5.2-001
R-6.6.5.2-002
R-6.6.5.2-003
R-6.6.5.2-004
R-6.6.5.2-005
R-6.6.5.2-006
R-6.6.5.2-007
R-6.6.5.2-008
6.7 Private Communication
NA
6.7.1 Overview
NA
6.7.2 General requirements
R-6.7.2-001
R-6.7.2-002
R-6.7.2-003
R-6.7.2-004
R-6.7.2-005
R-6.7.2-006
6.7.3 Administrative
R-6.7.3-001
R-6.7.3-002
R-6.7.3-003
R-6.7.3-004
R-6.7.3-005
R-6.7.3-006
R-6.7.3-007
R-6.7.3-007a
R-6.7.3-008
6.7.4 Prioritization
R-6.7.4-001
R-6.7.4-002
R-6.7.4-003
R-6.7.4-004
R-6.7.4-005
R-6.7.4-006
R-6.7.4-007
6.7.5 Private Communication (without Floor control) commencement requirements
R-6.7.5-001
R-6.7.5-002
R-6.7.5-003
6.7.6 Private Communication (without Floor control) termination
R-6.7.6-001
R-6.7.6-002
6.8 MCX Service priority Requirements
NA
6.8.1 General
R-6.8.1-001
R-6.8.1-002
R-6.8.1-003
R-6.8.1-004
R-6.8.1-005
R-6.8.1-006
R-6.8.1-007
R-6.8.1-008
R-6.8.1-009
R-6.8.1-010
R-6.8.1-011
R-6.8.1-012
R-6.8.1-013
R-6.8.1-014
R-6.8.1-015
R-6.8.1-016
6.8.2 3GPP system access controls
R-6.8.2-001
6.8.3 3GPP system admission controls
R-6.8.3-001
6.8.4 3GPP system scheduling controls
R-6.8.4-001
6.8.5 UE access controls
R-6.8.5-001
6.8.6 Mobility and load Management
NA
6.8.6.1 Mission Critical mobility management according to priority
R-6.8.6.1-001
R-6.8.6.1-002
6.8.6.2 Load management
R-6.8.6.2-001
R-6.8.6.2-002
R-6.8.6.2-003
R-6.8.6.2-004
R-6.8.6.2-005
6.8.7 Application layer priorities
NA
6.8.7.1 Overview
NA
6.8.7.2 Requirements
R-6.8.7.2-001
R-6.8.7.2-002
R-6.8.7.2-003
R-6.8.7.2-004
R-6.8.7.2-005
R-6.8.7.2-006
R-6.8.7.2-007
R-6.8.7.2-008
R-6.8.7.2-009
R-6.8.7.2-010
6.8.8 Communication types based on priorities
NA
6.8.8.1 MCX Service Emergency Group Communication requirements
R-6.8.8.1-001
R-6.8.8.1-002
R-6.8.8.1-003
R-6.8.8.1-004
6.8.8.2 MCX Service Emergency Private Communication requirements
R-6.8.8.2-001
R-6.8.8.2-002
R-6.8.8.2-003
R-6.8.8.2-004
6.8.8.3 Imminent Peril Group Communication requirements
R-6.8.8.3-001
R-6.8.8.3-002
R-6.8.8.3-003
6.8.8.4 MCX Service Emergency Alert
NA
6.8.8.4.1 Requirements
R-6.8.8.4.1-001
R-6.8.8.4.1-002
R-6.8.8.4.1-003
R-6.8.8.4.1-004
R-6.8.8.4.1-005
R-6.8.8.4.1-006
6.8.8.4.2 MCX Service Emergency Alert cancellation requirements
R-6.8.8.4.2-001
R-6.8.8.4.2-002
6.8.8.9 Ad hoc Group Communication requirements
R-6.8.8.9-001
6.9 IDs and aliases
R-6.9-001
R-6.9-002
R-6.9-003
R-6.9-004
6.10 User Profile management
R-6.10-001
R-6.10-002
R-6.10-003
R-6.10-004
6.11 Support for multiple devices
R-6.11-001
R-6.11-002
R-6.11-003
6.12 Location
R-6.12-001
R-6.12-002
R-6.12-003
R-6.12-004
R-6.12-005
R-6.12-006
R-6.12-007
6.13 Security
NA
6.13.1 Overview
NA
6.13.2 Cryptographic protocols
R-6.13.2-001
R-6.13.2-002
R-6.13.2-003
6.13.3 Authentication
R-6.13.3-001
6.13.4 Access control
R-6.13.4-001
R-6.13.4-002
R-6.13.4-003
R-6.13.4-004
R-6.13.4-005
R-6.13.4-006
R-6.13.4-007
R-6.13.4-008
R-6.13.4-009
R-6.13.4-010
6.13.5 Regulatory issues
R-6.13.5-001
6.13.6 Storage control
R-6.13.6-001
6.14 Interactions for MCX Service Group Communications and MCX Service Private Communications
R-6.14-001
R-6.14.002
6.15 Additional services for MCX Service communications
NA
6.15.1 Discreet listening capabilities
R-6.15.1-001a
R-6.15.1-001
R-6.15.1-002
R-6.15.1-002a
R-6.15.1-003
R-6.15.1-004
6.15.2 Ambient listening
NA
6.15.2.1 Overview of ambient listening
NA
6.15.2.2 Ambient listening requirements
NA
6.15.2.2.1 General ambient listening requirements
R-6.15.2.2.1-001
R-6.15.2.2.1-002
R-6.15.2.2.1-003
6.15.2.2.2 Remotely initiated ambient listening requirements
R-6.15.2.2.2-001
R-6.15.2.2.2-002
6.15.2.2.3 Locally initiated ambient listening requirements
R-6.15.2.2.3-001
R-6.15.2.2.3-002
6.15.3 Remotely initiated MCX Service communication
NA
6.15.3.1 Overview
NA
6.15.3.2 Requirements
R-6.15.3.2-002
R-6.15.3.2-001
6.15.4 Recording and audit requirements
R-6.15.4-001
R-6.15.4-002
R-6.15.4-003
R-6.15.4-004
R-6.15.4-005
R-6.15.4-006
R-6.15.4-007
R-6.15.4-008
R-6.15.4-009
R-6.15.4-010
R-6.15.4-011
6.15.5 MCX Service Ad hoc Group Communication
NA
6.15.5.1 Overview
NA
6.15.5.2 General Aspects
R-6.15.5.2-001
R-6.15.5.2-001a
R-6.15.5.2-001b
R-6.15.5.2-001c
R-6.15.5.2-00[[SUGGESTION_START]]1d[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]2[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]3[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]4[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]5[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]6[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]7[[SUGGESTION_END]]
R-6.15.5.2-00[[SUGGESTION_START]]8[[SUGGESTION_END]]
R-6.15.5.2-0[[SUGGESTION_START]]09[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]0[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]1[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]2[[SUGGESTION_END]]
R-6.15.5.2-01[[SUGGESTION_START]]3[[SUGGESTION_END]]
R-6.15.5.2-014
R-6.15.5.2-01[[SUGGESTION_START]]4a[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.5.2-015[[SUGGESTION_END]]
6.15.5.3 Administrative
R-6.15.5.3-001
R-6.15.5.3-002
R-6.15.5.3-003
R-6.15.5.3-004
R-6.15.5.3-005
6.15.5.4 Notification and acknowledgement for MCX Service Ad hoc Group Communications
R-6.15.5.4-001
[[SUGGESTION_START]]R-6.15.5.4-001a[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.5.4-002[[SUGGESTION_END]]
6.15.6 MCX Service Ad hoc Group Emergency Alert
NA
6.15.6.1 Overview
NA
6.15.6.2 General Aspects
R-6.15.6.2-001
R-6.15.6.2-002
R-6.15.6.2-002a
R-6.15.6.2-003
R-6.15.6.2-004
R-6.15.6.2-005
R-6.15.6.2-005a
R-6.15.6.2-005b
R-6.15.6.2-006
R-6.15.6.2-007
R-6.15.6.2-008
6.15.6.3 Administrative
R-6.15.6.3-001
R-6.15.6.3-002
R-6.15.6.3-003
R-6.15.6.3-004
R-6.15.6.3-005
6.16 Interaction with telephony services
R-6.16-001
R-6.16-002
6.17 Interworking
NA
6.17.1 Non-3GPP access
R-6.17.1-001
6.17.2 Interworking between MCX Service systems
R-6.17.2-001
R-6.17.2-002
R-6.17.2-003
R-6.17.2-004
R-6.17.2-005
R-6.17.2-006
R-6.17.2-007
R-6.17.2-008
6.17.3 Interworking with non-MCX Service systems
NA
6.17.3.1 GSM-R
R-6.17.3.1-001
6.17.3.2 External systems
R.6.17.3.2-001
R.6.17.3.2-002
6.18 MCX Service coverage extension using ProSe UE-to-Network Relays
R-6.18-001
R-6.18-002
R-6.18-003
R-6.18-004
R-6.18-005
R-6.18-006
6.19 Additional MCX Service requirements
NA
6.19.1 Communication rejection and queuing
NA
6.19.1.1 Requirements
R-6.19.1.1-001
R-6.19.1.1-002
R-6.19.1.1-003
R-6.19.1.1-004
R-6.19.1.1-005
R-6.19.1.1-006
R-6.19.1.1-007
7 MCX Service requirements specific to off-network use
NA
7.1 Off-network communications overview
NA
7.2 General off-network MCX Service requirements
R-7.2-001
R-7.2-002
R-7.2-003
R-7.2-004
R-7.2-005
7.3 Admission control
NA
7.3.1 General aspects
R-7.3.1-001
R-7.3.1-002
R-7.3.1-003
7.3.2 Communication initiation
R-7.3.2-001
R-7.3.2-002
R-7.3.2-003
R-7.3.2-004
R-7.3.2-005
7.4 Communication termination
R-7.4-001
R-7.4-002
R-7.4-003
R-7.4-004
7.5 Broadcast Group
NA
7.6 MCX Service priority requirements
R-7.6-001
R-7.6-002
R-7.6-003
R-7.6-004
R-7.6-005
R-7.6-006
R-7.6-007
R-7.6-008
R-7.6-009
7.7 Communication types based on priorities
NA
7.7.1 MCX Service Emergency Group Communication requirements
NA
7.7.2 MCX Service Emergency Group Communication cancellation requirements
NA
7.7.3 Imminent Peril Communication
NA
7.7.3.1 Imminent Peril Group Communication requirements
NA
7.7.3.2 Imminent Peril Group Communication cancellation requirements
NA
7.8 Location
R-7.8-001
R-7.8-002
R-7.8-003
7.9 Security
R-7.9-001
R-7.9-002
7.10 Off-network MCX Service operations
R-7.10-001
R-7.10-002
R-7.10-003
7.11 Off-network UE functionality
R-7.11-001
R-7.11-002
R-7.11-003
7.12 Streaming for ProSe UE-to-UE Relay and UE-to-Network Relay
NA
7.12.1 UE-to-Network Relay for all data types
R-7.12.1-001
R-7.12.1-002
R-7.12.1-003
R-7.12.1-004
7.12.2 UE-to-UE Relay streaming
R-7.12.2-001
R-7.12.2-002
R-7.12.2-003
7.12.3 Off-Network streaming
R-7.12.3-001
R-7.12.3-002
R-7.12.3-003
7.13 Switching to off-network MCX Service
R-7.13-001
R-7.13-002
R-7.13-003
R-7.13-004
R-7.13-005
7.14 Off-network recording and audit requirements
R-7.14-001
R-7.14-001a
R-7.14-002
R-7.14-002a
7.15 Off-network UE-to-UE relay
NA
7.15.1 Private Communications
R-7.15.1-001
R-7.15.1-002
R-7.15.1-003
7.16 Off-network Ad hoc Group Communication
R-7.16-001
7.15.2 Group Communications
R-7.15.2-001
R-7.15.2-002
8 Inter-MCX Service interworking
NA
8.1 Inter-MCX Service interworking overview
NA
8.2 Concurrent operation of different MCX Services
NA
8.2.1 Overview
NA
8.2.2 Requirements
R-8.2.2-001
R-8.2.2-002
R-8.2.2-003
R-8.2.2-004
R-8.2.2-005
R-8.2.2-006
R-8.2.2-007
8.3 Use of unsharable resources within a UE
R-8.3-001
R-8.3-002
R-8.3-003
R-8.3-004
R-8.3-005
R-8.3-006
8.4 Single Group with multiple MCX Services
NA
8.4.1 Overview
NA
8.4.2 Requirements
R-8.4.2-001
R-8.4.2-002
R-8.4.2-003
R-8.4.2-004
R-8.4.2-005
8.4.3 Compatibility of UE
NA
8.4.3.1 Advertising service capabilities required
R-8.4.3.1-001
R-8.4.3.1-002
R-8.4.3.1-003
R-8.4.3.1-004
8.4.3.2 Conversion between capabilities
R-8.4.3.2-001
8.4.4 Individual permissions for service access
R-8.4.4-001
8.4.5 Common alias and user identities or mappable
R-8.4.5-001
8.4.6 Single location message
R-8.4.6-001
R-8.4.6-002
R-8.4.6-003
8.5 Priority between services
NA
8.5.1 Overview
NA
8.5.2 Requirements
R-8.5.2-001
R-8.5.2-002
R-8.5.2-003
R-8.5.2-004
R-8.5.2-005
9 Air Ground Air Communications
NA
9.1 Service description
NA
9.2 Requirements
R-9.2-001
10 MCX Service in IOPS mode
R-10-001
End of changes
|
S1-253380.zip
|
2026-01-13T17:33:38.090747
|
S1-253381
|
SA1
|
TSGS1_111_Goteborg
|
CR
|
agreed
|
FRMCS_Ph6 – Normative [SP-250277]
|
3GPP TSG-SA WG1 Meeting #111 S1-253381
Gothenburg, Sweden, 25-29 August 2025 (revision of S1-253263)
CR-Form-v12.3
CHANGE REQUEST
22.280
CR
0180
rev
1
Current version:
20.0.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
X
Radio Access Network
Core Network
X
Title:
Addition of Functional Aliases in the notifications of AHG emergency alert and combining AHG emergency alerts
Source to WG:
UIC, Nokia
Source to TSG:
S1
Work item code:
FRMCS_Ph6-REQ
Date:
2025-08-28
Category:
C
Release:
Rel-20
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
Enhance alignement with functional requirements of UIC specifications (FU-7120 v2.1.2) related to the usage of functional aliases of participants of an AHG emergency alert, based on criteria and combining of AHG emergency allerts.
Summary of change:
Addition of the functional aliases of the receiving participants in the notifications of the AHG emergency alert
Combining AHG emergency alerts (authorizations, configurations)
Consequences if not approved:
Misalignement with functional requirements of the UIC specifications
Missing stage-1 requirements for downstream groups to include the relevant features
Clauses affected:
6.15.6.2, 6.15.6.3, 6.15.6.4 (new), Annex A
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
Start of changes
6.15.6.2 General aspects
[R-6.15.6.2-001] The MCX Service shall support an MCX Service Ad hoc Group Emergency Alert capability, which on initiation by an MCX User causes that MCX UE to send an MCX Service Ad hoc Group Emergency Alert and may put that MCX User into the MCX Service Emergency State.
[R-6.15.6.2-002] The MCX Service shall provide a means for an authorized user to be able to activate the MCX Service Ad hoc Group Emergency Alert capability.
[R-6.15.6.2-002a] The MCX Service shall provide the reason for denial to an MCX user who was not authorised to activate the MCX Service Ad hoc Group Emergency Alert.
[R-6.15.6.2-003] The MCX Service Emergency Alert shall contain the following information: Location information, MCX Service User ID, Functional Alias, Criteria for determining list of participants, available MCX Services, additional information related to the alert, and the user's Mission Critical Organization name.
[R-6.15.6.2-004] The MCX Service shall provide a mechanism for the initiator of an MCX Service Ad hoc Group Emergency Alert to request that the list of participants is to be determined and updated by the MCX Service system using pre-defined criteria.
[R-6.15.6.2-004a] The MCX Service shall provide a mechanism for an authorised user to update the pre-defined criteria during an on-going Ad hoc Group Emergency Alert.
[R-6.15.6.2-005] The MCX Service Ad hoc Group Emergency Alert shall be distributed to the list of participants determined by the MCX Service system.
[R-6.15.6.2-005a] [[SUGGESTION_START]]Void [[SUGGESTION_END]]
[R-6.15.6.2-005b] When the list of participants is determined or updated by the MCX Service system, the MCX Service shall provide a mechanism that monitors and ensures that the participants list is applied for MCX Service Ad hoc Group Emergency Alert, performing retries when needed.
[R-6.15.6.2-006] The MCX Service shall support MCX Service Ad hoc Group Emergency Alert cancellation by authorized MCX Users.
[R-6.15.6.2-007] The MCX Service shall provide a mechanism for an authorised user which is participant of an active MCX Service Ad hoc Group Emergency Alert to set up group communications using the ad hoc group.
[R-6.15.6.2-008] When an MCX Service Ad hoc Group Emergency Alert is cancelled and ongoing calls in the ad hoc group are terminated the ad hoc group shall not persist.
[[SUGGESTION_START]][R-6.15.6.2-009] The MCX Service shall provide a mechanism for an authorised user to combine [[SUGGESTION_END]][[SUGGESTION_START]]multiple[[SUGGESTION_END]][[SUGGESTION_START]] ongoing Ad hoc Group Emergency Alerts.[[SUGGESTION_END]]
[[SUGGESTION_START]][R-6.15.6.2-010] If there are ongoing Ad hoc Group communications, associated to the combined Ad hoc Group Emergency Alerts, the MCX Service shall provide a mechanism for an authorised user to combine them.[[SUGGESTION_END]]
[[SUGGESTION_START]][R-6.15.[[SUGGESTION_END]][[SUGGESTION_START]]6[[SUGGESTION_END]][[SUGGESTION_START]].2-01[[SUGGESTION_END]][[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]]] The MCX Service shall provide to the participants of the combined Ad hoc Group [[SUGGESTION_END]][[SUGGESTION_START]]Emergency Alert[[SUGGESTION_END]][[SUGGESTION_START]], the same MCX User authorizations for modifying or terminating a[[SUGGESTION_END]][[SUGGESTION_START]]n Emergency Alert[[SUGGESTION_END]][[SUGGESTION_START]], as in the previous original Ad hoc Group [[SUGGESTION_END]][[SUGGESTION_START]]Emergency [[SUGGESTION_END]][[SUGGESTION_START]]Alerts[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
Next change
6.15.6.3 Administrative
[R-6.15.6.3-001] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users, within their authority, are authorized to initiate a MCX Service Ad hoc Group Emergency Alert.
[R-6.15.6.3-002] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure the maximum number of MCX Users who can participate in a MCX Service Ad hoc Group Emergency Alert.
[R-6.15.6.3-003] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users are authorized to participate in a MCX Service Ad hoc Group Emergency Alert.
[R-6.15.6.3-004] The MCX Service shall provide a mechanism for an MCX Service Administrator to define the default parameters for the ad hoc group resulting from the MCX Service Ad hoc Group Emergency Alert (e.g., priority, hang time, broadcast mode).
[R-6.15.6.3-005] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure whether MCX Service Ad hoc Group Emergency Alert is allowed on the MCX system regardless of individual MCX User authorizations.
[[SUGGESTION_START]][R-6.15.6.3-006] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure which MCX Users, within their authority, are authorized to combine Ad hoc Group Emergency Alerts. [[SUGGESTION_END]]
Next change
[[SUGGESTION_START]]6.15.6.4 [[SUGGESTION_END]][[SUGGESTION_START]]Notification and acknowledgement for MCX Service Ad hoc Group Emergency Alert[[SUGGESTION_END]]
[[SUGGESTION_START]][R-6.15.6.4-001] The MCX Service shall provide mechanisms for notification and acknowledgement of MCX Service Ad hoc Group Emergency Alert, including for each [[SUGGESTION_END]][[SUGGESTION_START]]MC Service[[SUGGESTION_END]][[SUGGESTION_START]]ID the corresponding Functional Alias, if available.[[SUGGESTION_END]]
[[SUGGESTION_START]][R-6.15.6.[[SUGGESTION_END]][[SUGGESTION_START]]4[[SUGGESTION_END]][[SUGGESTION_START]]-00[[SUGGESTION_END]][[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]]] [[SUGGESTION_END]][[SUGGESTION_START]]T[[SUGGESTION_END]][[SUGGESTION_START]]he MCX Service shall provide notifications to the relevant participants and authorized users, including, if[[SUGGESTION_END]] [[SUGGESTION_START]]available[[SUGGESTION_END]][[SUGGESTION_START]], the Functional aliases of the receiving participants.[[SUGGESTION_END]]
Next change
Annex A (normative):
MCCoRe Requirements for MCPTT
Table A.1 provides an exhaustive list of those requirements in 3GPP TS 22.280 that are applicable to MCPTT.
Table A.1
5 MCX Service Requirements common for on the network and off the network
NA
5.1 General Group Communications Requirements
NA
5.1.1 General aspects
R-5.1.1-001
R-5.1.1-002
R-5.1.1-003
R-5.1.1-004
R-5.1.1-005
5.1.2 Group/status information
R-5.1.2-001
R-5.1.2-002
5.1.3 Group configuration
R-5.1.3-001
R5.1.3-002
5.1.4 Identification
R-5.1.4-001
5.1.5 Membership/affiliation
R-5.1.5-001
R-5.1.5-002
R-5.1.5-003
R-5.1.5-004
R-5.1.5-005
R-5.1.5-006
R-5.1.5-007
R-5.1.5-008
5.1.6 Group Communication administration
R-5.1.6-001
5.1.7 Prioritization
R-5.1.7-001
R-5.1.7-002
5.1.8 Charging requirements for MCX Service
R-5.1.8-001
R-5.1.8-003
R-5.1.8-004
R-5.1.8-005
R-5.1.8-006
R-5.1.8-007
R-5.1.8-008
R-5.1.8-009
R-5.1.8-010
R-5.1.8-011
5.1.9 MCX Service Emergency Alert triggered by location
NA
5.2 Broadcast Group
NA
5.2.1 General Broadcast Group Communication
R-5.2.1-001
R-5.2.1-002
5.2.2 Group-Broadcast Group (e.g., announcement group)
R-5.2.2-001
R-5.2.2-002
R-5.2.2-003
R-5.2.2-004
5.2.3 User-Broadcast Group (e.g., System Communication)
R-5.2.3-001
R-5.2.3-002
5.3 Late communication entry
R-5.3-001
R-5.3-002
R-5.3-003
R-5.3-004
R-5.3-005
5.4 Receiving from multiple MCX Service communications
5.4.1 Overview
NA
5.4.2 Requirements
R-5.4.2-001
R-5.4.2-002
R-5.4.2-003
R-5.4.2-004
R-5.4.2-004A
R-5.4.2-004B
R-5.4.2-005
R-5.4.2-006
R-5.4.2-007
R-5.4.2-007a
R-5.4.2-008
R-5.4.2-009
5.5 Private Communication
NA
5.5.1 Private Communication general requirements
NA
5.5.2 Charging requirement for MCX Service
R-5.5.2-001
5.6 MCX Service priority requirements
NA
5.6.1 Overview
NA
5.6.2 Communication types based on priorities
NA
5.6.2.1 MCX Service Emergency and Imminent Peril general requirements
NA
5.6.2.1.1 Overview
NA
5.6.2.1.2 Requirements
R-5.6.2.1.2-001
R-5.6.2.1.2-002
R-5.6.2.1.2-003
R-5.6.2.1.2-004
R-5.6.2.1.2-005
5.6.2.2 MCX Service Emergency Group Communication
NA
5.6.2.2.1 MCX Service Emergency Group Communication requirements
R-5.6.2.2.1-001
R-5.6.2.2.1-002
R-5.6.2.2.1-003
R-5.6.2.2.1-004
R-5.6.2.2.1-005
R-5.6.2.2.1-006
R-5.6.2.2.1-007
R-5.6.2.2.1-008
R-5.6.2.2.1-009
R-5.6.2.2.1-010
R-5.6.2.2.1-011
R-5.6.2.2.1-012
R-5.6.2.2.1-013
R-5.6.2.2.1-014
5.6.2.2.2 MCX Service Emergency Group Communication cancellation requirements
R-5.6.2.2.2-001
R-5.6.2.2.2-002
R-5.6.2.2.2-003
R-5.6.2.2.2-004
R-5.6.2.2.2-005
5.6.2.3 MCX Service Imminent Peril Group
NA
5.6.2.3.1 MCX Service Imminent Peril Group Communication requirements
R-5.6.2.3.1-001
R-5.6.2.3.1-002
R-5.6.2.3.1-003
R-5.6.2.3.1-004
R-5.6.2.3.1-005
R-5.6.2.3.1-006
R-5.6.2.3.1-007
R-5.6.2.3.1-008
R-5.6.2.3.1-009
5.6.2.3.2 MCX Service Imminent Peril Group Communications cancellation requirements
R-5.6.2.3.2-001
R-5.6.2.3.2-002
R-5.6.2.3.2-003
R-5.6.2.3.2-004
5.6.2.4 MCX Service Emergency Alert
NA
5.6.2.4.1 MCX Service Emergency Alert requirements
R-5.6.2.4.1-001
R-5.6.2.4.1-002
R-5.6.2.4.1-003
R-5.6.2.4.1-004
R-5.6.2.4.1-004a
R-5.6.2.4.1-005
R-5.6.2.4.1-006
R-5.6.2.4.1-007
R-5.6.2.4.1-008
R-5.6.2.4.1-009
R-5.6.2.4.1-010
R-5.6.2.4.1-011
R-5.6.2.4.1-012
R-5.6.2.4.1-013
5.6.2.4.2 MCX Service Emergency Alert cancellation requirements
R-5.6.2.4.2-001
R-5.6.2.4.2-002
R-5.6.2.4.2-003
5.7 MCX Service User ID
R-5.7-001
R-5.7-002
R-5.7-003
5.8 MCX UE Management
R-5.8-001
R-5.8-002
5.9 MCX Service User Profile
R-5.9-001
R-5.9-002
5.9A Functional alias
R-5.9a-001
R-5.9a-001a
R-5.9a-001b
R-5.9a-001c
R-5.9a-002
R-5.9a-002a
R-5.9a-003
R-5.9a-004
R-5.9a-005
R-5.9a-006
R-5.9a-007
R-5.9a-008
R-5.9a-009
R-5.9a-010
R-5.9a-011
R-5.9a-012
R-5.9a-013
R-5.9a-014
R-5.9a-015
R-5.9a-016
R-5.9a-017
R-5.9a-018
R-5.9a-019
R-5.9a-020
R-5.9a-021
R-5.9a-022
R-5.9a-023
[R-5.9a-024
R-5.9a-025
R-5.9a-026
R-5.9a-027
R-5.9a-028
R-5.9a-029
R-5.9a-030
R-5.9a-031
5.10 Support for multiple devices
R-5.10-001
R-5.10-001a
R-5.10-002
5.11 Location
R-5.11-001
R-5.11-002
R-5.11-002a
R-5.11-003
R-5.11-004
R-5.11-005
R-5.11-006
R-5.11-007
R-5.11-008
R-5.11-009
R-5.11-010
R-5.11-011
R-5.11-013
R-5.11-014
R-5.11-015
R-5.11-015
5.12 Security
R-5.12-001
R-5.12-002
R-5.12-003
R-5.12-004
R-5.12-005
R-5.12-006
R-5.12-007
R-5.12-008
R-5.12-009
R-5.12-010
R-5.12-011
R-5.12-012
R-5.12-013
R5-12-014
5.13 Media quality
R-5.13-001
5.14 Relay requirements
R-5.14-001
R-5.14-002
R-5.14-003
R-5.14-004
5.15 Gateway requirements
R-5.15-001
R-5.15-002
R-5.15-003
5.16 Control and management by Mission Critical Organizations
NA
5.16.1 Overview
NA
5.16.2 General requirements
R-5.16.2-001
R-5.16.2-002
R-5.16.2-003
R-5.16.2-004
R-5.16.2-005
5.16.3 Operational visibility for Mission Critical Organizations
R-5.16.3-001
5.17 General administrative – groups and users
R-5.17-001
R-5.17-002
R-5.17-003
R-5.17-004
R-5.17-005
R-5.17-006
R-5.17-007
R-5.17-008
5.18 Open interfaces for MCX services
NA
5.18.1 Overview
NA
5.18.2 Requirements
NA
5.19 Media forwarding
NA
5.19.1 Service description
NA
5.19.2 Requirements
NA
5.20 Receipt notification
NA
5.20.1 Service description
NA
5.20.2 Requirements
NA
5.21 Additional services for MCX Service communications
NA
5.21.1 Remotely initiated MCX Service communication
NA
5.21.1.1 Overview
NA
5.21.1.2 Requirements
NA
5.21.2 Remotely terminated MCX Service communication
NA
5.21.2.1 Requirements
R-5.21.2.1-001
6 MCX Service requirements specific to on-network use
NA
6.1 General administrative – groups and users
R-6.1-001
R-6.1-002
R-6.1-003
R-6.1-004
R-6.1-005
6.2 MCX Service communications
NA
6.2.1 Notification and acknowledgement for MCX Service Group Communications
NA
6.2.2 Queuing
R-6.2.2-001
R-6.2.2-002
R-6.2.2-003
R-6.2.2-004
R-6.2.2-005
R-6.2.2-006
6.3 General requirements
R-6.3-001
R-6.3-002
R-6.3-003
R-6.3-004
6.4 General group communication
NA
6.4.1 General aspects
R-6.4.1-001
6.4.2 Group status/information
R-6.4.2-005
R-6.4.2-001
R-6.4.2-002
R-6.4.2-003
R-6.4.2-004
R-6.4.2-006
R-6.4.2-007
6.4.3 Identification
R-6.4.3-001
R-6.4.3-002
6.4.4 Membership/affiliation
R-6.4.4-001
R-6.4.4-002
R-6.4.4-002a
R-6.4.4-003
R-6.4.4-004
6.4.5 Membership/affiliation list
R-6.4.5-001
R-6.4.5-002
R-6.4.5-003
R-6.4.5-003a
R-6.4.5-004
R-6.4.5-005
R-6.4.5-006
R-6.4.5-007
R-6.4.5-008
6.4.6 Authorized user remotely changes another MCX User’s affiliated and/or Selected MCX Service Group(s)
NA
6.4.6.1 Mandatory change
R-6.4.6.1-001
R-6.4.6.1-002
R-6.4.6.1-003
R-6.4.6.1-004
6.4.6.2 Negotiated change
R-6.4.6.2-001
R-6.4.6.2-002
R-6.4.6.2-003
R-6.4.6.2-004
R-6.4.6.2-005
R-6.4.6.2-006
6.4.7 Prioritization
R-6.4.7-001
R-6.4.7-002
R-6.4.7-003
R-6.4.7-004
6.4.8 Relay requirements
R-6.4.8-001
6.4.9 Administrative
R-6.4.9-001
R-6.4.9-002
R-6.4.9-003
R-6.4.9-004
R-6.4.9-005
R-6.4.9-006
6.5 Broadcast Group
NA
6.5.1 General Broadcast Group Communication
R-6.5.1-001
R-6.5.1-002
6.5.2 Group-Broadcast Group (e.g., announcement group)
R-6.5.2-001
6.5.3 User-Broadcast Group (e.g., system communication)
R-6.5.3-001
6.6 Dynamic group management (i.e., dynamic reporting)
NA
6.6.1 General dynamic regrouping
R-6.6.1-001
R-6.6.1-002
R-6.6.1-003
R-6.6.1-004
R-6.6.1-005
R-6.6.1-006
6.6.2 Group regrouping
NA
6.6.2.1 Service description
NA
6.6.2.2 Requirements
R-6.6.2.2-001
R-6.6.2.2-002
R-6.6.2.2-003
R-6.6.2.2-004
R-6.6.2.2-005
R-6.6.2.2-006
R-6.6.2.2-007
R-6.6.2.2-008
R-6.6.2.2-009
R-6.6.2.2-010
R-6.6.2.2-011
R-6.6.2.2-012
R-6.6.2.2-013
6.6.3 Temporary Broadcast Groups
R-6.6.3-001
R-6.6.3-001a
R-6.6.3-001b
R-6.6.3-002
6.6.4 User regrouping
NA
6.6.4.1 Service description
NA
6.6.4.2 Requirements
R-6.6.4.2-001
R-6.6.4.2-002
R-6.6.4.2-002a
R-6.6.4.2-002b
R-6.6.4.2-003
R-6.6.4.2-004
R-6.6.4.2-005
6.6.5 Dynamic Group Participation
NA
6.6.5.1 Service description
NA
6.6.5.2 Requirements
R-6.6.5.2-001
R-6.6.5.2-002
R-6.6.5.2-003
R-6.6.5.2-004
R-6.6.5.2-005
R-6.654.2-006
R-6.6.5.2-007
R-6.6.5.2-008
6.7 Private Communication
NA
6.7.1 Overview
NA
6.7.2 General requirements
R-6.7.2-001
R-6.7.2-002
R-6.7.2-003
R-6.7.2-004
R-6.7.2-005
R-6.7.2-006
6.7.3 Administrative
R-6.7.3-001
R-6.7.3-002
R-6.7.3-003
R-6.7.3-004
R-6.7.3-005
R-6.7.3-006
R-6.7.3-007
R-6.7.3-007a
R-6.7.3-008
6.7.4 Prioritization
R-6.7.4-001
R-6.7.4-002
R-6.7.4-003
R-6.7.4-004
R-6.7.4-005
R-6.7.4-006
R-6.7.4-007
6.7.5 Private Communication (without Floor control) commencement requirements
R-6.7.5-001
R-6.7.5-002
R-6.7.5-003
6.7.6 Private Communication (without Floor control) termination
R-6.7.6-001
R-6.7.6-002
6.8 MCX Service priority requirements
NA
6.8.1 General
R-6.8.1-001
R-6.8.1-002
R-6.8.1-003
R-6.8.1-004
R-6.8.1-005
R-6.8.1-006
R-6.8.1-007
R-6.8.1-008
R-6.8.1-009
R-6.8.1-010
R-6.8.1-011
R-6.8.1-012
R-6.8.1-013
R-6.8.1-014
R-6.8.1-015
R-6.8.1-016
6.8.2 3GPP system access controls
R-6.8.2-001
6.8.3 3GPP system admission controls
R-6.8.3-001
6.8.4 3GPP system scheduling controls
R-6.8.4-001
6.8.5 UE access controls
R-6.8.5-001
6.8.6 Mobility and load management
NA
6.8.6.1 Mission Critical mobility management according to priority
R-6.8.6.1-001
R-6.8.6.1-002
6.8.6.2 Load management
R-6.8.6.2-001
R-6.8.6.2-002
R-6.8.6.2-003
R-6.8.6.2-004
R-6.8.6.2-005
6.8.7 Application layer priorities
NA
6.8.7.1 Overview
NA
6.8.7.2 Requirements
R-6.8.7.2-001
R-6.8.7.2-002
R-6.8.7.2-003
R-6.8.7.2-004
R-6.8.7.2-005
R-6.8.7.2-006
R-6.8.7.2-007
R-6.8.7.2-008
R-6.8.7.2-009
R-6.8.7.2-010
6.8.8 Communication types based on priorities
NA
6.8.8.1 MCX Service Emergency Group Communication requirements
R-6.8.8.1-001
R-6.8.8.1-002
R-6.8.8.1-003
R-6.8.8.1-004
6.8.8.2 MCX Service Emergency Private Communication requirements
NA
6.8.8.3 Imminent Peril Group Communication requirements
R-6.8.8.3-001
R-6.8.8.3-002
R-6.8.8.3-003
6.8.8.4 MCX Service Emergency Alert
NA
6.8.8.4.1 Requirements
R-6.8.8.4.1-001
R-6.8.8.4.1-002
R-6.8.8.4.1-003
R-6.8.8.4.1-004
R-6.8.8.4.1-005
R-6.8.8.4.1-006
6.8.8.4.2 MCX Service Emergency Alert cancellation requirements
R-6.8.8.4.2-001
R-6.8.8.4.2-002
6.8.8.X Ad hoc Group Communication requirements
R-6.8.8.X-001
6.9 IDs and aliases
R-6.9-001
R-6.9-002
R-6.9-003
R-6.9-004
6.10 User Profile management
R-6.10-001
R-6.10-002
R-6.10-003
R-6.10-004
6.11 Support for multiple devices
R-6.11-001
R-6.11-002
R-6.11-003
6.12 Location
R-6.12-001
R-6.12-002
R-6.12-003
R-6.12-004
R-6.12-005
R-6.12-006
R-6.12-007
6.13 Security
NA
6.13.1 Overview
NA
6.13.2 Cryptographic protocols
R-6.13.2-001
R-6.13.2-002
R-6.13.2-003
6.13.3 Authentication
R-6.13.3-001
6.13.4 Access control
R-6.13.4-001
R-6.13.4-002
R-6.13.4-003
R-6.13.4-004
R-6.13.4-005
R-6.13.4-006
R-6.13.4-007
R-6.13.4-008
R-6.13.4-009
R-6.13.4-010
6.13.5 Regulatory issues
R-6.13.5-001
6.13.6 Storage control
NA
6.14 Interactions for MCX Service Group Communications and MCX Service Private Communications
R-6.14-001
R-6.14-002
6.15 Additional services for MCX Service communications
NA
6.15.1 Discreet listening capabilities
R-6.15.1-001a
R-6.15.1-001
R-6.15.1-002
R-6.15.1-002a
R-6.15.1-003
R-6.15.1-004
6.15.2 Ambient listening
NA
6.15.2.1 Overview of ambient listening
NA
6.15.2.2 Ambient listening requirements
NA
6.15.2.2.1 General ambient listening requirements
R-6.15.2.2.1-001
R-6.15.2.2.1-002
R-6.15.2.2.1-003
6.15.2.2.2 Remotely initiated ambient listening requirements
R-6.15.2.2.2-001
R-6.15.2.2.2-002
6.15.2.2.3 Locally initiated ambient listening requirements
R-6.15.2.2.3-001
R-6.15.2.2.3-002
6.15.3 Remotely initiated MCX Service Communication
NA
6.15.3.1 Overview
NA
6.15.3.2 Requirements
R-6.15.3.2-001
R-6.15.3.2-002
R-6.15.3.2-003
R-6.15.3.2-004
6.15.4 Recording and audit requirements
R-6.15.4-001
R-6.15.4-002
R-6.15.4-003
R-6.15.4-004
R-6.15.4-005
R-6.15.4-006
R-6.15.4-007
R-6.15.4-008
R-6.15.4-009
R-6.15.4-010
R-6.15.4-011
6.15.5 MCX Service Ad hoc Group Communication
NA
6.15.5.1 Overview
NA
6.15.5.2 General Aspects
R-6.15.5.2-001
R-6.15.5.2-001a
R-6.15.5.2-001b
R-6.15.5.2-001c
R-6.15.5.2-002
R-6.15.5.2-003
R-6.15.5.2-004
R-6.15.5.2-005
R-6.15.5.2-006
R-6.15.5.2-007
R-6.15.5.2-008
R-6.15.5.2-009
R-6.15.5.2-010
R-6.15.5.2-011
R-6.15.5.2-012
R-6.15.5.2-013
R-6.15.5.2-014
R-6.15.5.2-014a
R-6.15.5.2-015
R-6.15.5.2-016
R-6.15.5.2-017
6.15.5.3 Administrative
R-6.15.5.3-001
R-6.15.5.3-002
R-6.15.5.3-003
R-6.15.5.3-004
R-6.15.5.3-005
6.15.5.4 Notification and acknowledgement for MCX Service Ad hoc Group Communications
R-6.15.5.4-001
6.15.6 MCX Service Ad hoc Group Emergency Alert
NA
6.15.6.1 Overview
NA
6.15.6.2 General aspects
R-6.15.6.2-001
R-6.15.6.2-002
R-6.15.6.2-002a
R-6.15.6.2-003
R-6.15.6.2-004
R-6.15.6.2-005
[[SUGGESTION_START]] R-6.15.6.2-005b[[SUGGESTION_END]]
R-6.15.6.2-00[[SUGGESTION_START]]6[[SUGGESTION_END]]
R-6.15.6.2-00[[SUGGESTION_START]]7[[SUGGESTION_END]]
R-6.15.6.2-00[[SUGGESTION_START]]8[[SUGGESTION_END]]
R-6.15.6.2-00[[SUGGESTION_START]]9[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.6.2-010[[SUGGESTION_END]]
6.15.6.3 Administrative
R-6.15.6.3-001
R-6.15.6.3-002
R-6.15.6.3-003
R-6.15.6.3-004
R-6.15.6.3-005
[[SUGGESTION_START]]R-6.15.6.3-006[[SUGGESTION_END]]
[[SUGGESTION_START]]6.15.6.4 Notification and acknowledgement for MCX Service Ad hoc Group Emergency Alert[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.6.4-001[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.15.6.4-002[[SUGGESTION_END]]
6.16 Interaction with telephony services
R-6.16-001
R-6.16-002
6.17 Interworking
NA
6.17.1 Non-3GPP access
R-6.17.1-001
6.17.2 Interworking between MCX Service systems
R-6.17.2-001
R-6.17.2-002
R-6.17.2-003
R-6.17.2-004
R-6.17.2-005
R-6.17.2-006
R-6.17.2-007
R-6.17.2-008
6.17.3 Interworking with non-MCX Service systems
NA
6.17.3.1 GSM-R
R-6.17.3.1-001
R-6.17.3.1-002
R-6.17.3.1-003
R-6.17.3.1-004
R-6.17.3.1-005
6.17.3.2 External systems
R.6.17.3.2-001
R.6.17.3.2-002
6.18 MCX Service coverage extension using ProSe UE-to-Network Relays
R-6.18-001
R-6.18-002
R-6.18-003
R-6.18-004
R-6.18-005
R-6.18-006
6.19 Additional MCX Service requirements
NA
6.19.1 Communication rejection and queuing
NA
6.19.1.1 Requirements
R-6.19.1.1-001
R-6.19.1.1-002
R-6.19.1.1-003
R-6.19.1.1-004
R-6.19.1.1-005
R-6.19.1.1-006
R-6.19.1.1-007
7 MCX Service requirements specific to off-network use
NA
7.1 Off-network communications overview
NA
7.2 General off-network MCX Service requirements
R-7.2-001
R-7.2-002
R-7.2-003
R-7.2-004
R-7.2-005
7.3 Admission control
NA
7.3.1 General aspects
R-7.3.1-001
R-7.3.1-002
R-7.3.1-003
7.3.2 Communication initiation
R-7.3.2-001
R-7.3.2-002
R-7.3.2-003
R-7.3.2-004
R-7.3.2-005
7.4 Communication termination
R-7.4-001
R-7.4-002
R-7.4-003
R-7.4-004
7.5 Broadcast Group
R-7.5-001
R-7.5-002
7.6 MCX Service priority requirements
R-7.6-001
R-7.6-002
R-7.6-003
R-7.6-004
R-7.6-005
R-7.6-006
R-7.6-007
R-7.6-008
R-7.6-009
7.7 Communication types based on priorities
NA
7.7.1 MCX Service Emergency Group Communication requirements
R-7.7.1-001
R-7.7.1-002
R-7.7.1-003
7.7.2 MCX Service Emergency Group Communication cancellation requirements
R-7.7.2-001
7.7.3 Imminent Peril Communication
NA
7.7.3.1 Imminent Peril Group Communication requirements
R-7.7.3.1-001
R-7.7.3.1-002
R-7.7.3.1-003
R-7.7.3.1-004
R-7.7.3.1-005
7.7.3.2 Imminent Peril Group Communication cancellation requirements
R-7.7.3.2-001
R-7.7.3.2-002
7.8 Location
R-7.8-001
R-7.8-002
R-7.8-003
7.9 Security
R-7.9-001
R-7.9-002
7.10 Off-network MCX Service operations
R-7.10-001
R-7.10-002
R-7.10-003
7.11 Off-network UE functionality
R-7.11-001
R-7.11-002
R-7.11-003
7.12 Streaming for ProSe UE-to-UE Relay and UE-to-Network Relay
NA
7.12.1 UE-to-Network Relay for all data types
R-7.12.1-001
R-7.12.1-002
R-7.12.1-003
R-7.12.1-004
7.12.2 UE-to-UE Relay streaming
R-7.12.2-001
R-7.12.2-002
R-7.12.2-003
7.12.3 Off-Network streaming
R-7.12.3-001
R-7.12.3-002
R-7.12.3-003
7.13 Switching to off-network MCX Service
R-7.13-001
R-7.13-002
R-7.13-003
R-7.13-004
R-7.13-005
7.14 Off-network recording and audit requirements
R-7.14-001
R-7.14-001a
R-7.14-002
R-7.14-002a
7.15 Off-network UE-to-UE relay
NA
7.15.1 Private Communications
R-7.15.1-001
R-7.15.1-002
R-7.15.1-003
7.15.2 Group Communications
R-7.15.2-001
R-7.15.2-002
7.16 Off-network Ad hoc Group Communication
R-7.16-001
8 Inter-MCX Service interworking
NA
8.1 Inter-MCX Service interworking overview
NA
8.2 Concurrent operation of different MCX Services
NA
8.2.1 Overview
NA
8.2.2 Requirements
R-8.2.2-001
R-8.2.2-002
R-8.2.2-003
R-8.2.2-004
R-8.2.2-005
R-8.2.2-006
R-8.2.2-007
8.3 Use of unsharable resources within a UE
R-8.3-001
R-8.3-002
R-8.3-003
R-8.3-004
R-8.3-005
R-8.3-006
8.4 Single group with multiple MCX Services
NA
8.4.1 Overview
NA
8.4.2 Requirements
R-8.4.2-001
R-8.4.2-002
R-8.4.2-003
R-8.4.2-004
R-8.4.2-005
8.4.3 Compatibility of UE
NA
8.4.3.1 Advertising service capabilities required
R-8.4.3.1-001
R-8.4.3.1-002
R-8.4.3.1-003
R-8.4.3.1-004
8.4.3.2 Conversion between capabilities
R-8.4.3.2-001
8.4.4 Individual permissions for service access
R-8.4.4-001
8.4.5 Common alias and user identities or mappable
R-8.4.5-001
8.4.6 Single location message
R-8.4.6-001
R-8.4.6-002
R-8.4.6-003
8.5 Priority between services
NA
8.5.1 Overview
NA
8.5.2 Requirements
R-8.5.2-001
R-8.5.2-002
R-8.5.2-003
R-8.5.2-004
R-8.5.2-005
9 Air Ground Air Communications
NA
9.1 Service description
NA
9.2 Requirements
R-9.2-001
10 MCX Service in IOPS mode
R-10-001
End of changes
|
S1-253381.zip
|
2026-01-13T17:34:07.339614
|
S1-253655
|
SA1
|
TSGS1_111_Goteborg
|
CR
|
noted
|
FRMCS_Ph6 – Normative [SP-250277]
|
3GPP TSG-SA WG1 Meeting #111 S1-253655
Gothenburg, Sweden, 25-29 August 2025
CR-Form-v12.3
CHANGE REQUEST
22.280
CR
0178
rev
1
Current version:
20.0.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
X
Radio Access Network
Core Network
X
Title:
Availability status of a MC User
Source to WG:
Nokia, UIC
Source to TSG:
S1
Work item code:
FRMCS_Ph6-REQ
Date:
2025-08-27
Category:
C
Release:
Rel-20
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
Rel-20 (Release 20)
Reason for change:
3GPP SA1 has studied several use cases related to availability status of a MC User in Rel-20 FS_ FRMCS_Ph6. The corresponding requirements have been captured in TR 22.989.
It is proposed to introduce the corresponding requirements into TS 22.280.
Summary of change:
Provide the availability status of MC Service ID and Functional Alias
Consequences if not approved:
Missing requirements for downstream groups related to availability status of a MC User
Clauses affected:
6.7.2, 6.9, Annex A
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
Start of changes
6.7.2 General requirements
[R-6.7.2-001] The MCX Service should [[SUGGESTION_START]]enable[[SUGGESTION_END]] for authorized MCX Users to [[SUGGESTION_START]]subscribe to a [[SUGGESTION_END]][[SUGGESTION_START]]notif[[SUGGESTION_END]][[SUGGESTION_START]]ication [[SUGGESTION_END]][[SUGGESTION_START]]service [[SUGGESTION_END]][[SUGGESTION_START]]to get information [[SUGGESTION_END]][[SUGGESTION_START]]about[[SUGGESTION_END]] the [[SUGGESTION_START]]presence [[SUGGESTION_END]]of a particular MCX User [[SUGGESTION_START]]per [[SUGGESTION_END]][[SUGGESTION_START]]service, MCPTT, MCData or MCVideo[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]] [[SUGGESTION_START]]by checking the availability status of[[SUGGESTION_END]] [[SUGGESTION_START]]his/her[[SUGGESTION_END]][[SUGGESTION_START]] MC[[SUGGESTION_END]][[SUGGESTION_START]] Service ID[[SUGGESTION_END]][[SUGGESTION_START]] or [[SUGGESTION_END]][[SUGGESTION_START]]activated [[SUGGESTION_END]][[SUGGESTION_START]]functional alias(es)[[SUGGESTION_END]].
[[SUGGESTION_START]]NOTE: [[SUGGESTION_END]][[SUGGESTION_START]]a) [[SUGGESTION_END]][[SUGGESTION_START]]The [[SUGGESTION_END]][[SUGGESTION_START]]availability [[SUGGESTION_END]][[SUGGESTION_START]]status is assigned to the MC Service ID [[SUGGESTION_END]][[SUGGESTION_START]]or [[SUGGESTION_END]][[SUGGESTION_START]]corresponding [[SUGGESTION_END]][[SUGGESTION_START]]functional ali[[SUGGESTION_END]][[SUGGESTION_START]]as[[SUGGESTION_END]] [[SUGGESTION_START]]is [[SUGGESTION_END]][[SUGGESTION_START]]based on the fact if the [[SUGGESTION_END]][[SUGGESTION_START]]MC Service ID is [[SUGGESTION_END]][[SUGGESTION_START]]used[[SUGGESTION_END]] [[SUGGESTION_START]]a[[SUGGESTION_END]][[SUGGESTION_START]]s origination or terminating MC Service ID in a call, or if the functional a[[SUGGESTION_END]][[SUGGESTION_START]]l[[SUGGESTION_END]][[SUGGESTION_START]]ias is used [[SUGGESTION_END]][[SUGGESTION_START]]as calling or [[SUGGESTION_END]][[SUGGESTION_START]]called [[SUGGESTION_END]][[SUGGESTION_START]]functional alias[[SUGGESTION_END]][[SUGGESTION_START]] in a call[[SUGGESTION_END]].
[[SUGGESTION_START]] b) [[SUGGESTION_END]][[SUGGESTION_START]]If an MCX user [[SUGGESTION_END]][[SUGGESTION_START]]is engaged in a call[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] in [[SUGGESTION_END]][[SUGGESTION_START]]at least [[SUGGESTION_END]][[SUGGESTION_START]]one [[SUGGESTION_END]][[SUGGESTION_START]]MCX UE, [[SUGGESTION_END]][[SUGGESTION_START]]the availability status is considered as ‘busy’[[SUGGESTION_END]][[SUGGESTION_START]], depending on the type of call, MCPTT, MCData or MC[[SUGGESTION_END]][[SUGGESTION_START]]Video.[[SUGGESTION_END]]
[R-6.7.2-002] The MCX Service should provide a mechanism for an MCX Service Administrator to configure which MCX Users, within their authority, are authorized to place a Private Communication (without Floor control).
[R-6.7.2-003] The MCX Service should provide a mechanism for authorized MCX Users to [[SUGGESTION_START]]decide [[SUGGESTION_END]]whether a particular MCX User is capable of participating in a Private Communication [[SUGGESTION_START]]by checking if the[[SUGGESTION_END]] [[SUGGESTION_START]]availability[[SUGGESTION_END]][[SUGGESTION_START]] status of [[SUGGESTION_END]][[SUGGESTION_START]]MC[[SUGGESTION_END]][[SUGGESTION_START]] Service ID[[SUGGESTION_END]][[SUGGESTION_START]] or functional alias(es)[[SUGGESTION_END]][[SUGGESTION_START]] is set to ‘available’[[SUGGESTION_END]].
[R-6.7.2-004] The MCX Service shall provide a mechanism by which an MCX User can make a Private Communication to the local dispatcher based on the MCX User's current Location.
[R-6.7.2-005] The MCX Service shall provide a mechanism for the Private Communication to be set up with the MCX UE designated by the receiving MCX User to be used for Private Communications when the receiving MCX User has signed on to the MCX Service with multiple MCX UEs.
[R-6.7.2-006] The MCX Service shall provide ability for the MCX Service Administrator to set up a Private Communications with a different MCX UE than the one designated by the receiving MCX User, who has signed on to the MCX Service with multiple MCX UEs.
Next change
6.9 IDs and aliases
[R-6.9-001] The MCX Service shall provide a mechanism for permanent and temporary assignment of IDs and aliases.
[R-6.9-002] The MCX Service shall provide a mechanism for the enforcement of uniqueness of IDs and aliases.
[R-6.9-003] The MCX Service shall provide a mechanism for an MCX Service Administrator to configure IDs and aliases.
[R-6.9-004] The MCX Service shall provide the MCX Service User ID and /or associated aliases, the identity of the Selected MCX Service Group, and, if available, the identity of the Mission Critical Organization name of the transmitting MCX User to all MCX UEs that are receiving for display by each MCX UE.
[[SUGGESTION_START]][R-6.9-005] [[SUGGESTION_END]][[SUGGESTION_START]]The MCX Service shall provide a mechanism [[SUGGESTION_END]][[SUGGESTION_START]]to assign a[[SUGGESTION_END]][[SUGGESTION_START]]n availability[[SUGGESTION_END]][[SUGGESTION_START]] status to the MC Service ID of an MCX User after successful log-in or [[SUGGESTION_END]][[SUGGESTION_START]]succes[[SUGGESTION_END]][[SUGGESTION_START]]sful activation of [[SUGGESTION_END]][[SUGGESTION_START]]Functional Alias[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]][R-6.9-006] The MCX Service shall enable the update of [[SUGGESTION_END]][[SUGGESTION_START]]availability [[SUGGESTION_END]][[SUGGESTION_START]]status of the MC Service ID or Functional Alias based on the [[SUGGESTION_END]][[SUGGESTION_START]]call [[SUGGESTION_END]][[SUGGESTION_START]]activity of the MCX user[[SUGGESTION_END]][[SUGGESTION_START]], [[SUGGESTION_END]][[SUGGESTION_START]]available[[SUGGESTION_END]][[SUGGESTION_START]], busy, offline[[SUGGESTION_END]] [[SUGGESTION_START]].[[SUGGESTION_END]]
Next change
Annex A (normative):
MCCoRe Requirements for MCPTT
Table A.1 provides an exhaustive list of those requirements in 3GPP TS 22.280 that are applicable to MCPTT.
Table A.1
5 MCX Service Requirements common for on the network and off the network
NA
5.1 General Group Communications Requirements
NA
5.1.1 General aspects
R-5.1.1-001
R-5.1.1-002
R-5.1.1-003
R-5.1.1-004
R-5.1.1-005
5.1.2 Group/status information
R-5.1.2-001
R-5.1.2-002
5.1.3 Group configuration
R-5.1.3-001
R5.1.3-002
5.1.4 Identification
R-5.1.4-001
5.1.5 Membership/affiliation
R-5.1.5-001
R-5.1.5-002
R-5.1.5-003
R-5.1.5-004
R-5.1.5-005
R-5.1.5-006
R-5.1.5-007
R-5.1.5-008
5.1.6 Group Communication administration
R-5.1.6-001
5.1.7 Prioritization
R-5.1.7-001
R-5.1.7-002
5.1.8 Charging requirements for MCX Service
R-5.1.8-001
R-5.1.8-003
R-5.1.8-004
R-5.1.8-005
R-5.1.8-006
R-5.1.8-007
R-5.1.8-008
R-5.1.8-009
R-5.1.8-010
R-5.1.8-011
5.1.9 MCX Service Emergency Alert triggered by location
NA
5.2 Broadcast Group
NA
5.2.1 General Broadcast Group Communication
R-5.2.1-001
R-5.2.1-002
5.2.2 Group-Broadcast Group (e.g., announcement group)
R-5.2.2-001
R-5.2.2-002
R-5.2.2-003
R-5.2.2-004
5.2.3 User-Broadcast Group (e.g., System Communication)
R-5.2.3-001
R-5.2.3-002
5.3 Late communication entry
R-5.3-001
R-5.3-002
R-5.3-003
R-5.3-004
R-5.3-005
5.4 Receiving from multiple MCX Service communications
5.4.1 Overview
NA
5.4.2 Requirements
R-5.4.2-001
R-5.4.2-002
R-5.4.2-003
R-5.4.2-004
R-5.4.2-004A
R-5.4.2-004B
R-5.4.2-005
R-5.4.2-006
R-5.4.2-007
R-5.4.2-007a
R-5.4.2-008
R-5.4.2-009
5.5 Private Communication
NA
5.5.1 Private Communication general requirements
NA
5.5.2 Charging requirement for MCX Service
R-5.5.2-001
5.6 MCX Service priority requirements
NA
5.6.1 Overview
NA
5.6.2 Communication types based on priorities
NA
5.6.2.1 MCX Service Emergency and Imminent Peril general requirements
NA
5.6.2.1.1 Overview
NA
5.6.2.1.2 Requirements
R-5.6.2.1.2-001
R-5.6.2.1.2-002
R-5.6.2.1.2-003
R-5.6.2.1.2-004
R-5.6.2.1.2-005
5.6.2.2 MCX Service Emergency Group Communication
NA
5.6.2.2.1 MCX Service Emergency Group Communication requirements
R-5.6.2.2.1-001
R-5.6.2.2.1-002
R-5.6.2.2.1-003
R-5.6.2.2.1-004
R-5.6.2.2.1-005
R-5.6.2.2.1-006
R-5.6.2.2.1-007
R-5.6.2.2.1-008
R-5.6.2.2.1-009
R-5.6.2.2.1-010
R-5.6.2.2.1-011
R-5.6.2.2.1-012
R-5.6.2.2.1-013
R-5.6.2.2.1-014
5.6.2.2.2 MCX Service Emergency Group Communication cancellation requirements
R-5.6.2.2.2-001
R-5.6.2.2.2-002
R-5.6.2.2.2-003
R-5.6.2.2.2-004
R-5.6.2.2.2-005
5.6.2.3 MCX Service Imminent Peril Group
NA
5.6.2.3.1 MCX Service Imminent Peril Group Communication requirements
R-5.6.2.3.1-001
R-5.6.2.3.1-002
R-5.6.2.3.1-003
R-5.6.2.3.1-004
R-5.6.2.3.1-005
R-5.6.2.3.1-006
R-5.6.2.3.1-007
R-5.6.2.3.1-008
R-5.6.2.3.1-009
5.6.2.3.2 MCX Service Imminent Peril Group Communications cancellation requirements
R-5.6.2.3.2-001
R-5.6.2.3.2-002
R-5.6.2.3.2-003
R-5.6.2.3.2-004
5.6.2.4 MCX Service Emergency Alert
NA
5.6.2.4.1 MCX Service Emergency Alert requirements
R-5.6.2.4.1-001
R-5.6.2.4.1-002
R-5.6.2.4.1-003
R-5.6.2.4.1-004
R-5.6.2.4.1-004a
R-5.6.2.4.1-005
R-5.6.2.4.1-006
R-5.6.2.4.1-007
R-5.6.2.4.1-008
R-5.6.2.4.1-009
R-5.6.2.4.1-010
R-5.6.2.4.1-011
R-5.6.2.4.1-012
R-5.6.2.4.1-013
5.6.2.4.2 MCX Service Emergency Alert cancellation requirements
R-5.6.2.4.2-001
R-5.6.2.4.2-002
R-5.6.2.4.2-003
5.7 MCX Service User ID
R-5.7-001
R-5.7-002
R-5.7-003
5.8 MCX UE Management
R-5.8-001
R-5.8-002
5.9 MCX Service User Profile
R-5.9-001
R-5.9-002
5.9A Functional alias
R-5.9a-001
R-5.9a-001a
R-5.9a-001b
R-5.9a-001c
R-5.9a-002
R-5.9a-002a
R-5.9a-003
R-5.9a-004
R-5.9a-005
R-5.9a-006
R-5.9a-007
R-5.9a-008
R-5.9a-009
R-5.9a-010
R-5.9a-011
R-5.9a-012
R-5.9a-013
R-5.9a-014
R-5.9a-015
R-5.9a-016
R-5.9a-017
R-5.9a-018
R-5.9a-019
R-5.9a-020
R-5.9a-021
R-5.9a-022
R-5.9a-023
[R-5.9a-024
R-5.9a-025
R-5.9a-026
R-5.9a-027
R-5.9a-028
R-5.9a-029
R-5.9a-030
R-5.9a-031
5.10 Support for multiple devices
R-5.10-001
R-5.10-001a
R-5.10-002
5.11 Location
R-5.11-001
R-5.11-002
R-5.11-002a
R-5.11-003
R-5.11-004
R-5.11-005
R-5.11-006
R-5.11-007
R-5.11-008
R-5.11-009
R-5.11-010
R-5.11-011
R-5.11-013
R-5.11-014
R-5.11-015
R-5.11-015
5.12 Security
R-5.12-001
R-5.12-002
R-5.12-003
R-5.12-004
R-5.12-005
R-5.12-006
R-5.12-007
R-5.12-008
R-5.12-009
R-5.12-010
R-5.12-011
R-5.12-012
R-5.12-013
R5-12-014
5.13 Media quality
R-5.13-001
5.14 Relay requirements
R-5.14-001
R-5.14-002
R-5.14-003
R-5.14-004
5.15 Gateway requirements
R-5.15-001
R-5.15-002
R-5.15-003
5.16 Control and management by Mission Critical Organizations
NA
5.16.1 Overview
NA
5.16.2 General requirements
R-5.16.2-001
R-5.16.2-002
R-5.16.2-003
R-5.16.2-004
R-5.16.2-005
5.16.3 Operational visibility for Mission Critical Organizations
R-5.16.3-001
5.17 General administrative – groups and users
R-5.17-001
R-5.17-002
R-5.17-003
R-5.17-004
R-5.17-005
R-5.17-006
R-5.17-007
R-5.17-008
5.18 Open interfaces for MCX services
NA
5.18.1 Overview
NA
5.18.2 Requirements
NA
5.19 Media forwarding
NA
5.19.1 Service description
NA
5.19.2 Requirements
NA
5.20 Receipt notification
NA
5.20.1 Service description
NA
5.20.2 Requirements
NA
5.21 Additional services for MCX Service communications
NA
5.21.1 Remotely initiated MCX Service communication
NA
5.21.1.1 Overview
NA
5.21.1.2 Requirements
NA
5.21.2 Remotely terminated MCX Service communication
NA
5.21.2.1 Requirements
R-5.21.2.1-001
6 MCX Service requirements specific to on-network use
NA
6.1 General administrative – groups and users
R-6.1-001
R-6.1-002
R-6.1-003
R-6.1-004
R-6.1-005
6.2 MCX Service communications
NA
6.2.1 Notification and acknowledgement for MCX Service Group Communications
NA
6.2.2 Queuing
R-6.2.2-001
R-6.2.2-002
R-6.2.2-003
R-6.2.2-004
R-6.2.2-005
R-6.2.2-006
6.3 General requirements
R-6.3-001
R-6.3-002
R-6.3-003
R-6.3-004
6.4 General group communication
NA
6.4.1 General aspects
R-6.4.1-001
6.4.2 Group status/information
R-6.4.2-005
R-6.4.2-001
R-6.4.2-002
R-6.4.2-003
R-6.4.2-004
R-6.4.2-006
R-6.4.2-007
6.4.3 Identification
R-6.4.3-001
R-6.4.3-002
6.4.4 Membership/affiliation
R-6.4.4-001
R-6.4.4-002
R-6.4.4-002a
R-6.4.4-003
R-6.4.4-004
6.4.5 Membership/affiliation list
R-6.4.5-001
R-6.4.5-002
R-6.4.5-003
R-6.4.5-003a
R-6.4.5-004
R-6.4.5-005
R-6.4.5-006
R-6.4.5-007
R-6.4.5-008
6.4.6 Authorized user remotely changes another MCX User’s affiliated and/or Selected MCX Service Group(s)
NA
6.4.6.1 Mandatory change
R-6.4.6.1-001
R-6.4.6.1-002
R-6.4.6.1-003
R-6.4.6.1-004
6.4.6.2 Negotiated change
R-6.4.6.2-001
R-6.4.6.2-002
R-6.4.6.2-003
R-6.4.6.2-004
R-6.4.6.2-005
R-6.4.6.2-006
6.4.7 Prioritization
R-6.4.7-001
R-6.4.7-002
R-6.4.7-003
R-6.4.7-004
6.4.8 Relay requirements
R-6.4.8-001
6.4.9 Administrative
R-6.4.9-001
R-6.4.9-002
R-6.4.9-003
R-6.4.9-004
R-6.4.9-005
R-6.4.9-006
6.5 Broadcast Group
NA
6.5.1 General Broadcast Group Communication
R-6.5.1-001
R-6.5.1-002
6.5.2 Group-Broadcast Group (e.g., announcement group)
R-6.5.2-001
6.5.3 User-Broadcast Group (e.g., system communication)
R-6.5.3-001
6.6 Dynamic group management (i.e., dynamic reporting)
NA
6.6.1 General dynamic regrouping
R-6.6.1-001
R-6.6.1-002
R-6.6.1-003
R-6.6.1-004
R-6.6.1-005
R-6.6.1-006
6.6.2 Group regrouping
NA
6.6.2.1 Service description
NA
6.6.2.2 Requirements
R-6.6.2.2-001
R-6.6.2.2-002
R-6.6.2.2-003
R-6.6.2.2-004
R-6.6.2.2-005
R-6.6.2.2-006
R-6.6.2.2-007
R-6.6.2.2-008
R-6.6.2.2-009
R-6.6.2.2-010
R-6.6.2.2-011
R-6.6.2.2-012
R-6.6.2.2-013
6.6.3 Temporary Broadcast Groups
R-6.6.3-001
R-6.6.3-001a
R-6.6.3-001b
R-6.6.3-002
6.6.4 User regrouping
NA
6.6.4.1 Service description
NA
6.6.4.2 Requirements
R-6.6.4.2-001
R-6.6.4.2-002
R-6.6.4.2-002a
R-6.6.4.2-002b
R-6.6.4.2-003
R-6.6.4.2-004
R-6.6.4.2-005
6.6.5 Dynamic Group Participation
NA
6.6.5.1 Service description
NA
6.6.5.2 Requirements
R-6.6.5.2-001
R-6.6.5.2-002
R-6.6.5.2-003
R-6.6.5.2-004
R-6.6.5.2-005
R-6.654.2-006
R-6.6.5.2-007
R-6.6.5.2-008
6.7 Private Communication
NA
6.7.1 Overview
NA
6.7.2 General requirements
R-6.7.2-001
R-6.7.2-002
R-6.7.2-003
R-6.7.2-004
R-6.7.2-005
R-6.7.2-006
6.7.3 Administrative
R-6.7.3-001
R-6.7.3-002
R-6.7.3-003
R-6.7.3-004
R-6.7.3-005
R-6.7.3-006
R-6.7.3-007
R-6.7.3-007a
R-6.7.3-008
6.7.4 Prioritization
R-6.7.4-001
R-6.7.4-002
R-6.7.4-003
R-6.7.4-004
R-6.7.4-005
R-6.7.4-006
R-6.7.4-007
6.7.5 Private Communication (without Floor control) commencement requirements
R-6.7.5-001
R-6.7.5-002
R-6.7.5-003
6.7.6 Private Communication (without Floor control) termination
R-6.7.6-001
R-6.7.6-002
6.8 MCX Service priority requirements
NA
6.8.1 General
R-6.8.1-001
R-6.8.1-002
R-6.8.1-003
R-6.8.1-004
R-6.8.1-005
R-6.8.1-006
R-6.8.1-007
R-6.8.1-008
R-6.8.1-009
R-6.8.1-010
R-6.8.1-011
R-6.8.1-012
R-6.8.1-013
R-6.8.1-014
R-6.8.1-015
R-6.8.1-016
6.8.2 3GPP system access controls
R-6.8.2-001
6.8.3 3GPP system admission controls
R-6.8.3-001
6.8.4 3GPP system scheduling controls
R-6.8.4-001
6.8.5 UE access controls
R-6.8.5-001
6.8.6 Mobility and load management
NA
6.8.6.1 Mission Critical mobility management according to priority
R-6.8.6.1-001
R-6.8.6.1-002
6.8.6.2 Load management
R-6.8.6.2-001
R-6.8.6.2-002
R-6.8.6.2-003
R-6.8.6.2-004
R-6.8.6.2-005
6.8.7 Application layer priorities
NA
6.8.7.1 Overview
NA
6.8.7.2 Requirements
R-6.8.7.2-001
R-6.8.7.2-002
R-6.8.7.2-003
R-6.8.7.2-004
R-6.8.7.2-005
R-6.8.7.2-006
R-6.8.7.2-007
R-6.8.7.2-008
R-6.8.7.2-009
R-6.8.7.2-010
6.8.8 Communication types based on priorities
NA
6.8.8.1 MCX Service Emergency Group Communication requirements
R-6.8.8.1-001
R-6.8.8.1-002
R-6.8.8.1-003
R-6.8.8.1-004
6.8.8.2 MCX Service Emergency Private Communication requirements
NA
6.8.8.3 Imminent Peril Group Communication requirements
R-6.8.8.3-001
R-6.8.8.3-002
R-6.8.8.3-003
6.8.8.4 MCX Service Emergency Alert
NA
6.8.8.4.1 Requirements
R-6.8.8.4.1-001
R-6.8.8.4.1-002
R-6.8.8.4.1-003
R-6.8.8.4.1-004
R-6.8.8.4.1-005
R-6.8.8.4.1-006
6.8.8.4.2 MCX Service Emergency Alert cancellation requirements
R-6.8.8.4.2-001
R-6.8.8.4.2-002
6.8.8.X Ad hoc Group Communication requirements
R-6.8.8.X-001
6.9 IDs and aliases
R-6.9-001
R-6.9-002
R-6.9-003
R-6.9-004
[[SUGGESTION_START]]R-6.9-005[[SUGGESTION_END]]
[[SUGGESTION_START]]R-6.9-006[[SUGGESTION_END]]
6.10 User Profile management
R-6.10-001
R-6.10-002
R-6.10-003
R-6.10-004
6.11 Support for multiple devices
R-6.11-001
R-6.11-002
R-6.11-003
6.12 Location
R-6.12-001
R-6.12-002
R-6.12-003
R-6.12-004
R-6.12-005
R-6.12-006
R-6.12-007
6.13 Security
NA
6.13.1 Overview
NA
6.13.2 Cryptographic protocols
R-6.13.2-001
R-6.13.2-002
R-6.13.2-003
6.13.3 Authentication
R-6.13.3-001
6.13.4 Access control
R-6.13.4-001
R-6.13.4-002
R-6.13.4-003
R-6.13.4-004
R-6.13.4-005
R-6.13.4-006
R-6.13.4-007
R-6.13.4-008
R-6.13.4-009
R-6.13.4-010
6.13.5 Regulatory issues
R-6.13.5-001
6.13.6 Storage control
NA
6.14 Interactions for MCX Service Group Communications and MCX Service Private Communications
R-6.14-001
R-6.14-002
6.15 Additional services for MCX Service communications
NA
6.15.1 Discreet listening capabilities
R-6.15.1-001a
R-6.15.1-001
R-6.15.1-002
R-6.15.1-002a
R-6.15.1-003
R-6.15.1-004
6.15.2 Ambient listening
NA
6.15.2.1 Overview of ambient listening
NA
6.15.2.2 Ambient listening requirements
NA
6.15.2.2.1 General ambient listening requirements
R-6.15.2.2.1-001
R-6.15.2.2.1-002
R-6.15.2.2.1-003
6.15.2.2.2 Remotely initiated ambient listening requirements
R-6.15.2.2.2-001
R-6.15.2.2.2-002
6.15.2.2.3 Locally initiated ambient listening requirements
R-6.15.2.2.3-001
R-6.15.2.2.3-002
6.15.3 Remotely initiated MCX Service Communication
NA
6.15.3.1 Overview
NA
6.15.3.2 Requirements
R-6.15.3.2-001
R-6.15.3.2-002
R-6.15.3.2-003
R-6.15.3.2-004
6.15.4 Recording and audit requirements
R-6.15.4-001
R-6.15.4-002
R-6.15.4-003
R-6.15.4-004
R-6.15.4-005
R-6.15.4-006
R-6.15.4-007
R-6.15.4-008
R-6.15.4-009
R-6.15.4-010
R-6.15.4-011
6.15.5 MCX Service Ad hoc Group Communication
NA
6.15.5.1 Overview
NA
6.15.5.2 General Aspects
R-6.15.5.2-001
R-6.15.5.2-001a
R-6.15.5.2-001b
R-6.15.5.2-001c
R-6.15.5.2-002
R-6.15.5.2-003
R-6.15.5.2-004
R-6.15.5.2-005
R-6.15.5.2-006
R-6.15.5.2-007
R-6.15.5.2-008
R-6.15.5.2-009
R-6.15.5.2-010
R-6.15.5.2-011
R-6.15.5.2-012
R-6.15.5.2-013
R-6.15.5.2-014
R-6.15.5.2-014a
R-6.15.5.2-015
R-6.15.5.2-016
R-6.15.5.2-017
6.15.5.3 Administrative
R-6.15.5.3-001
R-6.15.5.3-002
R-6.15.5.3-003
R-6.15.5.3-004
R-6.15.5.3-005
6.15.5.4 Notification and acknowledgement for MCX Service Ad hoc Group Communications
R-6.15.5.4-001
6.15.6 MCX Service Ad hoc Group Emergency Alert
NA
6.15.6.1 Overview
NA
6.15.6.2 General aspects
R-6.15.6.2-001
R-6.15.6.2-002
R-6.15.6.2-002a
R-6.15.6.2-003
R-6.15.6.2-004
R-6.15.6.2-005
R-6.15.6.2-005a
R-6.15.6.2-005b
R-6.15.6.2-006
R-6.15.6.2-007
R-6.15.6.2-008
6.15.6.3 Administrative
R-6.15.6.3-001
R-6.15.6.3-002
R-6.15.6.3-003
R-6.15.6.3-004
R-6.15.6.3-005
6.16 Interaction with telephony services
R-6.16-001
R-6.16-002
6.17 Interworking
NA
6.17.1 Non-3GPP access
R-6.17.1-001
6.17.2 Interworking between MCX Service systems
R-6.17.2-001
R-6.17.2-002
R-6.17.2-003
R-6.17.2-004
R-6.17.2-005
R-6.17.2-006
R-6.17.2-007
R-6.17.2-008
6.17.3 Interworking with non-MCX Service systems
NA
6.17.3.1 GSM-R
R-6.17.3.1-001
R-6.17.3.1-002
R-6.17.3.1-003
R-6.17.3.1-004
R-6.17.3.1-005
6.17.3.2 External systems
R.6.17.3.2-001
R.6.17.3.2-002
6.18 MCX Service coverage extension using ProSe UE-to-Network Relays
R-6.18-001
R-6.18-002
R-6.18-003
R-6.18-004
R-6.18-005
R-6.18-006
6.19 Additional MCX Service requirements
NA
6.19.1 Communication rejection and queuing
NA
6.19.1.1 Requirements
R-6.19.1.1-001
R-6.19.1.1-002
R-6.19.1.1-003
R-6.19.1.1-004
R-6.19.1.1-005
R-6.19.1.1-006
R-6.19.1.1-007
7 MCX Service requirements specific to off-network use
NA
7.1 Off-network communications overview
NA
7.2 General off-network MCX Service requirements
R-7.2-001
R-7.2-002
R-7.2-003
R-7.2-004
R-7.2-005
7.3 Admission control
NA
7.3.1 General aspects
R-7.3.1-001
R-7.3.1-002
R-7.3.1-003
7.3.2 Communication initiation
R-7.3.2-001
R-7.3.2-002
R-7.3.2-003
R-7.3.2-004
R-7.3.2-005
7.4 Communication termination
R-7.4-001
R-7.4-002
R-7.4-003
R-7.4-004
7.5 Broadcast Group
R-7.5-001
R-7.5-002
7.6 MCX Service priority requirements
R-7.6-001
R-7.6-002
R-7.6-003
R-7.6-004
R-7.6-005
R-7.6-006
R-7.6-007
R-7.6-008
R-7.6-009
7.7 Communication types based on priorities
NA
7.7.1 MCX Service Emergency Group Communication requirements
R-7.7.1-001
R-7.7.1-002
R-7.7.1-003
7.7.2 MCX Service Emergency Group Communication cancellation requirements
R-7.7.2-001
7.7.3 Imminent Peril Communication
NA
7.7.3.1 Imminent Peril Group Communication requirements
R-7.7.3.1-001
R-7.7.3.1-002
R-7.7.3.1-003
R-7.7.3.1-004
R-7.7.3.1-005
7.7.3.2 Imminent Peril Group Communication cancellation requirements
R-7.7.3.2-001
R-7.7.3.2-002
7.8 Location
R-7.8-001
R-7.8-002
R-7.8-003
7.9 Security
R-7.9-001
R-7.9-002
7.10 Off-network MCX Service operations
R-7.10-001
R-7.10-002
R-7.10-003
7.11 Off-network UE functionality
R-7.11-001
R-7.11-002
R-7.11-003
7.12 Streaming for ProSe UE-to-UE Relay and UE-to-Network Relay
NA
7.12.1 UE-to-Network Relay for all data types
R-7.12.1-001
R-7.12.1-002
R-7.12.1-003
R-7.12.1-004
7.12.2 UE-to-UE Relay streaming
R-7.12.2-001
R-7.12.2-002
R-7.12.2-003
7.12.3 Off-Network streaming
R-7.12.3-001
R-7.12.3-002
R-7.12.3-003
7.13 Switching to off-network MCX Service
R-7.13-001
R-7.13-002
R-7.13-003
R-7.13-004
R-7.13-005
7.14 Off-network recording and audit requirements
R-7.14-001
R-7.14-001a
R-7.14-002
R-7.14-002a
7.15 Off-network UE-to-UE relay
NA
7.15.1 Private Communications
R-7.15.1-001
R-7.15.1-002
R-7.15.1-003
7.15.2 Group Communications
R-7.15.2-001
R-7.15.2-002
7.16 Off-network Ad hoc Group Communication
R-7.16-001
8 Inter-MCX Service interworking
NA
8.1 Inter-MCX Service interworking overview
NA
8.2 Concurrent operation of different MCX Services
NA
8.2.1 Overview
NA
8.2.2 Requirements
R-8.2.2-001
R-8.2.2-002
R-8.2.2-003
R-8.2.2-004
R-8.2.2-005
R-8.2.2-006
R-8.2.2-007
8.3 Use of unsharable resources within a UE
R-8.3-001
R-8.3-002
R-8.3-003
R-8.3-004
R-8.3-005
R-8.3-006
8.4 Single group with multiple MCX Services
NA
8.4.1 Overview
NA
8.4.2 Requirements
R-8.4.2-001
R-8.4.2-002
R-8.4.2-003
R-8.4.2-004
R-8.4.2-005
8.4.3 Compatibility of UE
NA
8.4.3.1 Advertising service capabilities required
R-8.4.3.1-001
R-8.4.3.1-002
R-8.4.3.1-003
R-8.4.3.1-004
8.4.3.2 Conversion between capabilities
R-8.4.3.2-001
8.4.4 Individual permissions for service access
R-8.4.4-001
8.4.5 Common alias and user identities or mappable
R-8.4.5-001
8.4.6 Single location message
R-8.4.6-001
R-8.4.6-002
R-8.4.6-003
8.5 Priority between services
NA
8.5.1 Overview
NA
8.5.2 Requirements
R-8.5.2-001
R-8.5.2-002
R-8.5.2-003
R-8.5.2-004
R-8.5.2-005
9 Air Ground Air Communications
NA
9.1 Service description
NA
9.2 Requirements
R-9.2-001
10 MCX Service in IOPS mode
R-10-001
End of changes
|
S1-253655.zip
|
2026-01-13T17:34:39.023573
|
S1-250029
|
SA1
|
TSGS1_109_Athens
|
pCR
|
revised
|
6G General
|
3GPP TSG SA WG 1 Meeting #109 S1-240029
Athens, Greece, 17-21 February 2025 (revision of S1-24xxxx)
Source: 6G Rapporteurs
pCR Title: Definitions for the 6G Study
Draft Spec: 3GPP TR 22.870v0.1.1
Agenda item: 8.1
Document for: Approval
Contact: Xiaonan Shi (shixiaonan@chinamobile.com) and Jean Trakinat (jean.trakinat1@t-mobile.com)
Abstract: This pCR proposes terms for use in the 6G TR.
1. Introduction
Since SA1 is working on the 6G Stage 1 Study, then hopefully SA1 delegates agreed that 6G is going to happen, independent of “what” SA1 (and other groups) defined 6G to be. If 6G is like previous generations, then hopefully all SA1 delegates can agree that a “6G System” will also be defined.
In TR 22.905, a 3GPP system is defined as “a telecommunication system conforming to 3GPP specifications, consisting of one or more 3GPP core networks, one or more 3GPP access networks (providing GSM/EDGE, UTRA, E-UTRA, or NR radio access), and/or non-3GPP access networks (such as WLAN), and User Equipment.”
So, if 6G is like previous generations, then the 6G System will probably contain one or more core networks, one or more access networks (i.e., 3GPP, non-3GPP), and User Equipment.
Also, from TR 22.905, a 3GPP System core network refers in this specification to an evolved GSM core network infrastructure.
SA1 needs to find terminology to use while defining the functionality of the 6G System, without the distraction of what exactly the 6G System is (or will be). What that system looks like (i.e., architectures and components of the Core and RAN networks) will be defined by other 3GPP Working Groups. For example, TSG RAN may (or may not) define a new 6G radio access technology and SA2 will determine the network entities and/or network functions that comprise the 6GC.
The proposed definitions are based either on SA1 implicit usage or on the 3GPP system and core network definitions – with any implicit architecture constructs removed.
SA1 may want to consider generalizing a term that is more inclusive than “UE”, since the 5G System currently supports a variety of user equipment and devices (e.g., smart phones, IoT devices, Ambient IoT devices, wearables). Also, during SA1’s discussions on scoping the 6G study, many contributions identified and discussed “device diversity”.
2. Reason for Change
During the Orlando meeting, use case discussions became overly complicated by differing assumptions and disagreements on what comprises a 6G System and the 6G Core Network.
We hope that a common understanding of some terms being used in the TR would progress the work in a more efficient way.
Therefore, we propose some initial definitions for common terms (e.g., 6G System, 6G Core).
3. Proposal
To discuss the the proposed definitions and agree on terminology for inclusion in 3GPP TR 22.870 v0.1.1.
* * * First Change * * * *
3.1 Terms
For the purposes of the present document, the terms given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
example: text used to clarify abstract rules by applying them literally.
6G Core Network (6GC):
Choice “A”: interconnected network functions that evolved from the GSM core network infrastructure,
Choice “B”: a Core Network that connects to a 6G (3GPP or non-3GPP) access network,
Choice “C”: a 3GPP System core network of the 6G system.
6G System (6GS):
Choice “A”: includes core and radio access networks and UE(s).
Choice “B”: a 3GPP system consisting of 6G Access Network (AN), 6G Core Network and UE(s).
Choice “C”: a 3GPP system that is the next generation from the 5G System that supports a diversity of devices (e.g., UE, IoT devices, wearables).
* * * End of Change * * * *
|
S1-250029.zip
|
2026-01-13T17:36:25.192402
|
S1-250050
|
SA1
|
TSGS1_109_Athens
|
pCR
|
revised
|
6G General
|
3GPP TSG-SA WG1 Meeting #109 S1-250050
Athens, Greece, 17-21 February 2025
Source: OTD_US
pCR Title: Editorial Changes to 6G TR
Draft Spec: 3GPP TR 22.870v0.1.1
Agenda item: 8.1.1
Document for: Approval
Contact: selvam@trideaworks.com
Abstract: This contribution provides a detailed set of editorial to the 6G TR to help improve clarity and readability.
---------- First Change ----------
5.2.6 Potential New Requirements needed to support the use case
[PR 5.2.6-1] The 6G system shall provide security protection for communication against potential [[SUGGESTION_START]]CRQC-based [[SUGGESTION_END]]attacks.
---------- Second Change ----------
7.1.1 Description
In large disaster areas, a high degree of coordination for the search and rescue operations is essential. In such situations, sensing can play an important role in providing helpful information to the Public Protection and Disaster Relief (PPDR) authorities and first responders by providing an integrated platform for real-time monitoring and coordination which will support the efficient allocation of resources and the facilitation of decision-making in challenging environments.
Collecting data from a disaster area(s) involves the use of special equipment and devices supporting sensing to capture real-time information about the affected area. Also, the base stations in the disaster areas, or temporarily deployable and tactical base stations, can be used for providing the required sensing services.
The collected environmental and devices sensing data can be used to generate real-time maps for the affected areas allowing [[SUGGESTION_START]]the PPDR authorities [[SUGGESTION_END]]to prioritize and to direct the rescue efforts more efficiently. Furthermore, these maps can be used to monitor the evacuation processes, as well as the situations at the evacuation centres (e.g. to detect overcrowding at an evacuation centre).
---------- Third Change ----------
7.1.2 Pre-conditions
A) After the earthquake struck, a wide disaster area emerged.
Base stations can provide sensing services even after a disaster, or new base stations can be installed to ensure [[SUGGESTION_START]]the [[SUGGESTION_END]][[SUGGESTION_START]]provision of [[SUGGESTION_END]]the required capacity and/or coverage over areas where the Terrestrial Networks (TN) is down.
Rescue teams [[SUGGESTION_START]]are [[SUGGESTION_END]]equipped [[SUGGESTION_START]]with [[SUGGESTION_END]]and [[SUGGESTION_START]]use [[SUGGESTION_END]]special equipment and devices supporting sensing.
B) People are expected to gather at evacuation centres (rescue points).
The data on people flow is needed at evacuation centres to distribute disaster relief supplies.
---------- Fourth Change ----------
7.1.3 Service Flows
A) Coordination of search and rescue:
- Rescue teams start searching using sensing devices.
- Sensing technology is used to analyse the environment, structure, etc. of the disaster areas and generate real-time maps.
- The generated maps will reflect severely affected areas, searched areas, and unsearched areas.
- The rescue command centre can use the real-time map information to optimize the dispatch of rescue teams.
B) People evacuatin[[SUGGESTION_START]]g[[SUGGESTION_END]] and gathering at designated evacuation centres:
- By using sensing technologies, data about people flow can be monitored and provided to rescue teams.
- The rescue teams will provide the necessary relief supplies based on the level of crowding at the evacuation centres and distribute them accordingly.
- In the event of overcrowding at an evacuation centre, additional facilities will be installed (e.g. portable toilets, drinking water points) or a secondary evacuation centre will be set up to [[SUGGESTION_START]]which [[SUGGESTION_END]]people [[SUGGESTION_START]]will be guided[[SUGGESTION_END]].
---------- Fifth Change ----------
7.1.6 Potential New Requirements needed to support the use case
[PR 7.1.6-1] Based on operator policy, regional and/or national regulations, the 6G network in the Earthquake and Tsunami Warning System (ETWS) Notification Area shall provide secure mechanisms of collecting the sensing results with a specified level of accuracy that can be used to generate real-time maps[[SUGGESTION_START]].[[SUGGESTION_END]]
Editor's Note: Definition of KPIs for the above requirements is FFS.
---------- Sixth Change ----------
7.2.1 Description
In a presentation by 5GAA at the 6G Use Case Workshop [7], a positioning accuracy of 1 meter was identified as necessary for Vulnerable Road Users (VRUs). Additionally, their technical report [8] noted that VRUs might carry various devices to enhance pedestrian safety. Global Navigation Satellite System (GNSS)-based positioning alone is insufficient due to issues such as slow convergence, multipath in urban jungles, and susceptibility to jamming or spoofing. Therefore, rapid sensing through communication with roadside base stations and other infrastructure is essential. For instance, if a pedestrian begins moving toward a crosswalk [[SUGGESTION_START]]wh[[SUGGESTION_END]][[SUGGESTION_START]]ile a crossing signal [[SUGGESTION_END]][[SUGGESTION_START]]shows[[SUGGESTION_END]] red light, it is crucial to quickly detect this movement and send a warning to the UE they carry. This situation may occur with typical pedestrians[[SUGGESTION_START]] including [[SUGGESTION_END]]those looking at their smartphones while waiting for the light often mistakenly interpret movements around them as an indication that the signal has changed and start walking. Furthermore, assessing whether a pedestrian can fully cross before the light turns red requires an instantaneous, quantitative understanding of their walking speed. With this data, it becomes possible to determine, based on the road width and remaining green-light time, whether the pedestrian can cross safely.
While speed detection can be achieved through side imaging, using radio waves with high rectilinearity, such as mm-wave, to detect variations of the propagation time or to measure the Doppler shift creates a robust system that does not demand extensive computing power. Although pedestrians are the primary VRU group, the aging population has led to increasingly varied walking speeds, making it impractical to assume a single, typical walking speed. Real-time measurement [[SUGGESTION_START]]for each[[SUGGESTION_END]][[SUGGESTION_START]] pedestrian [[SUGGESTION_END]]in each instance is essential. [[SUGGESTION_START]]In [[SUGGESTION_END]]the case of wheelchair users[[SUGGESTION_START]] operating a vehicle,[[SUGGESTION_END]] [[SUGGESTION_START]]vehicle [[SUGGESTION_END]]braking can be applied more quickly and reliably without human intervention. However, applying the optimal braking force to prevent forward pitching requires knowledge of the speed before braking, making it particularly important to accurately capture low-speed movements.
---------- Seventh Change ----------
7.2.3 Service Flows
1. Bill, who lives in City B, is now 75 years old. Although he remains active, he can no longer conceal the decline in his mobility. At his family's suggestion, he has signed up for the safety assistance service option with Mobile Operator A.
2. One summer day, after enjoying a conversation with an old friend at his favourite bar, Bill started walking home. However, his thoughts were so absorbed in replaying the conversation that he attempted to cross the street at a red light without realizing it.
3. At that moment, a warning sound emitted from his mobile phone, stopping him in his tracks. A young person standing next to him, who happened to be looking at their smartphone while waiting for the light, also noticed the warning and helped him stop.
4. Just as Bill was thanking the young person, the light changed to green without him realizing. The young person quickly crossed the street, and Bill started crossing as well, but his phone emitted another warning sound.
5. This second alert made Bill realize that, at his current walking speed, he would not be able to make it across during the current green light. He [[SUGGESTION_START]]turned around and walked all the way back to where he started after which he [[SUGGESTION_END]]decided to wait until the light turned green again. As soon as it did, he [[SUGGESTION_START]]on[[SUGGESTION_END]][[SUGGESTION_START]]ce again [[SUGGESTION_END]]started crossing, and this time there was no warning sound, allowing him to cross safely.
---------- Eighth Change ----------
7.4.1 Description
With the development of Uncrewed Aerial Vehicles (UAV) technologies, light and small civilian UAVs have played a great role in aerial photography, agriculture, mapping and other fields. Various commercial UAV applications are now becoming a reality. These UAVs typically operate at low altitudes and may produce a series of safety control problems, e.g. UAV illegal intrusion and UAV collision. Thus, how to realize the low-altitude UAV supervision is important and challenging in 6G.
In some scenario[[SUGGESTION_START]]s[[SUGGESTION_END]], these UAVs may follow carefully planned routes that ensure efficient, regulated, and safe operation in designated airspace. To perform tasks like package delivery, surveillance, or environmental monitoring, commercial UAVs operate based on pre-determined flight paths that dictate their altitude, speed, and direction. For instance, a UAV delivering goods will follow a direct route from the dispatch location to the recipient, while a UAV assigned to environmental monitoring will travel from its station to a specific target area for data collection. Route design and optimization are crucial for safe and efficient UAV operations. These flight routes which are approved by UAV operators, prioritize the shortest flight path, avoid restricted airspace, and ensure safe distances from obstacles such as buildings, trees, or other UAVs. Following a strict route minimizes the risk of accidents and enhances the reliability of UAV services.
Although commercial UAVs are equipped with sensors to assist with real-time navigation, these sensors can be affected by environmental conditions like lighting, weather, or geographical obstructions. Such limitations can impair a UAV's ability to accurately determine its position, altitude, or velocity, which may lead to deviations from the approved flight path. Furthermore, for services such as good delivery, where a number of UAVs are involved in a given area, UAV collision might happen due to sensor limitations and lead to safety issues.
The existing UAV tracking technologies, such as ground-based radar systems and dedicated surveillance equipment, provide route monitoring. However, the widespread deployment of these systems faces challenges due to high installation and maintenance costs and limited availability of suitable installation sites.
As illustrated in Figure 7.4.1-1 [9], UEs connected to 6G Radio Access Network (RAN) entities can be configured to support sensing operations. This configuration enhances sensing coverage, provides additional positioning reference points for sensing measurements, and improves the accuracy and reliability of sensing results. These improvements are due to the higher density of UEs compared to base stations, which increases the likelihood that some UEs are positioned closer to the UAV than the 6G RAN entities (for example, with a UAV located between two 6G RAN entities and a UE located directly beneath the UAV). Additionally, certain UEs may be placed in reflection directions that provide a larger radar cross section for the UAV, taking into account the UAV's [[SUGGESTION_START]]radar cross section [[SUGGESTION_END]]variations in different incident/reflection angles.
The 6G sensing processing unit can gather sensing data from one or multiple network infrastructures. Upon request, the 6G network operator can provide UAV flight trajectory tracking services to trusted third-party applications, such as UAV service operators, regulatory agencies, Uncrewed Aerial System Traffic Management (UTM) systems, UAV itself, etc.
Figure 7.4.1-1: Low-altitude UAV trajectory tracing by 6G system
Thus, [[SUGGESTION_START]]realizing [[SUGGESTION_END]]the low-altitude UAV supervision (e.g. UAV intrusion detection, UAV trajectory tracking) is important and challenging in 6G.
Sensing is an efficient technology for object detection by means of 6G radio signals, e.g. monitoring UAV illegal[[SUGGESTION_START]]ly[[SUGGESTION_END]] flying in a specific area. 6G network could provide sensing service by collecting sensing data, transmitting sensing data, processing sensing data, storing sensing data and support sensing result exposure to the third application platform. These sensing data [[SUGGESTION_START]]have [[SUGGESTION_END]]the following characteristics:
- These data may not necessarily belong to a specific UE while these data may be produced by the relationship between the network and the physical environment.
- These data may be produced by a UE or a base station in order to complete a specific task which needs multi-dimensional cooperation.
- These data may have [[SUGGESTION_START]]a [[SUGGESTION_END]]relationship with time and space which needs efficient on-demand transmission, storage[[SUGGESTION_START]],[[SUGGESTION_END]] and collection.
- These data may be collected from non-3GPP sensing sources which needs unified management in [[SUGGESTION_START]]the [[SUGGESTION_END]]6G network.
[[SUGGESTION_START]]To address [[SUGGESTION_END]]these new data characteristics, the 6G network capability of data processing needs to be extended based on the 5G network including multi-source heterogeneous data collection, efficient and guaranteed large-scale data transmission, efficient data processing within the network, and unified data storage to support multi-node cooperative sensing and multi-node information convergence.
In the low-altitude UAV supervision scenario, the 6G network could be used for sensing the UAV intrusion such as a UAV illegally flying in a restricted area including government and company regions. In this scenario, the network security and data security need to be guaranteed due to the privacy issue. Furthermore, the historical data may be used to identify an illegal UAV. In addition to the communication property of the detected UAV itself, the data management in this low-altitude sensing scenario must be [[SUGGESTION_START]]handled [[SUGGESTION_END]]within the network.
In a word, [[SUGGESTION_START]]the [[SUGGESTION_END]]6G network will break through the "pipeline" capability and go towards to a new type of information service network by realizing diversified data collection, transmission, processing, storage and exposure [[SUGGESTION_START]]by [[SUGGESTION_END]]the core network.
---------- Ninth Change ----------
7.4.2 Pre-conditions
In this use case, a UAV Operator/UTM provides package delivery services within an area covered by a 6G network.
Network operator NN provides 6G sensing service for UAV flight assistance service, including illegal UAV intrusion[[SUGGESTION_START]] detection[[SUGGESTION_END]], UAV flight trajectory tracing, UAV collision prediction[[SUGGESTION_START]],[[SUGGESTION_END]] etc. NN can make use of wireless base station[[SUGGESTION_START]]s[[SUGGESTION_END]] to sense the airspace within their coverage area and report the sensing information (including tracked UAV and the environment around the UAV) to the USS (Uncrewed Aerial System Service Supplier)/UTM.
The Company MM uses the USS/UMT to supervise the low-altitude UAVs and manage potential illegal intrusion into the restricted areas. MM has proved its restricted area information to the USS/UMT.
The USS/UMT uses 6G sensing service provided by the 6G network operator NN to detect potential UAV illegal intrusion and UAV collision prediction.
The UAV Operator/UTM provides specific details to the 6G network operator NN, including the characteristics of the UAV [[SUGGESTION_START]]to [[SUGGESTION_END]]be tracked, along with details about the time and location for flight tracing. This information includes regulated flight paths as well as potential areas where the UAV might temporarily deviate from its route.
The 6G network operator NN can realize 6G data collection, 6G data transmission, 6G data processing, 6G data storage within the network, and [[SUGGESTION_START]]provide [[SUGGESTION_END]]sensing results to the USS/UMT/UAV.
---------- Tenth Change ----------
7.4.3 Service Flows
For illegal UAV intrusion:
1. The Company MM requests 6G sensing service for [[SUGGESTION_START]]detection of [[SUGGESTION_END]]illegal UAV intrusion in[[SUGGESTION_START]]to[[SUGGESTION_END]] the restricted area from the USS/UMT.
2. The USS/UMT transmits the request to the 6G Network operator NN.
3. The 6G Network operator NN selects the base stations located in the restricted area to collect and process initial sensing data by collaborative sensing. The selected 6G base station[[SUGGESTION_START]]s[[SUGGESTION_END]] constantly [[SUGGESTION_START]]collect [[SUGGESTION_END]]sensing data of the location of UAVs near the restricted area and sends the sensing data to the 6G core network with a defined frequency to obtain the sensing result (i.e., the distance between the UAV and the border or motion trail).
4. The 6G Network aggregates and transmits the data generated by the base stations and processes the data to obtain the sensing results.
5. The 6G Network exposes the sensing result to the USS/UMIT. The USS/UMIT could trigger [[SUGGESTION_START]]the 6G Network [[SUGGESTION_END]]to send warning messages to the UAV or intercept the illegal UAV directly based on the sensing results.
For UAV flight trajectory tracing:
1. When the scheduled time for tracking begins, the 6G network operator activates the UAV trajectory tracing service within the designated area until the tracking session ends. The UAV operator then launches UAV#1, which takes off from the delivery source and heads toward the destination, following a pre-set flight path.
2. Using radio sensing, a network of 6G base stations and connected devices (UEs) detect UAV#1 and continuously gather data on its position and movement, such as distance, velocity and angle. These metrics, also known as 3GPP sensing data, are sent to a 6G processing unit for real-time analysis.
3. During the flight, if UAV#1 leaves the coverage range of one base station and enters a new coverage zone, the 6G system could let the old base station stop radio sensing, and switch to [[SUGGESTION_START]]a [[SUGGESTION_END]]new base station for sensing UAV#1 until it is out of coverage. This transition is based on the UAV's estimated position and velocity, which the 6G processing unit calculates. The network can automatically adjust the sensing operations at base stations depending on this data or based on a pre-defined time frame. In certain cases, sensing handover may be triggered to maintain continuous coverage. For instance, if the current base station's connection weakens or if another nearby base station can offer better coverage for UAV#1, the system proactively shifts the sensing function to this new station to ensure uninterrupted tracking.
4. The 6G processing unit can aggregate sensing data from multiple sources, including RANs and UEs, to estimate UAV#1's location and velocity. Similar approach can also be applied to UAV#2 - UAV#N, in the case there are multiple UAVs providing service in the area. This real-time information is then transmitted to the UAV operator and/or UTM, who monitors the UAV's trajectory.
5. If UAV#1 - UAV#N deviate from prescribed routes, the UAV operator and/or UTM receives alerts, allowing them to take corrective action and redirect the UAVs as necessary.
---------- Eleventh Change ----------
7.4.4 Post-conditions
For illegal UAV intrusion: The illegal UAVs are [[SUGGESTION_START]]moved [[SUGGESTION_END]]away from the restricted area. Potential privacy risks are avoided. Thanks to the wide-area and constant sensing capability of the 6G base station[[SUGGESTION_START]]s[[SUGGESTION_END]], and the efficient data transmission, processing and storage by the 6G core network[[SUGGESTION_START]] as well as exposure of the sensing data[[SUGGESTION_END]], the safety supervision of the low-altitude space of Company 'MM' is improved.
For UAV flight trajectory tracing: UAV#1 follows the tracked flight route to deliver the package to its destination [[SUGGESTION_START]]and [[SUGGESTION_END]]any off-route movements are detected.
For UAV collision prediction: UAV#1 flies efficiently to its destination[[SUGGESTION_START]] and any potential collisions are detected and reported[[SUGGESTION_END]].
---------- Twelfth Change ----------
7.5.1 Description
Environmental object reconstruction offers significant potential with great societal and business impact, representing opportunities across wide range of sectors, from smart factories, homes[[SUGGESTION_START]],[[SUGGESTION_END]] and transportation to components of/entire smart cities and countries. The 3GPP ISAC provides a non-invasive dual-functionality of communication and sensing data collection of environmental features for reconstruction. This sensing data collection is expected to unlock new services and support enhanced performance, deployment utilization, energy, and spectral efficiencies due to the nature of ISAC and its wide-area coverage and radio-signal availability. For static environmental objects, such as outdoor building/city construction and indoor machinery, 3GPP wireless sensing is effective to provide a wealth of low-cost environmental information for both wide-areas and [[SUGGESTION_START]]more limited[[SUGGESTION_END]] areas. Additionally, for monitoring dynamic targets of interest, such as vehicles, this is essential for enhancing environmental perception, especially in scenarios where non-line-of-sight (NLOS) conditions or poor visibility may limit traditional sensing methods.
The use case of environmental object reconstruction in 3GPP demands finer characterization of surrounding targeted objects based on wireless sensing signal to facilitate industrial innovation. Some applications include:
- Smart Transportation: The impact of autonomous driving is expected to be significant in terms of safety and comfort, and potentially high efficiencies with respect to traffic, logistics, energy, etc. Such application[[SUGGESTION_START]]s[[SUGGESTION_END]] requireadvanced knowledge of moving target detection and its trajectory with detection-to-track association [12], within a multi-object tracking context, including perception of micro-features of the surrounding objects such as micro-Doppler effects, precise classification, and accurate dimensions/orientations, etc. Rough localization of these objects is insufficient for ensuring transport safety and public confidence. Therefore, environmental object reconstruction by the 3GPP wireless sensing [[SUGGESTION_START]]system [[SUGGESTION_END]]is [[SUGGESTION_START]]needed [[SUGGESTION_END]]to provide assistance for interpreting the complex traffic scenario[[SUGGESTION_START]]s[[SUGGESTION_END]] and making rapid decisions.
- Smart City: a smart city can demand survey/digital twinning for a component/system of an entire city, which has motivated a number of smart city initiatives [13] to capture [[SUGGESTION_START]]the [[SUGGESTION_END]]dynamic nature of society. Initially based on cameras, these digital twin models, can be greatly enhanced by environmental object reconstruction enabled by 3GPP wireless sensing[[SUGGESTION_START]].[[SUGGESTION_END]]
- Smart Home: there are increasing interests for innovative applications of smart home, e.g. for fall detection, provided with better sensing[[SUGGESTION_START]],[[SUGGESTION_END]] privacy protection[[SUGGESTION_START]],[[SUGGESTION_END]] and reliability. Environmental object reconstruction at home is expected for finer sensing information of home objects in order to improve service stability for better consumer experience.
Typical environmental objects to be reconstructed can include the following:
Table 7.5.1-1: Representative dimensions of an environment[[SUGGESTION_START]]al[[SUGGESTION_END]] object
Object Type
Dimensions
Building
Approximately ~400 m x ~200 m x ~20 m (L x W x H), static
Vehicle
Truck: 13 m x 2.6 m x 3 m (LxWxH), up to 140 km/h
The 3GPP ISAC is expected to offer significantly higher precision and resolution in sensing, and more detailed characteristics of an environment[[SUGGESTION_START]]al[[SUGGESTION_END]] object in the spatial domain.
---------- Thirteenth Change ----------
7.5.2 Pre-conditions
Map Provider A is a third-party service provider which can render and virtualize detected/tracked surrounding environmental objects within its application, offer real-time 3D virtualization of objects (e.g. via 3D glasses), and display alerts for object-related warnings and information. Examples of alerts include "a car is approaching from the left street corner in 3 seconds".
Good partnership and cooperation are established between Map Provider A and Mobile Operator B in City C. Requested by Map Provider A for sensing service, suitable sensing transmitter and/or sensing receiver deployed in City C are selected by Mobile Operator B to constantly sense environment[[SUGGESTION_START]]al[[SUGGESTION_END]] objects of City C including building[[SUGGESTION_START]]s[[SUGGESTION_END]] and vehicle[[SUGGESTION_START]]s[[SUGGESTION_END]]. The sensing signal emitted from [[SUGGESTION_START]]a [[SUGGESTION_END]]sensing transmitter arrives at an environment[[SUGGESTION_START]]al[[SUGGESTION_END]] object whose micro-objects will reflect/diffract the signal to be detected by selected sensing receivers.
NOTE: For the ease of elaboration, base station or UE is acting as sensing transmitter and/or sensing receiver. Other sensing modes are not excluded and can be useful for environmental object reconstruction.
Charlie is a subscriber of Mobile Operator B and also a subscriber of Map Provider A for real-time 3D virtualisation of his surrounding objects. Charlie would like to navigate unfamiliar city streets while wearing 3D glasses, which display surrounding objects in real-time, for his interest and safety.
---------- Fourteenth Change ----------
7.5.3 Service Flows
Figure 7.5.3-1
1. Charlie is a tourist, who is driving a car to enjoy the view around City C. He would like to navigate unfamiliar city streets while wearing 3D glasses, which display surrounding objects in real-time, for [[SUGGESTION_START]]his [[SUGGESTION_END]]interest and safety. Map Provider A initiates a sensing request to Mobile Operator B for the latest information of environment reconstruction in the vicinity of Charlie, who is the subscriber of both Map Provider A and Mobile Operator B. The sensing objects, required by Map Provider A, include vehicles, city architecture, etc., around Charlie up to a certain range.
2. Mobile Operator B selects and configures the sensing transmitter[[SUGGESTION_START]]s[[SUGGESTION_END]] and sensing receiver[[SUGGESTION_START]]s[[SUGGESTION_END]], and any applicable non-3GPP sensors, to facilitate the sensing operations. Both 3GPP and non-3GPP sensing data will be collected, aggregated, and processed by Mobile Operator B's network in accordance with environment reconstruction requirements.
3. Based on environment reconstruction requirements, Mobile Operator B is expected to preform further sensing operations dedicated to each individual object, to provide fine characteristic[[SUGGESTION_START]]s[[SUGGESTION_END]], in order to determine object type/dimension, monitor micro-motions like car turning, etc. Corresponding sensing operations may require cooperative and dedicated sensing resource allocation across [[SUGGESTION_START]]the [[SUGGESTION_END]]3GPP system for Charlie. Thereafter sensing results of each object are delivered to Map Provider A.
4. The sensing results generated by Mobile Operator B, with more detailed characteristics of environment[[SUGGESTION_START]]al[[SUGGESTION_END]] objects, are exposed to Map Provider A. This data enables Map Provider A to update Charlie's localized map, reflecting changes such as ongoing construction of [[SUGGESTION_START]]the [[SUGGESTION_END]]university campus, shapes/dimensions of targets, and real-time tracking of movement direction/speed etc.
5. Charlie receives a real-time hazard [[SUGGESTION_START]]display [[SUGGESTION_END]](e.g. a car is approaching from the left street corner in 3 seconds) from Map Provider A virtualized in his glass[[SUGGESTION_START]]es[[SUGGESTION_END]], tailored to Charlie's trajectory. This information is transmitted through Mobile Operator B's network, adhering to pre-defined latency requirements.
6. Luckily Charlie has a chance to visit City C again. Mobile Operator B keeps detecting and tracking ongoing renovation by sensing for local university campus [[SUGGESTION_START]]changes [[SUGGESTION_END]]including new classroom buildings. During his visit, such renovation is [[SUGGESTION_START]]nearly [[SUGGESTION_END]]completed. By partnering with Mobile Operator B, Map Provider A can integrate and reconstruct the buildings into the real-time 3D map. Charlie decides to pay a visit because of his interest [[SUGGESTION_START]]in [[SUGGESTION_END]]unique building design.
---------- Fifteenth Change ----------
7.5.5 Existing features partly or fully covering the use case functionality
3GPP TR 22.837 [9] has described use cases to monitor micro doppler effect by ISAC caused by chest rise/fall during sleeping. The sensing results represent [[SUGGESTION_START]]the [[SUGGESTION_END]]human respiration rate.
In this use case, 3GPP ISAC is expected to detect and track more comprehensive characteristics of individual environmental object, e.g. for a building, vehicle, robot, etc., with sufficient and accurate sensing information per object type.
---------- Sixteenth Change ----------
7.6.1 Description
Many transportation and urban applications require real-time and citywide traffic flow estimation, which is the basis for transportation planning and traffic control. Estimated traffic flow is generally represented by the number of cyclists/vehicles/pedestrians passing a reference location per unit of time and can be virtualized by a third-party application.
In order to enable real-time navigation for [[SUGGESTION_START]]automated [[SUGGESTION_END]]driving or traffic flow monitoring, it is necessary to define the upper limit of sensing object detection/tracking required by a 3GPP sensing service, since any 3GPP based sensing detection/tracking coexists with communication services and is not cost-free for operators [[SUGGESTION_START]]to simply share[[SUGGESTION_END]]. 3GPP system shall allocate limited but sufficient spectrum resources, power, base stations and/or roadside units[[SUGGESTION_START]],[[SUGGESTION_END]] etc[[SUGGESTION_START]].[[SUGGESTION_END]] for sensing operations. [[SUGGESTION_START]]This upper limit[[SUGGESTION_END]] can be represented by [[SUGGESTION_START]]a [[SUGGESTION_END]]sensing target density[[SUGGESTION_START]].[[SUGGESTION_END]]
For example, the number of vehicles that needs to be simultaneously detected and tracked at a crossroad may be up to 1000 cars per [km2], including all stationary and moving vehicles occupying that crossroad temporally:
NOTE: It is assumed that the number of lanes for a major crossroad could be 8 for each direction with 3.5 meter lane width, and that the number of vehicles per lane for the purpose of tracking is 5.
---------- Seventeenth Change ----------
7.6.2 Pre-conditions
Good partnership and cooperation are established between Traffic Department A and Mobile Operator B. Traffic Department A subscribes [[SUGGESTION_START]]to [[SUGGESTION_END]]the 3GPP wireless sensing service from Mobile Operator B for the real-time road digitalization.
In order to monitor traffic volume constantly, Mobile Operator B has deployed and activated base stations and roadside units around traffic intensive areas, such as crossroads, urban roads and highways, in order to provide a wide sensing coverage and capability to detect and track all moving objects, including vehicles, bicycles and pedestrians effectively.
NOTE: For the ease of elaboration, [[SUGGESTION_START]]the [[SUGGESTION_END]]base station is acting as sensing transmitter and/or sensing receiver in this case. Other sensing modes can also be feasible and useful as well.
---------- Eighteenth Change ----------
7.6.4 Post-conditions
Thanks to the network-wide coverage, the [[SUGGESTION_START]]base station’s [[SUGGESTION_END]]bird's-eye-view of [[SUGGESTION_START]]the environment[[SUGGESTION_END]], road digitalization is enabled by capturing real-time information of the road environment.
---------- Nineteenth Change ----------
8.1.2 Pre-conditions
Alex is living in a mountainous area with challenging access conditions. He has registered UEs (e.g. smartphone, tablet, computer) for communication and [[SUGGESTION_START]]his [[SUGGESTION_END]]registered network can provide the wide coverage with TN and also NTN.
---------- Twentieth Change ----------
8.1.3 Service Flows
1. Alex is located in a mountainous area with difficult access conditions.
2. The network ensures the continuous connectivity between the terrestrial and non-terrestrial networks (TN and NTN).
3. [[SUGGESTION_START]]For consul[[SUGGESTION_END]][[SUGGESTION_START]]t[[SUGGESTION_END]][[SUGGESTION_START]]ation with a doctor, [[SUGGESTION_END]]Alex connects to the [[SUGGESTION_START]]network [[SUGGESTION_END]]using a compatible device (smartphone, tablet, computer), with sufficient video quality to enable the doctor to diagnose common health problems.
4. Throughput and latency requirements are less stringent than for URLLC communications, enabling [[SUGGESTION_START]]a [[SUGGESTION_END]]smooth consultation [[SUGGESTION_START]]call [[SUGGESTION_END]]even under variable network conditions.
5. Advanced security mechanisms are in place to protect data integrity, confidentiality, and availability across the network.
---------- Twenty First Change ----------
8.2.1 Description
Different from terrestrial cellular and GEO deployment, the LEO satellite has a moving coverage which requires a new deployment thinking. A satellite operator may have a launch plan for a large size LEO constellation. However, the deployment of complete LEO constellation is a long-term task which needs several years or even longer. The deployment is both time consuming and costly. According to the target service area, a satellite operator may choose the sequence of satellites[[SUGGESTION_START]] for deployment[[SUGGESTION_END]], which helps to provide the service to the user of target service area as early as possible. The satellite operator can start the commercialization of the constellation once the number of the satellites is sufficient instead of waiting until the whole constellation has been deployed completely. Even with the [[SUGGESTION_START]]partially[[SUGGESTION_END]] deployed constellation, the satellite operator should guarantee the uninterrupted service for the users. To achieve this, each satellite of the constellation should provide larger coverage compared with the case that the LEO satellites are densely deployed.
---------- Twenty Second Change ----------
8.2.2 Pre-conditions
Operator A is a satellite operator with launch plans for a large-size constellation of LEO satellite. Alice has subscribed the UE directly to the satellite service of Operator A and her smartphone is capable [[SUGGESTION_START]]of [[SUGGESTION_END]]directly connect[[SUGGESTION_START]]ing[[SUGGESTION_END]] with Operator A's satellites.
---------- Twenty Third Change ----------
8.2.3 Service Flows
1. Operator A plans to deploy a large-size LEO constellation step by step.
2. Before the deployment of the large-size LEO constellation is complete, Operator A decides to provide connectivity service to users when the sparse constellation with sufficient satellites (e.g. several hundred) for the target service area has been [[SUGGESTION_START]]deployed[[SUGGESTION_END]]. Due to the sparse deployment, each satellite needs to support sufficiently long-rang[[SUGGESTION_START]]e[[SUGGESTION_END]] coverage.
3. Alice is going hiking in a mountain area with her smartphone. She noticed that there is no terrestrial network coverage and her smartphone has switched to the Operator A's satellite network. [[SUGGESTION_START]]During this time[[SUGGESTION_END]], she picks up a call from her mother. She talks with her mother for several minutes. During the call, there is no interruption since the satellites provide the continuous coverage.
4. After walking for a long time, Alice takes a break[[SUGGESTION_START]] and[[SUGGESTION_END]] she browses the web to search [[SUGGESTION_START]]for [[SUGGESTION_END]]some information on the hiking route using her smartphone. There is also no interruption during the internet access. After the break, Alice continues hiking.
---------- Twenty Third Change ----------
8.2.4 Post-conditions
Thanks to the support of [[SUGGESTION_START]]satellite communication [[SUGGESTION_END]]service using LEO satellites access with spar[[SUGGESTION_START]]s[[SUGGESTION_END]]e satellite deployment, Operator A can start the commercial usage of the constellation even though the constellation is sparse, while the users can experience [[SUGGESTION_START]]good communications via [[SUGGESTION_END]] satellite in the constellation's early deployment stage.
---------- Twenty Fourth Change ----------
8.3.1 Description
Reelika has just started a famous ultra-trail "Grand Raid des Pyrénées" in the Pyrénées mountains between France and Spain. She is equipped with wearable mobile devices including a wrist watch, a forehead mounted camera and a smart phone in order to share in real time her position, her health conditions and possibly voice message or short videos with her friends Guillaume and Didier in Paris but also with her coach Loïc waiting for her at a check point in the middle of the race in a remote area without mobile access coverage. Reelika expects that her personal data (e.g. position, health conditions, etc.) are protected[[SUGGESTION_START]] from unauthorized parties[[SUGGESTION_END]].
As she starts, her mobile device is first served by the mobile access but then when climbing the first mountain, she reaches the edge of the mobile access coverage. Throughout the race, her wearable devices are transferred between mobile and satellite access whenever the mobile access coverage disappears.
Editor's note: consistency of terms – mobile access coverage = TN access?
Her friends do not perceive the transition between both access technologies (i.e. no packet loss [[SUGGESTION_START]]for [[SUGGESTION_END]]non real time service and no interruption for real time service). However, they may perceive that the quality of the image/voice is adapted to varying available bandwidth and latency.
The race organisers as well as all her friends including Loïc, [[SUGGESTION_START]]regularly [[SUGGESTION_END]]keep receiving [[SUGGESTION_START]]Reelika’s [[SUGGESTION_END]]position and health conditions. At some point Loïc detects that Reelika is getting very tired and cannot progress. He decides to send her a short message to cheer her up. Reelika’s wearable mobile device receives the text message and convert[[SUGGESTION_START]]s[[SUGGESTION_END]] it to voice. Reelika listens to the message and thanks him with a video message.
During the night, Reelika initiates a call [[SUGGESTION_START]]to the race organizers to report an emergency[[SUGGESTION_END]]. Reelika reports to the race organisers that she is with a runner who injured himself while accidentally falling in a pit. The race organisers detect that Reelika may have crossed the Spanish border and launches a request for reliable positioning service in order to determine whether the French or the Spanish public safety organisation should intervene. Reelika's reported position is confirmed to be in Spain and hence the Spanish public safety organisation is alerted who takes over Reelika[[SUGGESTION_START]]’s[[SUGGESTION_END]] call. The on-going emergency call is extended to a Spanish first responder who fortunately is not far from the accident. He is able to reach the spot within a quarter of an hour, thanks to complementary guidance from Reelika.
Editor's note: consistency of terms – emergency call to third party and multi-party emergency call in Service Flow clause?
Reelika is able to continue her race leaving the injured runner in good hands.
As Loïc is monitoring the progress of Reelika, it starts raining and Loïc decides to enter the large shelter where the runners will find some assistance. The rain is so dense that the race organisers decide in coordination with the public safety to alert the runners and all the followers along the track about possible slippage of terrain. An alert message is broadcast to the area with the intent to reach the maximum number of persons, including the ones in indoor conditions. With this alert message, public safety requests all runners to report their position every 10 minutes during the next two hours. Loïc (indoor), Reelika and all race participants/followers in the area receive this alert message.
At a certain point in the race, Reelika sets off on a bad track, due to local activists[[SUGGESTION_START]] changing the route markers[[SUGGESTION_END]]. The monitoring system of the race organizer quickly detects that she is going off the planned track. She is immediately alerted with an alert message that triggers an alarm sound in her wristwatch. She consults her wearable device [[SUGGESTION_START]]which shows [[SUGGESTION_END]]where she lost the planned track and how she can rejoin it.
The race organizers inform the race participants behind Reelika about the virtual track to rejoin the planned track as well as [[SUGGESTION_START]]the [[SUGGESTION_END]]field member of the race organization to correct the track. This prevents late runners to lose time with this track issue and no one need[[SUGGESTION_START]]s[[SUGGESTION_END]] to be sent after lost runners.
Fortunately, Reelika can successfully complete her race without further technical incident and all her friends join in a video conference to celebrate with her.
---------- Twenty Fifth Change ----------
8.3.3 Service Flows
1. From Reelika (mobile or satellite access) to the race organizers (mobile access), to Guillaume/Didier (mobile access) and to Loïc (satellite access): messaging (including reported position), non-real time and real time services.
2. From Reelika (mobile or satellite access) to Loïc (satellite access): messaging services.
3. From Loïc (satellite access) to Reelika (mobile or satellite access) messaging services.
4. From Reelika (satellite access) to public safety headquarter (mobile access): emergency call (including reported position).
5. From race organiser (mobile access) to Reelika (satellite access): reliable positioning service (network based).
6. Between Reelika (satellite access), Spanish public safety headquarters (satellite access) and 1st responder (satellite access): multi party emergency call (including reported positions of both Reelika and 1st responder).
7. From public safety headquarter (mobile access) to Reelika (satellite access) and Loïc (satellite access, indoor): public warning.
8. From Reelika (satellite access) to in field first responder (satellite access) and to public safety headquarter (mobile access): reported position.
9. From race organisers to Reelika (satellite access) and following runners (satellite access): alert about off track and guidance to re-join the planned track.
10. Between Reelika (mobile access), Guillaume/Didier in France (mobile access) and Loïc (satellite access): real time services (video conference).
---------- Twenty Sixth Change ----------
8.3.6 Potential New Requirements needed to support the use case
[PR 8.3.6-1]: The 6G system shall be able to ensure service continuity with minimum interruption for UE[[SUGGESTION_START]]s[[SUGGESTION_END]] during the transition between terrestrial and satellite access and vice versa.
[PR 8.3.6-2]: The 6G system via its satellite access, shall support SMS delivery to a high density of UE[[SUGGESTION_START]]s[[SUGGESTION_END]] i.e. up to [1000] UE[[SUGGESTION_START]]s[[SUGGESTION_END]] per km2.
[PR 8.3.6-3]: Subject to regulatory requirements, the 6G system using satellite access, shall be able to support PWS for broadcasting warning notifications to UE[[SUGGESTION_START]]s[[SUGGESTION_END]] in adverse propagation conditions e.g. light indoor conditions, dense forest.
---------- Twenty Seventh Change ----------
8.5.1 Description
The national meteorological centre has detected [[SUGGESTION_START]]an [[SUGGESTION_END]]upcoming [[SUGGESTION_START]]major[[SUGGESTION_END]] storm with heavy rains which are likely to cause a major flood in the Valencia region. The alert is propagated to the public safety organisations as well as to the population.
All first responders are equipped with a set of wearable devices (i.e. handheld, bodycam, vital signs monitoring sensors, …) and some with drones with regular and thermal (infrared) cameras.
The storm and floods [[SUGGESTION_START]]caused [[SUGGESTION_END]]power supply cuts in the region that resulted in the loss of terrestrial cellular network connectivity. Thanks to the available satellite access, the national public safety organisation is guiding first responders that are already on site (most are volunteers among the population). Additional first responder teams are deployed[[SUGGESTION_START]],[[SUGGESTION_END]] each with their all-terrain vehicles or amphibious vehicles in the harbour. The remaining bandwidth of the satellite access can be used by the population to exchange messages.
Quickly, it is decided to ask assistance from public safety and disaster relief organisations from neighbouring countries.
Local access networks are mounted on each land vehicle with enabled satellite connectivity. Each local access network can be used for communication between the team members, with their headquarters (area, regional, national) as well as with other responder teams. Even civilians could exploit this connectivity if sufficient remaining bandwidth is available. When the wind burst [[SUGGESTION_START]]subsided,[[SUGGESTION_END]] drones are used by team members to assess the disaster, report to headquarter(s) and rescue the population.
As the flood took place in the coastal area, casualties are being spread to the sea and therefore, public safety organisation[[SUGGESTION_START]]s[[SUGGESTION_END]] are deploying boats[[SUGGESTION_START]],[[SUGGESTION_END]] each equipped with an on-board local access network and satellite connectivity.
Responders or drones may have to move beyond the coverage of a local access network. In such cases[[SUGGESTION_START]],[[SUGGESTION_END]] continuity of service is ensured through smooth transition to the satellite access. Several local access networks can be directly connected via satellite to ease the coordination between the teams. User equipment belonging to national first responders can seamlessly communicate with user equipment [[SUGGESTION_START]]of[[SUGGESTION_END]] responders of the neighbouring countries.
During the recovery phase, an HIBS (base station on board a HAPS) can be launched to increase the available capacity over the area before the terrestrial base stations are repaired.
---------- Twenty Eighth Change ----------
8.5.3 Service Flows
Given that the terrestrial network is down, all UEs will look for [[SUGGESTION_START]]an [[SUGGESTION_END]]alternative available network in the area, that is the satellite access and later the HAPS based access network.
Satellite or HAPS based access network can be used to support:
- Public warning service to [[SUGGESTION_START]]the entire [[SUGGESTION_END]]population (including 1st responders [[SUGGESTION_START]]and [[SUGGESTION_END]]volunteers) in the impacted area (also in adverse propagation conditions such as light indoor)
- Non real time and real time services to pedestrian[[SUGGESTION_START]]s[[SUGGESTION_END]] or drone mounted UE[[SUGGESTION_START]]s[[SUGGESTION_END]]
- Backhaul connectivity to vehicle/boat mounted local access point
- Connectivity (without usage of satellite feeder link) between two local access points
The satellite or HAPS based access bandwidth can be pre-empted for public safety organisations but the remaining bandwidth if available may be used by the population only for messaging.
Vehicle/boat mounted local access points can be used
- To serve pedestrian or drone mounted UE[[SUGGESTION_START]]s[[SUGGESTION_END]]
- To support UE to UE connectivity
---------- Twenty Ninth Change ----------
8.6.3 Service Flows
1. At some point, the IoT or low-power device may lose its original positioning sources due to one of the following reasons:
- GNSS-originated position: GNSS signal may be lost due to interference, or obstruction, or intentionally disabled to augment its autonomy.
- Terrestrial network-originated position: the IoT or low-power device may have moved beyond terrestrial network coverage, such as in the case of asset tracking sensor installed on a container to be transport[[SUGGESTION_START]]ed[[SUGGESTION_END]] by sea, rail, or road.
In this context, the IoT or low-power device, served by the satellite access, is expected to know an approximate position from prior positioning. This requires the IoT or low-power device to refresh a more accurate position.
Therefore, the IoT or low-power UE initiates the 3GPP positioning method over satellite to estimate its current location.
The 6G system broadcasts to the UE via its satellite access at least the following necessary information, including but not limited to:
- Network assistance data: Satellite ephemeris and additional assistance data to improve accuracy (e.g. ionospheric models to correct the atmospheric delay errors, etc.)
- Reference signal for time of arrival measurements.
---------- Thirtieth Change ----------
8.7.1 Description
Many places and people are currently underserved when it comes to Mobile Broadband (MBB) services. From the user's and society's point of view a lot is gained already with a basic internet connection, since many internet services can be delivered with a fairly moderate bitrate, and the most important needs would be met already with a low activity factor. Therefore, the problem is mainly related to providing remote service coverage for basic MBB. Still, such basic services may be the basis of sensitive systems (e.g. related to health or surveillance) and therefore uninterrupted and resilient operation is important.
The use case Global [[SUGGESTION_START]]Mobile [[SUGGESTION_END]][[SUGGESTION_START]]Video [[SUGGESTION_END]]is about provisioning access to basic broadband services, exemplified by the capability to make a video call, at remote places on earth where people live or work, using handheld-type of 3GPP devices. A full global area coverage (e.g. 99.9 %) needs to be provided through NTN access. However, TN needs to handle more populated areas, for instance through very large cells, such that the total traffic to be handled by NTN is not too high.
Based on population density this can be divided into remote and deep rural scenarios.
1) Remote scenario
[[SUGGESTION_START]]This case c[[SUGGESTION_END]]over[[SUGGESTION_START]]s[[SUGGESTION_END]] virtually all people on earth, and virtually all areas including oceans. [[SUGGESTION_START]]Most [[SUGGESTION_END]]cases [[SUGGESTION_START]]involve [[SUGGESTION_END]]very sparsely populated areas (approx. < 1 person/km2). The expectation is that this scenario is covered mainly by NTN access. A lower rate, activity factor, and area traffic can be supported in these areas. The Global [[SUGGESTION_START]]Mobile [[SUGGESTION_END]]Video service might be delivered with reduced quality compared to the Deep Rural scenario.
2) Deep Rural scenario
A deep rural scenario represents sparsely populated areas (approx. 1-10 persons/km2). The expectation is that this scenario is mainly covered by TN access, from large macro cells to very large "boomer" cells, and in addition have NTN coverage to fill gaps between TN cells. A higher rate, activity factor, and area traffic can be supported in these areas.
Key value impact analysis:
Energy resources: Increased energy consumption due to the buildout of networks.
Material resources: The material need increases when building out networks with new 6G sites and satellites. Material resource depletion increases as materials sent into space is considered not recyclable.
Emissions: Internet access provides possibility for various activities (e.g. bank, hospital, work) leading to less travel needs and thereby reduced emissions. Emissions related to producing and operating new equipment can be somewhat mitigated.
Biodiversity and land use: New sites, especially high tower 6G sites, increases the land use for ICT.
Education: Internet access enable access to remote educational material.
Health: Global internet could enable using remote healthcare/first aid leading to better healthcare access in rural areas.
Inclusion and Equality: Providing the possibility [[SUGGESTION_START]]of [[SUGGESTION_END]]internet access facilitating banking, healthcare and education services is positive from an inclusion point of view. However, it is important not to leave anyone behind and thereby risk widening the gap.
Trustworthiness: Increased resilience for ICT services could be provided by satellites included in the use case as back up in unforeseeable situations.
Work and income: This use case could enable the possibility of running businesses from everywhere.
Infrastructure: Increasing access to [[SUGGESTION_START]]the [[SUGGESTION_END]]internet is the main goal of the use case.
---------- Thirty First Change ----------
8.7.3 Service Flows
1. User initiates or receives video call to handheld device to/from another user.
2. If TN access [[SUGGESTION_START]]is [[SUGGESTION_END]]available the traffic is supported by TN, otherwise NTN access is used.
3. The call is sustained uninterrupted, also under mobility, until either user terminates [[SUGGESTION_START]]the [[SUGGESTION_END]]call.
---------- Thirty Second Change ----------
9.2.1 Description
Usually, the limited image rendering capability of mobile devices poses challenges for services that require high-quality image processing capability like high order super-resolution and de-noising algorithm, as well as high-quality rendering capability like ray tracing effect for gaming. As shown in Figure 9.2.1-1, the GPU computing power for image rendering increasing in last ten years and the predication up to 2030 for mobile phone platform as well as the computing power required for advanced services has been summarized as below based on [16], [17] and [18]:
Figure 9.2.1-1: increasing GPU computing power for mobile platform
It can be observed that with the failure of Moore's law, the growth of mobile platform computing power gradually slows down. Offloading rendering task to network/cloud is a promising trend to support advanced multi-media services in the future. However, the latency for offloading rendering task to network/cloud is quite difficult to be always guaranteed in anytime/anywhere due to the varied radio channel conditions and network load/coverage. UE-Network-Cloud synergized multi-media operation is a collaboration mode which allows mobile devices to dynamically offload part of rendering task to the cloud to improve the user experience based on communication link status and maintains deterministic rendering task processing latency by local backup processing in case communication link is not good as shown in Figure 9.2.1-2. There's an expectation for 3GPP systems to consider such dynamic rendering offload framework to adapt to the synergized multi-media service processing trend.
Figure 9.2.1-2: UE-Network-Cloud synergized multi-media operation framework
The rendering task split function is a function provided by the UE operating system (OS) to map the rendering task request from the application (APP) to local or remote rendering resources. Upon [[SUGGESTION_START]]determin[[SUGGESTION_END]][[SUGGESTION_START]]in[[SUGGESTION_END]][[SUGGESTION_START]]g that [[SUGGESTION_END]]a rendering task is requested to be executed, the task split function can select the local and/or the remote rendering resources based on relevant factors, such as required processing quality, radio link status, [[SUGGESTION_START]]and [[SUGGESTION_END]]estimated latency, to ensure [[SUGGESTION_START]]a [[SUGGESTION_END]]deterministic user experience for the requested multi-media service. Typical services which can benefit from UE-Network-Cloud synergized multi-media operation include photo enhancement and ray tracing for gaming and so on.
The UE-Network-Cloud synergized operation raises several new issues, as follows:
- Uplink data rate: The rendering task offloading operation usually requires [[SUGGESTION_START]]a [[SUGGESTION_END]]high uplink data rate to achieve higher service availability, considering the metadata to be uploaded for the rendering task is usually large with tight latency, e.g. 5 Mbits/50 ms, 150 Mbits/1.5 s (see detail in service flow part). However, the current network typically cannot guarantee sufficient uplink data rate especially at cell edge.
- Real time communication link capability awareness: The rendering task split function might not know the exact communication link capability such as data rate, latency, and UE power consumption for data transmission, therefore it is hard for the UE to make the split decision.
- Computing task level PDB requirement guarantee: The uplink metadata burst for a rendering task offloading arrives in a random way, and the latency requirement is intended for the whole data burst (i.e. the metadata of the task) rather than an individual packet. As shown in Figure 9.2.1-3, the current GBR QoS flow framework guarantees the data rate in a fixed averaging window and fulfils latency in a per packet basis which is suitable for the periodic traffic. For the burst data carrying the metadata for rendering tasks, the latency and data rate should be guaranteed toward the whole data set rather than a specific packet. Therefore, the fixed averaging window as for periodic traffic is not suitable for bursty traffic. There is no suitable QoS type to guarantee the deterministic transmission latency for unpredictable uplink bu[[SUGGESTION_START]]r[[SUGGESTION_END]]sty data traffic.
Figure 9.2.1-3: Burst latency requirement
---------- Thirty Third Change ----------
9.2.2 Pre-conditions
1. Rendering resources are deployed in the cloud.
2. The UE operatin[[SUGGESTION_START]]g[[SUGGESTION_END]] system supports map[[SUGGESTION_START]]ping[[SUGGESTION_END]] the rendering task from the APP to the local (in the mobile phone) and/or the remote (in the cloud) rendering resources.
---------- Thirty Fourth Change ----------
9.2.3 Service Flows
Synergized photo enhancement case:
1. User A is in a [[SUGGESTION_START]]tourist [[SUGGESTION_END]]attraction, taking photos with his mobile phone. Once he clicks the photo button, the phone sen[[SUGGESTION_START]]s[[SUGGESTION_END]]or produces raw data of the photo, including up to 6 frames (4K per frame) of raw files spanning 300 ms for HDR reconstruction. Raw files retain the original uncompressed image data from the camera sensor, which is usually larger than compressed image formats such as JPEG and PNG, and has unique advantages for [[SUGGESTION_START]]image [[SUGGESTION_END]]processing in practice.
NOTE 1: 2 to 10 frames are needed for HDR reconstruction based on [19]; 6 frames are assumed in this use case.
2. Upon reception of the rendering task request from [[SUGGESTION_START]]the [[SUGGESTION_END]]camera APP for photo enhancement, the rendering task split function inside the UE OS determines whether or not to upload the raw data to the cloud for more powerful post-processing to enhance the photo quality, for which some factors would be considered such as potential delay and UE power consumption for data transmission.
3. If the rendering task split function decides to upload the photo to the cloud, the raw data for the photo is compressed to around 150 Mb (based on the assumption of 6 frames × 4K raw data and compressed ratio assumption in [20] [21]) and uploaded to the cloud within around 1.5 s and the processed photo [[SUGGESTION_START]]is downloaded [[SUGGESTION_END]]from the cloud. The UE can reduce the number of frames for uploading based on communication link capability. Therefore, the required uplink data rate in radio layer would be 150 Mb/1.5 s=100 Mbps.
NOTE 2: E2E latency of 3 s is assumed based on users' patience statistics as shown in [22], where 1.5 s is allocated for uploading to the cloud while 1.5 s for processing in the cloud and downloading to the UE.
4. If the rendering task split function decides not to upload the photo to the cloud, the local post-processing will be performed.
5. When the user A views the photos, the downloaded photo or the local enhanced photo can be shown to the user.
Synergized gaming enhancement case:
1. User A starts a [[SUGGESTION_START]]game[[SUGGESTION_END]], the gaming APP produces the scene metadata, including 3D objects, lighting data, and materials, user actions, etc. The typical data size after compression can be 5-20 Mb (assuming middle to high complexity 3D model including 0.35 to 2 million vertexes (200 bits per vertex) 3D models and compression ratio assumption for 3D model in [23]).
2. The gaming APP submits the metadata to [[SUGGESTION_START]]the [[SUGGESTION_END]]rendering task split function inside the UE OS for rendering[[SUGGESTION_START]].[[SUGGESTION_END]] [[SUGGESTION_START]]The [[SUGGESTION_END]]rendering task split function can seek to utilize the more powerful computing in the cloud for higher quality such as ray tracing effect. The rendering task split function determines how to split the metadata to the cloud by considering some factors such as potential delay and UE power consumption for data transmission. For example, the UE selects to offload the slow changed scene background rendering to the cloud while performing rendering for fast moving objects using local rendering resources.
3. The UE uploads the metadata allocated for the cloud rendering within 50 ms [24] and download[[SUGGESTION_START]]s[[SUGGESTION_END]] the rendering result.
4. The UE combines the local rendered result and cloud rendered result [[SUGGESTION_START]]and presents the overall result [[SUGGESTION_END]]to user A.
5. Once the radio condition goes worse, the full local rendering can be activated to maintain [[SUGGESTION_START]]the [[SUGGESTION_END]]user experience.
---------- Thirty Fifth Change ----------
9.2.5 Existing features partly or fully covering the use case functionality
The split rendering architectures defined in TR 26.928 [50] and TS 26.565 [51], and the corresponding QoS requirements in TS 22.261 [14], allow a UE to offload rendering requirement[[SUGGESTION_START]]s[[SUGGESTION_END]] to the cloud for XR rendering. These architectures assume that the metadata including 3D models of the gaming and APP logic is pre-installed in the cloud, therefore only user [[SUGGESTION_START]]provided [[SUGGESTION_END]]information is needed to be transferred in the uplink and there is no dynamic offload decision on whether to render a specific scene in the cloud or [[SUGGESTION_START]]via [[SUGGESTION_END]]UE local resources (i.e. fully rely on the rendering result in the cloud). Therefore, the uplink requirement and dynamic offload decision requirement are not covered by the existing split rendering use cases and part of the downlink requirements on rendering result delivery are covered by the existing split rendering use cases.
It is worth noticing that Edge Enabler Client (EEC) is defined in TS 23.558 [52], which provides supporting functions needed for Application Clients. There is some similarity between EEC and the "rendering task split function" described in this use case.
---------- Thirty Sixth Change ----------
9.3.1 Description
There is a growing demand [[SUGGESTION_START]]to support [[SUGGESTION_END]]people [[SUGGESTION_START]]who [[SUGGESTION_END]]use diverse types of devices other than smartphones, which connect to mobile network system [29]. Then, in 6G, a [[SUGGESTION_START]]variety of[[SUGGESTION_END]] devices are expected to be connected to [[SUGGESTION_START]]the [[SUGGESTION_END]]6G system. With [[SUGGESTION_START]]this [[SUGGESTION_END]]trend, wearable devices are expected to be more popular devices for people, but even with such devices like XR devices, users would like to experience immersive applications which require much computing capability for processing application data. However, due to limited computing capability, [[SUGGESTION_START]]the [[SUGGESTION_END]]user experience could be affected. Therefore, there will be strong need for such devices to be able to offload application data processing to the edge/cloud server.
In current computing technology, application data processing can be offloaded to edge/cloud server [[SUGGESTION_START]]wh[[SUGGESTION_END]][[SUGGESTION_START]]ich are[[SUGGESTION_END]] completely separated from [[SUGGESTION_START]]the [[SUGGESTION_END]]mobile network system. However, from [[SUGGESTION_START]]a [[SUGGESTION_END]]user experience point of view, [[SUGGESTION_START]]the [[SUGGESTION_END]]6G system shall support network and/or device control based on computing offload use of user devices.
This use case aims to [[SUGGESTION_START]]describe [[SUGGESTION_END]]a service scenario [[SUGGESTION_START]]where [[SUGGESTION_END]]a user wants a computing offload service supported by the network and [[SUGGESTION_START]]the corresponding [[SUGGESTION_END]]requirements for [[SUGGESTION_START]]the [[SUGGESTION_END]]6G system.
---------- Thirty Seventh Change ----------
11.1.1 Description
Urban air mobility (UAM) is a new safe, secure and more sustainable air transportation system for passengers and cargo in urban environments, enabled by new technologies and integrated into multimodal transportation systems. The transportation is performed by electric vertical take-off and landing (eVTOL) aircrafts, remotely piloted or with a pilot onboard [32]. In February 2024 an eVTOL named as "PROSPERITY" [33] that can contain 5 passengers conducted a Shenzhen-Zhuhai test flight, which was a cross-sea and cross-city route. KT showed, at the MWC2024, their UAM Skypath solution, which can provide 5G service for UAM aircrafts flying at 300 – 600 meters altitude.
Compared with the common UAVs (Uncrewed Aerial Aircraft), the UAM aircrafts have the following major differences:
- Large size and heavy weight, higher AGL (above ground level)
Low-altitude airspace usually refers to the airspace with a vertical distance of less than 1000 m from the ground, and it can be extended to less than 3000 m according to the characteristics and actual needs of different regions [34]. The AGL of UAM aircrafts can be up to 1000 m, while the AGL of small UAVs is less than 300m. Communication for the UAM aircrafts with higher AGL need to be considered.
- Higher reliability and safety requirement with human beings onboard
UAM aircrafts share some common low altitude airspace. As predicted by Professor Shen, there will be about 100,000 UAVs flying in Shenzhen's sky at the same time in the future [36]. Considering the area of Shenzhen is 1997 km2, there will be about 50 UAVs flying in 1 km2 airspace. To ensure high reliability and safety, the UAM aircrafts must be aware of the object information that [[SUGGESTION_START]]are [[SUGGESTION_END]]near its flight trajectory.
To [[SUGGESTION_START]]provide for [[SUGGESTION_END]]the safety of aircrafts, Detect and Avoid (DAA) technology is widely used in UAVs by using a combination of sensors, cameras, and radar to continuously monitor the UAV's surroundings [38]. These sensors detect obstacles, other aircraft, and potential hazards in the flight path. The system then processes this information in real-time and adjusts the UAV's flight path to avoid collisions. However, the capabilities of the sensors on UAVs are not able to sense the blockage far away. Since the UAM aircrafts carry human beings on board, UAV's DAA system is not adequate to ensure the safety of passengers onboard. By also utilizing the 3GPP sensing service, the safety of UAM aircrafts flying in the common airspace can be [[SUGGESTION_START]]accommodated[[SUGGESTION_END]], as well as the running efficiency of UAM aircraft can be improved so as to transport more passengers or goods.
Typically, the message size for sensing one object could be 1 Kbyte [37], including information of size/position/speed/direction. Around 25 objects (25 kKbyte) per frame (20 ms) need to be sensed for an aircraft. The reliability requirement for UAM aircrafts is about 99.9 %.
- In addition, [[SUGGESTION_START]]the communications needs of[[SUGGESTION_END]] passengers on board of [[SUGGESTION_START]]the [[SUGGESTION_END]]UAM has to be considered.
---------- Thirty Eighth Change ----------
11.1.3 Service Flows
1. May and Fei plan [[SUGGESTION_START]]to [[SUGGESTION_END]]visit City B from City A by UAM, [[SUGGESTION_START]]which[[SUGGESTION_END]] can contain up to 4 passengers.
2. To [[SUGGESTION_START]]provide for [[SUGGESTION_END]]the safety of the passengers onboard, the UAM aircraft requests the sensing service from the 6G network. The base station(s) along the flight path will sense the environment information especially other aircrafts within its area[[SUGGESTION_START]] of interest[[SUGGESTION_END]], e.g. the area [[SUGGESTION_START]]of interest [[SUGGESTION_END]]is an airspace with the size of 1 square kilometre and height from 0 to 1000 meters.
3. As part of the sensing service, the 6G network sends the sensing and/or the warning information to the UAM aircraft.
4. Upon receipt of the above information, the UAM aircraft further processes the information to identify [[SUGGESTION_START]]an[[SUGGESTION_END]][[SUGGESTION_START]]y [[SUGGESTION_END]]collision threat[[SUGGESTION_START]]s[[SUGGESTION_END]]. If there are some blockages threatening flight safety, the UAM aircraft will perform collision avoidance in advance.
5. Meanwhile passengers onboard either watch HD video or surf the Internet using their smartphone [[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]or [[SUGGESTION_END]][[SUGGESTION_START]]even s[[SUGGESTION_END]][[SUGGESTION_START]]leep[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]. The view is very beautiful during the flight, May is very happy to share the scenery with her friends through 4K real-time video on social media by smartphone. Fei is not interested in the scenery during the flight, and he is enjoying a football match live broadcast. The core network sends data of [[SUGGESTION_START]]a [[SUGGESTION_END]]football game with 4K or 8K live broadcast to base station, then Fei can receive the football game data by his smartphone.
---------- Thirty Ninth Change ----------
11.1.6 Potential New Requirements needed to support the use case
[PR 11.1.6-1] The 6G System shall support the transmission of sensing result information to the UAM aircrafts with the following KPI requirements.
Typical transmission interval
Altitude AGL
Typical message Size
End to end Latency
Reliability
Sensing result to a UAM aircraft
[20 ms]
up to 1000 m
[25 kbyte]
[20 ms]
[99.9 %]
NOTE: Typically, the message size for sensing one object is 1 kbyte [x6]. It is assumed that around 25 objects (25 kbyte) per frame (20 ms) are sensed for an aircraft. The reliability requirement for UAM aircrafts is about 99.9 %.
[PR 11.1.6-2] The 6G System shall support services provided to the UAM applications with the following KPI requirements.
Services
Data rate
End to end Latency
Altitude AGL
Service area
8K video live broadcast
100 Mbps
UAM aircraft originated
(note)
200 ms
(note)
up to 1000 m
Urban, scenic area
600 Kbps
UAM aircraft terminated
(note)
20 ms
(note)
up to 1000 m
Video streaming
4 Mbps for 720p video
9 Mbps for 1080p video
UAM aircraft originated
(note)
100 ms
(note)
up to 1000 m
Urban, rural area
100 Mbps for 8K video
UAM aircraft originated
(note)
100 ms
(note)
up to 1000 m
NOTE: These values are aligned with the KPIs for services provided to the UAV applications in TS 22.261 [14], table 7.1-1.
---------- Fortieth Change ----------
W. 1 Use case on coordinating computing and communication for XR rendering
W.1.1 Description
The exploration of ultimate user experience in consumer products, and the digital and intelligent transformation in the industrial fields have promoted the wide use of computing-intensive applications such as AR/XR, cyber-physical systems, industry robots and etc. However, the balance between computing capabilities and the size and cost of the devices raises a big challenge for the deployment of the applications. The ITU-R report [39] points out that several emerging technologies are being envisioned to address the challenges. One trend is to process data at the network edge close to the data source for real-time response, low data transport costs and energy efficiency by edge computing technologies, while another trend is to scale out device computing capability beyond its physical limitations by splitting computing workload over reachable computing resources. With the development of each technology trend, the [[SUGGESTION_START]]lack of [[SUGGESTION_END]]independent control, management and orchestration of communication and the computing resources has been identified [[SUGGESTION_START]]as causing a[[SUGGESTION_END]] significant negative impact on the performance of the end-to-end solution.
The 6G system is expected to change the situation by coordinating the communication services and computing resource utilization in the stage of system architecture design. In this way, the 6G network can make the ubiquitous single-point computing resource connected and participate in the scheduling of various types of computing resources such as UE, MEC server, cloud server. For example, the 6G network can select appropriate computing resource[[SUGGESTION_START]]s[[SUGGESTION_END]] based on service characteristics to achieve optimal resource usage. On the other hand, the data transmission for the computation can be more flexible and efficient [[SUGGESTION_START]]utilizing [[SUGGESTION_END]]additional information (e.g. workload, available capability) about the selected computing resources.
The real-time rendering of 3D scenes in [[SUGGESTION_START]]a [[SUGGESTION_END]]large-scale XR application is a typical case of [[SUGGESTION_START]]a [[SUGGESTION_END]]computing-intensive application, which requires [[SUGGESTION_START]]a [[SUGGESTION_END]]large amount of calculation to handle the complicated model and texture mapping. Sometime[[SUGGESTION_START]]s[[SUGGESTION_END]] limited [[SUGGESTION_START]]by [[SUGGESTION_END]]the capability of [[SUGGESTION_START]]the [[SUGGESTION_END]]hardware, it is impossible for a single device to render the scene individually[[SUGGESTION_START]].[[SUGGESTION_END]] [[SUGGESTION_START]]The [[SUGGESTION_END]]distribution of the computing workload to other computing capable nodes can solve the issue. Sometimes the duplicated rendering of the same XR scene which is being requested by multiple users can be avoided if [[SUGGESTION_START]]they [[SUGGESTION_END]]choose to execute the rendering in the cloud server. However, [[SUGGESTION_START]]as [[SUGGESTION_END]]the XR application [[SUGGESTION_START]]has [[SUGGESTION_END]]little information on the status of computing resources[[SUGGESTION_START]],[[SUGGESTION_END]] it is difficult for the planned data transmission and computation to work together efficiently.
This use case illustrates how the 6G system enables better user experience of split XR rendering via the coordination of computing resources and communication resources.
Figure W.1.1-1: Coordinating Computing and Communication for XR rendering
W.1.2 Pre-conditions
Several computing resource nodes (e.g. MEC servers, cloud computing center) for XR services are deployed in different locations and have been enrolled in [[SUGGESTION_START]]the [[SUGGESTION_END]]6G core network. MEC server[[SUGGESTION_START]]s[[SUGGESTION_END]] A, B and C are provisioned as a part of rendering pipelines to execute render engine, engine adaption, rendering acceleration for the rendering task. [[SUGGESTION_START]]The c[[SUGGESTION_END]]loud computing center is capable of all the functions for XR rendering.
XR Application Platform provides the render services to various XR applications.
Task Management Server is responsible for task analysis, sub-task planning, graphic composition and etc.
The UE supporting [[SUGGESTION_START]]the [[SUGGESTION_END]]XR application is the subscriber of the 6G network. It has registered to [[SUGGESTION_START]]the [[SUGGESTION_END]]6G network and been authorized for the computing coordination service.
W.1.3 Service Flows
1. Regarding [[SUGGESTION_START]]the [[SUGGESTION_END]]XR application's need, [[SUGGESTION_START]]the [[SUGGESTION_END]]UE sends a request [[SUGGESTION_START]]for [[SUGGESTION_END]]XR rendering to [[SUGGESTION_START]]the [[SUGGESTION_END]]XR Application Platform.
2. [[SUGGESTION_START]]The [[SUGGESTION_END]]XR application platform [[SUGGESTION_START]]decomposes the [[SUGGESTION_END]]UE's request [[SUGGESTION_START]]in[[SUGGESTION_END]]to different tasks and dispatches the render task to [[SUGGESTION_START]]the [[SUGGESTION_END]]Task Management Server through [[SUGGESTION_START]]the [[SUGGESTION_END]]IP network.
3. [[SUGGESTION_START]]The [[SUGGESTION_END]]Task Management Server analyses the render task and plan[[SUGGESTION_START]]s[[SUGGESTION_END]] the computing task based on the capability of [[SUGGESTION_START]]the [[SUGGESTION_END]]computing resources. Then it sends the requests [[SUGGESTION_START]]for the [[SUGGESTION_END]]computing coordination service and XR communication service to [[SUGGESTION_START]]the [[SUGGESTION_END]]6G core network, including information [[SUGGESTION_START]]about the [[SUGGESTION_END]]computing coordination service QoS (e.g. requested computing capabilities, response time) and [[SUGGESTION_START]]the [[SUGGESTION_END]]communication service QoS (e.g. latency).
4. [[SUGGESTION_START]]The [[SUGGESTION_END]]6G core network selects MEC server B and cloud computing center based on the requested QoS [[SUGGESTION_START]]from [[SUGGESTION_END]][[SUGGESTION_START]]the [[SUGGESTION_END]]computing coordination service, and the status information of all enrolled computing resources, and then sends feedback to [[SUGGESTION_START]]the [[SUGGESTION_END]]Task Management Server about the coordination result[[SUGGESTION_START]]s[[SUGGESTION_END]] (e.g. selected computing resources). Meanwhile, [[SUGGESTION_START]]the [[SUGGESTION_END]]6G core network establishes the communication paths between [[SUGGESTION_START]]the [[SUGGESTION_END]]UE and MEC server B and [[SUGGESTION_START]]the [[SUGGESTION_END]]cloud computing center to transmit data for the rendering based on the requested QoS of [[SUGGESTION_START]]the [[SUGGESTION_END]]communication service.
5. [[SUGGESTION_START]]The [[SUGGESTION_END]]Task Management Server distributes the necessary data and associated information to all the selected computing resources via [[SUGGESTION_START]]the [[SUGGESTION_END]]IP network.
6. MEC server B and [[SUGGESTION_START]]the [[SUGGESTION_END]]cloud computing center execute individual rendering sub-tasks, sync-up with [[SUGGESTION_START]]the [[SUGGESTION_END]]Task Management Server about the progress of its sub-tasks and sync-up the status information (e.g. computing workload, congestion status) with [[SUGGESTION_START]]the [[SUGGESTION_END]]6G core network.
7. The 6G core network detects the overload of MEC server B, and informs [[SUGGESTION_START]]the [[SUGGESTION_END]]Task Management Server to replace MEC server B with MEC server C.
8. [[SUGGESTION_START]]The [[SUGGESTION_END]]Task Management Server collects the rendering graphics from MEC server B, MEC server C[[SUGGESTION_START]],[[SUGGESTION_END]] and [[SUGGESTION_START]]the c[[SUGGESTION_END]]loud [[SUGGESTION_START]]computing [[SUGGESTION_END]]center after the sub-tasks are finished and composes the XR image for [[SUGGESTION_START]]the [[SUGGESTION_END]]XR Application Platform. Then [[SUGGESTION_START]]the [[SUGGESTION_END]]XR Application Platform [[SUGGESTION_START]]tran[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]]mits [[SUGGESTION_END]]the rendered XR image to [[SUGGESTION_START]]the [[SUGGESTION_END]]UE.
W.1.4 Post-conditions
The render task is efficiently completed with the collaboration of several edge computing servers under the coordination of [[SUGGESTION_START]]the [[SUGGESTION_END]]6G core network.
The rendered XR image is successfully provided to [[SUGGESTION_START]]the [[SUGGESTION_END]]UE as expected.
W.1.5 Existing features partly or fully covering the use case functionality
None.
W.1.6 Potential New Requirements needed to support the use case
[PR W.1.6-1] The 6G network shall support mechanisms to manage computing resources (i.e. edge server(s) or cloud server(s)). e.g. enrol computing resources, maintain the information [[SUGGESTION_START]]about [[SUGGESTION_END]]computing resources, etc.
Editor's note: It is FFS to refine the wording of examples.
[PR W.1.6-2] The 6G network shall support mechanisms to collect status information (e.g. computing workload, congestion information, available capability, power consumption) of trusted computing resources (i.e. edge server(s) or cloud server(s)) on-demand or periodically.
[PR W.1.6-3] The 6G network shall provide mechanisms to expose to [[SUGGESTION_START]]a [[SUGGESTION_END]]trusted 3rd party the information (e.g. computing capability, the location, allowed service types, status, power consumption) [[SUGGESTION_START]]about [[SUGGESTION_END]]computing resources (i.e. edge server(s) or cloud server(s)).
[PR W.1.6-4] Subject to operator's policy, application needs or both, the 6G network shall support the selection of computing resource(s) (i.e. edge server(s) or cloud server(s)) based on e.g. requested computing capabilities, and support routing of data traffic between a UE and the selected computing resource(s) based on required communication service QoS.
Editor's note: It is FFS to clarify the terminology regarding computing resource and computing node in the requirements.
---------- Forty First Change ----------
W.2.3 Service Flows
1. A third-party requests access to specific GPU or AI resources via an API provided by Operator A.
2. The 6G network verifies the request against defined policies and authorizes access if conditions are met.
3. The 6G network checks with the policy framework, which in turn may confirm with [[SUGGESTION_START]]the [[SUGGESTION_END]]orchestration system if the resources are available, how many of them are used for NF[[SUGGESTION_START]](s)[[SUGGESTION_END]] and if the load is expected to increase based on the history of network usage. Upon approval, the 6G system allocates the requested resources and establishes secure access for the third-party. This unique network-aware resource exposure capability is enabled by the 6G system's centralized policy framework.
4. The operator could also add its own rules and policy to manage the resource allocation with the help of [[SUGGESTION_START]]a [[SUGGESTION_END]]policy framework.
5. The system continuously monitors resource usage and records data for charging and reporting purposes.
6. The 3rd party is also allowed to analyse the usage in real time.
---------- Forty Second Change ----------
W.2.4 Post-conditions
1. Third-party entity completes its tasks using the allocated resources.
2. The 6G network releases the resources and updates the availability status in real-time.
3. Usage data is logged, and relevant charges are generated and sent for billing.
4. [[SUGGESTION_START]]The [[SUGGESTION_END]]Policy function is in control of [[SUGGESTION_START]]the [[SUGGESTION_END]]entire operation.
|
S1-250050.zip
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2026-01-13T17:37:57.798978
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S1-250111
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SA1
|
TSGS1_109_Athens
|
pCR
|
revised
|
6G General
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3GPP TSG SA WG 1 Meeting #109 S1-250111
Athens, Greece, 17-21 February 2025 (revision of S1-25xxxx)
Source: 6G Rapporteurs
pCR Title: pCR on updating Existing features partly or fully covering the use case functionality Template
Draft Spec: -
Agenda item: 8.1
Document for: Approval
Contact: Xiaonan Shi (shixiaonan@chinamobile.com) and Jean Trakinat (jean.trakinat1@t-mobile.com)
Abstract: This pCR proposes an update on template of Existing features partly or fully covering the use case functionality in new use case template.
1. Introduction
In the existing new use case template, there is no unified template for Existing features partly or fully covering the use case functionality.
2. Reason for Change
Following the existing template, in practical, this related in low readability and there’s no alignment throughout the different use cases.
3. Conclusions
It is recommended to update the Existing features partly or fully covering the use case functionality with a unified template.
4. Proposal
It is proposed to agree the following changes to new use case template.
* * * First Change * * * *
---------- Use Case template ----------
x.1 Use case on …
x.1.1 Description
<Describe what the use case intends to achieve.>
x.1.2 Pre-conditions
<List any pre-conditions that need to exist for this use case, preferably as a bulleted list, e.g. UE is registered to the network.>
x.1.3 Service Flows
<Describe the sequence of events that explain what needs to happen, preferably as a numbered list, e.g. 1. User makes a voice call, 2. Called party receives alerting message.>
x.1.4 Post-conditions
<Describe the end result e.g. Called party can decide whether to accept call based on information displayed on UE screen.>
x.1.5 Existing features partly or fully covering the use case functionality
< Highlight existing features in the existing set of normative specifications that partly or fully cover this use case.>
[[SUGGESTION_START]]Specifications and clauses[[SUGGESTION_END]]
[[SUGGESTION_START]]Gap Analysis[[SUGGESTION_END]]
[[SUGGESTION_START]]E[[SUGGESTION_END]][[SUGGESTION_START]]xamples of Existing [[SUGGESTION_END]]
[[SUGGESTION_START]]Requirement[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
x.1.6 Potential New Requirements needed to support the use case
<Provide draft new requirements that are needed to realise the use case, and that are not yet covered in any normative specification.>
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S1-250111.zip
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2026-01-13T17:38:25.958697
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S1-250369
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SA1
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TSGS1_109_Athens
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pCR
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revised
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6G General
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3GPP TSG-SA WG1 Meeting #109 S1-250369
Athens, Greece, 17-21 February 2025 Revision of S1-250050
Source: OTD_US
pCR Title: Editorial Changes to 6G TR
Draft Spec: 3GPP TR 22.870v0.1.1
Agenda item: 8.1.1
Document for: Approval
Contact: selvam@trideaworks.com
Abstract: This contribution provides a detailed set of editorial to the 6G TR to help improve clarity and readability.
---------- First Change ----------
5.2.6 Potential New Requirements needed to support the use case
[PR 5.2.6-1] The 6G system shall provide security protection for communication against potential [[SUGGESTION_START]]CRQC-based [[SUGGESTION_END]]attacks.
---------- Second Change ----------
7.1.1 Description
In large disaster areas, a high degree of coordination for the search and rescue operations is essential. In such situations, sensing can play an important role in providing helpful information to the Public Protection and Disaster Relief (PPDR) authorities and first responders by providing an integrated platform for real-time monitoring and coordination which will support the efficient allocation of resources and the facilitation of decision-making in challenging environments.
Collecting data from a disaster area(s) involves the use of special equipment and devices supporting sensing to capture real-time information about the affected area. Also, the base stations in the disaster areas, or temporarily deployable and tactical base stations, can be used for providing the required sensing services.
The collected environmental and devices sensing data can be used to generate real-time maps for the affected areas allowing [[SUGGESTION_START]]the PPDR authorities [[SUGGESTION_END]]to prioritize and to direct the rescue efforts more efficiently. Furthermore, these maps can be used to monitor the evacuation processes, as well as the situations at the evacuation centres (e.g. to detect overcrowding at an evacuation centre).
---------- Third Change ----------
7.1.2 Pre-conditions
A) After the earthquake struck, a wide disaster area emerged.
Base stations can provide sensing services even after a disaster, or new base stations can be installed to ensure [[SUGGESTION_START]]the [[SUGGESTION_END]][[SUGGESTION_START]]provision of [[SUGGESTION_END]]the required capacity and/or coverage over areas where the Terrestrial Networks (TN) is down.
Rescue teams [[SUGGESTION_START]]are [[SUGGESTION_END]]equipped [[SUGGESTION_START]]with [[SUGGESTION_END]]and [[SUGGESTION_START]]use [[SUGGESTION_END]]special equipment and devices supporting sensing.
B) People are expected to gather at evacuation centres (rescue points).
The data on people flow is needed at evacuation centres to distribute disaster relief supplies.
---------- Fourth Change ----------
7.1.3 Service Flows
A) Coordination of search and rescue:
- Rescue teams start searching using sensing devices.
- Sensing technology is used to analyse the environment, structure, etc. of the disaster areas and generate real-time maps.
- The generated maps will reflect severely affected areas, searched areas, and unsearched areas.
- The rescue command centre can use the real-time map information to optimize the dispatch of rescue teams.
B) People evacuatin[[SUGGESTION_START]]g[[SUGGESTION_END]] and gathering at designated evacuation centres:
- By using sensing technologies, data about people flow can be monitored and provided to rescue teams.
- The rescue teams will provide the necessary relief supplies based on the level of crowding at the evacuation centres and distribute them accordingly.
- In the event of overcrowding at an evacuation centre, additional facilities will be installed (e.g. portable toilets, drinking water points) or a secondary evacuation centre will be set up to [[SUGGESTION_START]]which [[SUGGESTION_END]]people [[SUGGESTION_START]]will be guided[[SUGGESTION_END]].
---------- Fifth Change ----------
7.1.6 Potential New Requirements needed to support the use case
[PR 7.1.6-1] Based on operator policy, regional and/or national regulations, the 6G network in the Earthquake and Tsunami Warning System (ETWS) Notification Area shall provide secure mechanisms of collecting the sensing results with a specified level of accuracy that can be used to generate real-time maps[[SUGGESTION_START]].[[SUGGESTION_END]]
Editor's Note: Definition of KPIs for the above requirements is FFS.
---------- Sixth Change ----------
7.2.1 Description
In a presentation by 5GAA at the 6G Use Case Workshop [7], a positioning accuracy of 1 meter was identified as necessary for Vulnerable Road Users (VRUs). Additionally, their technical report [8] noted that VRUs might carry various devices to enhance pedestrian safety. Global Navigation Satellite System (GNSS)-based positioning alone is insufficient due to issues such as slow convergence, multipath in urban jungles, and susceptibility to jamming or spoofing. Therefore, rapid sensing through communication with roadside base stations and other infrastructure is essential. For instance, if a pedestrian begins moving toward a crosswalk [[SUGGESTION_START]]wh[[SUGGESTION_END]][[SUGGESTION_START]]ile a crossing signal [[SUGGESTION_END]][[SUGGESTION_START]]shows[[SUGGESTION_END]] red light, it is crucial to quickly detect this movement and send a warning to the UE they carry. This situation may occur with typical pedestrians[[SUGGESTION_START]] including [[SUGGESTION_END]]those looking at their smartphones while waiting for the light often mistakenly interpret movements around them as an indication that the signal has changed and start walking. Furthermore, assessing whether a pedestrian can fully cross before the light turns red requires an instantaneous, quantitative understanding of their walking speed. With this data, it becomes possible to determine, based on the road width and remaining green-light time, whether the pedestrian can cross safely.
While speed detection can be achieved through side imaging, using radio waves with high rectilinearity, such as mm-wave, to detect variations of the propagation time or to measure the Doppler shift creates a robust system that does not demand extensive computing power. Although pedestrians are the primary VRU group, the aging population has led to increasingly varied walking speeds, making it impractical to assume a single, typical walking speed. Real-time measurement in each instance is essential. [[SUGGESTION_START]]In [[SUGGESTION_END]]the case of wheelchair users braking can be applied more quickly and reliably without human intervention. However, applying the optimal braking force to prevent forward pitching requires knowledge of the speed before braking, making it particularly important to accurately capture low-speed movements.
---------- Seventh Change ----------
7.2.3 Service Flows
1. Bill, who lives in City B, is now 75 years old. Although he remains active, he can no longer conceal the decline in his mobility. At his family's suggestion, he has signed up for the safety assistance service option with Mobile Operator A.
2. One summer day, after enjoying a conversation with an old friend at his favourite bar, Bill started walking home. However, his thoughts were so absorbed in replaying the conversation that he attempted to cross the street at a red light without realizing it.
3. At that moment, a warning sound emitted from his mobile phone, stopping him in his tracks. A young person standing next to him, who happened to be looking at their smartphone while waiting for the light, also noticed the warning and helped him stop.
4. Just as Bill was thanking the young person, the light changed to green without him realizing. The young person quickly crossed the street, and Bill started crossing as well, but his phone emitted another warning sound[[SUGGESTION_START]] at that moment[[SUGGESTION_END]].
5. This second alert made Bill realize that, at his current walking speed, he would not be able to make it across during the current green light. He [[SUGGESTION_START]]turned around and walked all the way back to where he started after which he [[SUGGESTION_END]]decided to wait until the light turned green again. As soon as it did, he [[SUGGESTION_START]]on[[SUGGESTION_END]][[SUGGESTION_START]]ce again [[SUGGESTION_END]]started crossing, and this time there was no warning sound, allowing him to cross safely.
---------- Eighth Change ----------
7.4.1 Description
With the development of Uncrewed Aerial Vehicles (UAV) technologies, light and small civilian UAVs have played a great role in aerial photography, agriculture, mapping and other fields. Various commercial UAV applications are now becoming a reality. These UAVs typically operate at low altitudes and may produce a series of safety control problems, e.g. UAV illegal intrusion and UAV collision. Thus, how to realize the low-altitude UAV supervision is important and challenging in 6G.
In some scenario[[SUGGESTION_START]]s[[SUGGESTION_END]], these UAVs may follow carefully planned routes that ensure efficient, regulated, and safe operation in designated airspace. To perform tasks like package delivery, surveillance, or environmental monitoring, commercial UAVs operate based on pre-determined flight paths that dictate their altitude, speed, and direction. For instance, a UAV delivering goods will follow a direct route from the dispatch location to the recipient, while a UAV assigned to environmental monitoring will travel from its station to a specific target area for data collection. Route design and optimization are crucial for safe and efficient UAV operations. These flight routes which are approved by UAV operators, prioritize the shortest flight path, avoid restricted airspace, and ensure safe distances from obstacles such as buildings, trees, or other UAVs. Following a strict route minimizes the risk of accidents and enhances the reliability of UAV services.
Although commercial UAVs are equipped with sensors to assist with real-time navigation, these sensors can be affected by environmental conditions like lighting, weather, or geographical obstructions. Such limitations can impair a UAV's ability to accurately determine its position, altitude, or velocity, which may lead to deviations from the approved flight path. Furthermore, for services such as good delivery, where a number of UAVs are involved in a given area, UAV collision might happen due to sensor limitations and lead to safety issues.
The existing UAV tracking technologies, such as ground-based radar systems and dedicated surveillance equipment, provide route monitoring. However, the widespread deployment of these systems faces challenges due to high installation and maintenance costs and limited availability of suitable installation sites.
As illustrated in Figure 7.4.1-1 [9], UEs connected to 6G Radio Access Network (RAN) entities can be configured to support sensing operations. This configuration enhances sensing coverage, provides additional positioning reference points for sensing measurements, and improves the accuracy and reliability of sensing results. These improvements are due to the higher density of UEs compared to base stations, which increases the likelihood that some UEs are positioned closer to the UAV than the 6G RAN entities (for example, with a UAV located between two 6G RAN entities and a UE located directly beneath the UAV). Additionally, certain UEs may be placed in reflection directions that provide a larger radar cross section (RCS) for the UAV, taking into account the UAV's RCS variations in different incident/reflection angles.
The 6G sensing processing unit can gather sensing data from one or multiple network infrastructures. Upon request, the 6G network operator can provide UAV flight trajectory tracking services to trusted third-party applications, such as UAV service operators, regulatory agencies, Uncrewed Aerial System Traffic Management (UTM) systems, UAV itself, etc.
Figure 7.4.1-1: Low-altitude UAV trajectory tracing by 6G system
Thus, [[SUGGESTION_START]]realizing [[SUGGESTION_END]]the low-altitude UAV supervision (e.g. UAV intrusion detection, UAV trajectory tracking) is important and challenging in 6G.
Sensing is an efficient technology for object detection by means of 6G radio signals, e.g. monitoring UAV illegal[[SUGGESTION_START]]ly[[SUGGESTION_END]] flying in a specific area. 6G network could provide sensing service by collecting sensing data, transmitting sensing data, processing sensing data, storing sensing data and support sensing result exposure to the third application platform. These sensing data [[SUGGESTION_START]]have [[SUGGESTION_END]]the following characteristics:
- These data may not necessarily belong to a specific UE while these data may be produced by the relationship between the network and the physical environment.
- These data may be produced by a UE or a base station in order to complete a specific task which needs multi-dimensional cooperation.
- These data may have [[SUGGESTION_START]]a [[SUGGESTION_END]]relationship with time and space which needs efficient on-demand transmission, storage[[SUGGESTION_START]],[[SUGGESTION_END]] and collection.
- These data may be collected from non-3GPP sensing sources which needs unified management in [[SUGGESTION_START]]the [[SUGGESTION_END]]6G network.
[[SUGGESTION_START]]To address [[SUGGESTION_END]]these new data characteristics, the 6G network capability of data processing needs to be extended based on the 5G network including multi-source heterogeneous data collection, efficient and guaranteed large-scale data transmission, efficient data processing within the network, and unified data storage to support multi-node cooperative sensing and multi-node information convergence.
In the low-altitude UAV supervision scenario, the 6G network could be used for sensing the UAV intrusion such as a UAV illegally flying in a restricted area including government and company regions. In this scenario, the network security and data security need to be guaranteed due to the privacy issue. Furthermore, the historical data may be used to identify an illegal UAV. In addition to the communication property of the detected UAV itself, the data management in this low-altitude sensing scenario must be [[SUGGESTION_START]]handled [[SUGGESTION_END]]within the network.
In a word, [[SUGGESTION_START]]the [[SUGGESTION_END]]6G network will break through the "pipeline" capability and go towards to a new type of information service network by realizing diversified data collection, transmission, processing, storage and exposure [[SUGGESTION_START]]by [[SUGGESTION_END]]the core network.
---------- Ninth Change ----------
7.4.2 Pre-conditions
In this use case, a UAV Operator/UTM provides package delivery services within an area covered by a 6G network.
Network operator NN provides 6G sensing service for UAV flight assistance service, including illegal UAV intrusion[[SUGGESTION_START]] detection[[SUGGESTION_END]], UAV flight trajectory tracing, UAV collision prediction[[SUGGESTION_START]],[[SUGGESTION_END]] etc. NN can make use of wireless base station[[SUGGESTION_START]]s[[SUGGESTION_END]] to sense the airspace within their coverage area and report the sensing information (including tracked UAV and the environment around the UAV) to the USS (Uncrewed Aerial System Service Supplier)/UTM.
The Company MM uses the USS/UMT to supervise the low-altitude UAVs and manage potential illegal intrusion into the restricted areas. MM has proved its restricted area information to the USS/UMT.
The USS/UMT uses 6G sensing service provided by the 6G network operator NN to detect potential UAV illegal intrusion and UAV collision prediction.
The UAV Operator/UTM provides specific details to the 6G network operator NN, including the characteristics of the UAV that will be tracked, along with details about the time and location for flight tracing. This information includes regulated flight paths as well as potential areas where the UAV might temporarily deviate from its route.
The 6G network operator NN can realize 6G data collection, 6G data transmission, 6G data processing, 6G data storage within the network, and [[SUGGESTION_START]]provide [[SUGGESTION_END]]sensing results to the USS/UMT/UAV.
---------- Tenth Change ----------
7.4.3 Service Flows
For illegal UAV intrusion:
1. The Company MM requests 6G sensing service for [[SUGGESTION_START]]detection of [[SUGGESTION_END]]illegal UAV intrusion in[[SUGGESTION_START]]to[[SUGGESTION_END]] the restricted area from the USS/UMT.
2. The USS/UMT transmits the request to the 6G Network operator NN.
3. The 6G Network operator NN selects the base stations located in the restricted area to collect and process initial sensing data by collaborative sensing. The selected 6G base station[[SUGGESTION_START]]s[[SUGGESTION_END]] constantly [[SUGGESTION_START]]collect [[SUGGESTION_END]]sensing data of the location of UAVs near the restricted area and sends the sensing data to the 6G core network with a defined frequency to obtain the sensing result (i.e., the distance between the UAV and the border or motion trail).
4. The 6G Network aggregates and transmits the data generated by the base stations and processes the data to obtain the sensing results.
5. The 6G Network exposes the sensing result to the USS/UMIT. The USS/UMIT could trigger [[SUGGESTION_START]]the 6G Network [[SUGGESTION_END]]to send warning messages to the UAV or intercept the illegal UAV directly based on the sensing results.
For UAV flight trajectory tracing:
1. When the scheduled time for tracking begins, the 6G network operator activates the UAV trajectory tracing service within the designated area until the tracking session ends. The UAV operator then launches UAV#1, which takes off from the delivery source and heads toward the destination, following a pre-set flight path.
2. Using radio sensing, a network of 6G base stations and connected devices (UEs) detect UAV#1 and continuously gather data on its position and movement, such as distance, velocity and angle. These metrics, also known as 3GPP sensing data, are sent to a 6G processing unit for real-time analysis.
3. During the flight, if UAV#1 leaves the coverage range of one base station and enters a new coverage zone, the 6G system could let the old base station stop radio sensing, and switch to [[SUGGESTION_START]]a [[SUGGESTION_END]]new base station for sensing UAV#1 until it is out of coverage. This transition is based on the UAV's estimated position and velocity, which the 6G processing unit calculates. The network can automatically adjust the sensing operations at base stations depending on this data or based on a pre-defined time frame. In certain cases, sensing handover may be triggered to maintain continuous coverage. For instance, if the current base station's connection weakens or if another nearby base station can offer better coverage for UAV#1, the system proactively shifts the sensing function to this new station to ensure uninterrupted tracking.
4. The 6G processing unit can aggregate sensing data from multiple sources, including RANs and UEs, to estimate UAV#1's location and velocity. Similar approach can also be applied to UAV#2 - UAV#N, in the case there are multiple UAVs providing service in the area. This real-time information is then transmitted to the UAV operator and/or UTM, who monitors the UAV's trajectory.
5. If UAV#1 - UAV#N deviate from prescribed routes, the UAV operator and/or UTM receives alerts, allowing them to take corrective action and redirect the UAVs as necessary.
---------- Eleventh Change ----------
7.4.4 Post-conditions
For illegal UAV intrusion: The illegal UAVs are [[SUGGESTION_START]]moved [[SUGGESTION_END]]away from the restricted area. Potential privacy risks are avoided. Thanks to the wide-area and constant sensing capability of the 6G base station[[SUGGESTION_START]]s[[SUGGESTION_END]], and the efficient data transmission, processing and storage by the 6G core network, the safety supervision of the low-altitude space of Company 'MM' is improved.
For UAV flight trajectory tracing: UAV#1 follows the tracked flight route to deliver the package to its destination [[SUGGESTION_START]]and [[SUGGESTION_END]]any off-route movements are detected.
For UAV collision prediction: UAV#1 flies efficiently to its destination.
---------- Twelfth Change ----------
7.5.1 Description
Environmental object reconstruction offers significant potential with great societal and business impact, representing opportunities across wide range of sectors, from smart factories, homes[[SUGGESTION_START]],[[SUGGESTION_END]] and transportation to components of/entire smart cities and countries. The 3GPP ISAC provides a non-invasive dual-functionality of communication and sensing data collection of environmental features for reconstruction. This sensing data collection is expected to unlock new services and support enhanced performance, deployment utilization, energy, and spectral efficiencies due to the nature of ISAC and its wide-area coverage and radio-signal availability. For static environmental objects, such as outdoor building/city construction and indoor machinery, 3GPP wireless sensing is effective to provide a wealth of low-cost environmental information for both wide-areas and [[SUGGESTION_START]]more limited[[SUGGESTION_END]] areas. Additionally, for monitoring dynamic targets of interest, such as vehicles, this is essential for enhancing environmental perception, especially in scenarios where non-line-of-sight (NLOS) conditions or poor visibility may limit traditional sensing methods.
The use case of environmental object reconstruction in 3GPP demands finer characterization of surrounding targeted objects based on wireless sensing signal to facilitate industrial innovation. Some applications include:
- Smart Transportation: The impact of autonomous driving is expected to be significant in terms of safety and comfort, and potentially high efficiencies with respect to traffic, logistics, energy, etc. Such application[[SUGGESTION_START]]s[[SUGGESTION_END]] requireadvanced knowledge of moving target detection and its trajectory with detection-to-track association [12], within a multi-object tracking context, including perception of micro-features of the surrounding objects such as micro-Doppler effects, precise classification, and accurate dimensions/orientations, etc. Rough localization of these objects is insufficient for ensuring transport safety and public confidence. Therefore, environmental object reconstruction by the 3GPP wireless sensing [[SUGGESTION_START]]system [[SUGGESTION_END]]is [[SUGGESTION_START]]needed [[SUGGESTION_END]]to provide assistance for interpreting the complex traffic scenario[[SUGGESTION_START]]s[[SUGGESTION_END]] and making rapid decisions.
- Smart City: a smart city can demand survey/digital twinning for a component/system of an entire city, which has motivated a number of smart city initiatives [13] to capture [[SUGGESTION_START]]the [[SUGGESTION_END]]dynamic nature of society. Initially based on cameras, these digital twin models, can be greatly enhanced by environmental object reconstruction enabled by 3GPP wireless sensing[[SUGGESTION_START]].[[SUGGESTION_END]]
- Smart Home: there are increasing interests for innovative applications of smart home, e.g. for fall detection, provided with better sensing[[SUGGESTION_START]],[[SUGGESTION_END]] privacy protection[[SUGGESTION_START]],[[SUGGESTION_END]] and reliability. Environmental object reconstruction at home is expected for finer sensing information of home objects in order to improve service stability for better consumer experience.
Typical environmental objects to be reconstructed can include the following:
Table 7.5.1-1: Representative dimensions of an environment[[SUGGESTION_START]]al[[SUGGESTION_END]] object
Object Type
Dimensions
Building
Approximately ~400 m x ~200 m x ~20 m (L x W x H), static
Vehicle
Truck: 13 m x 2.6 m x 3 m (LxWxH), up to 140 km/h
The 3GPP ISAC is expected to offer significantly higher precision and resolution in sensing, and more detailed characteristics of an environment[[SUGGESTION_START]]al[[SUGGESTION_END]] object in the spatial domain.
---------- Thirteenth Change ----------
7.5.2 Pre-conditions
Map Provider A is a third-party service provider which can render and virtualize detected/tracked surrounding environmental objects within its application, offer real-time 3D virtualization of objects (e.g. via 3D glasses), and display alerts for object-related warnings and information. Examples of alerts include "a car is approaching from the left street corner in 3 seconds".
Good partnership and cooperation are established between Map Provider A and Mobile Operator B in City C. Requested by Map Provider A for sensing service, suitable sensing transmitter and/or sensing receiver deployed in City C are selected by Mobile Operator B to constantly sense environment[[SUGGESTION_START]]al[[SUGGESTION_END]] objects of City C including building[[SUGGESTION_START]]s[[SUGGESTION_END]] and vehicle[[SUGGESTION_START]]s[[SUGGESTION_END]]. The sensing signal emitted from [[SUGGESTION_START]]a [[SUGGESTION_END]]sensing transmitter arrives at an environment[[SUGGESTION_START]]al[[SUGGESTION_END]] object whose micro-objects will reflect/diffract the signal to be detected by selected sensing receivers.
NOTE: For the ease of elaboration, base station or UE is acting as sensing transmitter and/or sensing receiver. Other sensing modes are not excluded and can be useful for environmental object reconstruction.
Charlie is a subscriber of Mobile Operator B and also a subscriber of Map Provider A for real-time 3D virtualisation of his surrounding objects. Charlie would like to navigate unfamiliar city streets while wearing 3D glasses, which display surrounding objects in real-time, for his interest and safety.
---------- Fourteenth Change ----------
7.5.3 Service Flows
Figure 7.5.3-1
1. Charlie is a tourist, who is driving a car to enjoy the view around City C. He would like to navigate unfamiliar city streets while wearing 3D glasses, which display surrounding objects in real-time, for [[SUGGESTION_START]]his [[SUGGESTION_END]]interest and safety. Map Provider A initiates a sensing request to Mobile Operator B for the latest information of environment reconstruction in the vicinity of Charlie, who is the subscriber of both Map Provider A and Mobile Operator B. The sensing objects, required by Map Provider A, include vehicles, city architecture, etc., around Charlie up to a certain range.
2. Mobile Operator B selects and configures the sensing transmitter[[SUGGESTION_START]]s[[SUGGESTION_END]] and sensing receiver[[SUGGESTION_START]]s[[SUGGESTION_END]], and any applicable non-3GPP sensors, to facilitate the sensing operations. Both 3GPP and non-3GPP sensing data will be collected, aggregated, and processed by Mobile Operator B's network in accordance with environment reconstruction requirements.
3. Based on environment reconstruction requirements, Mobile Operator B is expected to preform further sensing operations dedicated to each individual object, to provide fine characteristic[[SUGGESTION_START]]s[[SUGGESTION_END]], in order to determine object type/dimension, monitor micro-motions like car turning, etc. Corresponding sensing operations may require cooperative and dedicated sensing resource allocation across [[SUGGESTION_START]]the [[SUGGESTION_END]]3GPP system for Charlie. Thereafter sensing results of each object are delivered to Map Provider A.
4. The sensing results generated by Mobile Operator B, with more detailed characteristics of environment[[SUGGESTION_START]]al[[SUGGESTION_END]] objects, are exposed to Map Provider A. This data enables Map Provider A to update Charlie's localized map, reflecting changes such as ongoing construction of [[SUGGESTION_START]]the [[SUGGESTION_END]]university campus, shapes/dimensions of targets, and real-time tracking of movement direction/speed etc.
5. Charlie receives a real-time hazard [[SUGGESTION_START]]display [[SUGGESTION_END]](e.g. a car is approaching from the left street corner in 3 seconds) from Map Provider A virtualized in his glass[[SUGGESTION_START]]es[[SUGGESTION_END]], tailored to Charlie's trajectory. This information is transmitted through Mobile Operator B's network, adhering to pre-defined latency requirements.
6. Luckily Charlie has a chance to visit City C again. Mobile Operator B keeps detecting and tracking ongoing renovation by sensing for local university campus [[SUGGESTION_START]]changes [[SUGGESTION_END]]including new classroom buildings. During his visit, such renovation is [[SUGGESTION_START]]nearly [[SUGGESTION_END]]completed. By partnering with Mobile Operator B, Map Provider A can integrate and reconstruct the buildings into the real-time 3D map. Charlie decides to pay a visit because of his interest [[SUGGESTION_START]]in [[SUGGESTION_END]]unique building design.
---------- Fifteenth Change ----------
7.5.5 Existing features partly or fully covering the use case functionality
3GPP TR 22.837 [9] has described use cases to monitor micro doppler effect by ISAC caused by chest rise/fall during sleeping. The sensing results represent [[SUGGESTION_START]]the [[SUGGESTION_END]]human respiration rate.
In this use case, 3GPP ISAC is expected to detect and track more comprehensive characteristics of individual environmental object, e.g. for a building, vehicle, robot, etc., with sufficient and accurate sensing information per object type.
---------- Sixteenth Change ----------
7.6.1 Description
Many transportation and urban applications require real-time and citywide traffic flow estimation, which is the basis for transportation planning and traffic control. Estimated traffic flow is generally represented by the number of cyclists/vehicles/pedestrians passing a reference location per unit of time and can be virtualized by a third-party application.
In order to enable real-time navigation for [[SUGGESTION_START]]automated [[SUGGESTION_END]]driving or traffic flow monitoring, it is necessary to define the upper limit of sensing object detection/tracking required by a 3GPP sensing service, since any 3GPP based sensing detection/tracking coexists with communication services and is not cost-free for operators [[SUGGESTION_START]]to simply share[[SUGGESTION_END]]. 3GPP system shall allocate limited but sufficient spectrum resources, power, base stations and/or roadside units[[SUGGESTION_START]],[[SUGGESTION_END]] etc[[SUGGESTION_START]].[[SUGGESTION_END]] for sensing operations. [[SUGGESTION_START]]This upper limit[[SUGGESTION_END]] can be represented by [[SUGGESTION_START]]a [[SUGGESTION_END]]sensing target density[[SUGGESTION_START]].[[SUGGESTION_END]]
For example, the number of vehicles that needs to be simultaneously detected and tracked at a crossroad may be up to 1000 cars per [km2], including all stationary and moving vehicles occupying that crossroad temporally:
NOTE: It is assumed that the number of lanes for a major crossroad could be 8 for each direction with 3.5 meter lane width, and that the number of vehicles per lane for the purpose of tracking is 5.
---------- Seventeenth Change ----------
7.6.2 Pre-conditions
Good partnership and cooperation are established between Traffic Department A and Mobile Operator B. Traffic Department A subscribes [[SUGGESTION_START]]to [[SUGGESTION_END]]the 3GPP wireless sensing service from Mobile Operator B for the real-time road digitalization.
In order to monitor traffic volume constantly, Mobile Operator B has deployed and activated base stations and roadside units around traffic intensive areas, such as crossroads, urban roads and highways, in order to provide a wide sensing coverage and capability to detect and track all moving objects, including vehicles, bicycles and pedestrians effectively.
NOTE: For the ease of elaboration, [[SUGGESTION_START]]the [[SUGGESTION_END]]base station is acting as sensing transmitter and/or sensing receiver in this case. Other sensing modes can also be feasible and useful as well.
---------- Eighteenth Change ----------
7.6.4 Post-conditions
Thanks to the network-wide coverage, the [[SUGGESTION_START]]base station’s [[SUGGESTION_END]]bird's-eye-view of [[SUGGESTION_START]]the environment[[SUGGESTION_END]], road digitalization is enabled by capturing real-time information of the road environment.
---------- Nineteenth Change ----------
8.1.2 Pre-conditions
Alex is living in a mountainous area with challenging access conditions. He has registered UEs (e.g. smartphone, tablet, computer) for communication and the registered to network can provide the wide coverage with TN and also NTN.
---------- Twentieth Change ----------
8.1.3 Service Flows
1. Alex is located in a mountainous area with difficult access conditions.
2. The network ensures the continuous connectivity between the terrestrial and non-terrestrial networks (TN and NTN).
3. [[SUGGESTION_START]]For consul[[SUGGESTION_END]][[SUGGESTION_START]]t[[SUGGESTION_END]][[SUGGESTION_START]]ation with a doctor, [[SUGGESTION_END]]Alex connects to the [[SUGGESTION_START]]network [[SUGGESTION_END]]using a compatible device (smartphone, tablet, computer), with sufficient video quality to enable the doctor to diagnose common health problems.
4. Throughput and latency requirements are less stringent than for URLLC communications, enabling [[SUGGESTION_START]]a [[SUGGESTION_END]]smooth consultation [[SUGGESTION_START]]call [[SUGGESTION_END]]even under variable network conditions.
5. Advanced security mechanisms are in place to protect data integrity, confidentiality, and availability across the network.
---------- Twenty First Change ----------
8.2.1 Description
Different from terrestrial cellular and GEO deployment, the LEO satellite has a moving coverage which requires a new deployment thinking. A satellite operator may have a launch plan for a large size LEO constellation. However, the deployment of complete LEO constellation is a long-term task which needs several years or even longer. The deployment is both time consuming and costly. According to the target service area, a satellite operator may choose the sequence of satellites[[SUGGESTION_START]] for deployment[[SUGGESTION_END]], which helps to provide the service to the user of target service area as early as possible. The satellite operator can start the commercialization of the constellation once the number of the satellites is sufficient instead of waiting until the whole constellation has been deployed completely. Even with the [[SUGGESTION_START]]partially[[SUGGESTION_END]] deployed constellation, the satellite operator should guarantee the uninterrupted service for the users. To achieve this, each satellite of the constellation should provide larger coverage compared with the case that the LEO satellites are densely deployed.
---------- Twenty Second Change ----------
8.2.2 Pre-conditions
Operator A is a satellite operator with launch plans for a large-size constellation of LEO satellite. Alice has subscribed the UE directly to the satellite service of Operator A and her smartphone is capable [[SUGGESTION_START]]of [[SUGGESTION_END]]directly connect[[SUGGESTION_START]]ing[[SUGGESTION_END]] with Operator A's satellites.
---------- Twenty Third Change ----------
8.2.3 Service Flows
1. Operator A plans to deploy a large-size LEO constellation step by step.
2. Before the deployment of the large-size LEO constellation is complete, Operator A decides to provide connectivity service to users when the sparse constellation with sufficient satellites (e.g. several hundred) for the target service area has been [[SUGGESTION_START]]deployed[[SUGGESTION_END]]. Due to the sparse deployment, each satellite needs to support sufficiently long-rang[[SUGGESTION_START]]e[[SUGGESTION_END]] coverage.
3. Alice is going hiking in a mountain area with her smartphone. She noticed that there is no terrestrial network coverage and her smartphone has switched to the Operator A's satellite network. [[SUGGESTION_START]]During this time[[SUGGESTION_END]], she picks up a call from her mother. She talks with her mother for several minutes. During the call, there is no interruption since the satellites provide the continuous coverage.
4. After walking for a long time, Alice takes a break[[SUGGESTION_START]] and[[SUGGESTION_END]] she browses the web to search [[SUGGESTION_START]]for [[SUGGESTION_END]]some information on the hiking route using her smartphone. There is also no interruption during the internet access. After the break, Alice continues hiking.
---------- Twenty Third Change ----------
8.2.4 Post-conditions
Thanks to the support of [[SUGGESTION_START]]satellite communication [[SUGGESTION_END]]service using LEO satellites access with spar[[SUGGESTION_START]]s[[SUGGESTION_END]]e satellite deployment, Operator A can start the commercial usage of the constellation even though the constellation is sparse, while the users can experience [[SUGGESTION_START]]good communications via [[SUGGESTION_END]] satellite in the constellation's early deployment stage.
---------- Twenty Fourth Change ----------
8.3.1 Description
Reelika has just started a famous ultra-trail "Grand Raid des Pyrénées" in the Pyrénées mountains between France and Spain. She is equipped with wearable mobile devices including a wrist watch, a forehead mounted camera and a smart phone in order to share in real time her position, her health conditions and possibly voice message or short videos with her friends Guillaume and Didier in Paris but also with her coach Loïc waiting for her at a check point in the middle of the race in a remote area without mobile access coverage. Reelika expects that her personal data (e.g. position, health conditions, etc.) are protected.
As she starts, her mobile device is first served by the mobile access but then when climbing the first mountain, she reaches the edge of the mobile access coverage. Throughout the race, her wearable devices are transferred between mobile and satellite access whenever the mobile access coverage disappears.
Editor's note: consistency of terms – mobile access coverage = TN access?
Her friends do not perceive the transition between both access technologies (i.e. no packet loss [[SUGGESTION_START]]for [[SUGGESTION_END]]non real time service and no interruption for real time service). However, they may perceive that the quality of the image/voice is adapted to varying available bandwidth and latency.
The race organisers as well as all her friends including Loïc, [[SUGGESTION_START]]regularly [[SUGGESTION_END]]keep receiving [[SUGGESTION_START]]Reelika’s [[SUGGESTION_END]]position and health conditions. At some point Loïc detects that Reelika is getting very tired and cannot progress. He decides to send her a short message to cheer her up. Reelika’s wearable mobile device receives the text message and convert[[SUGGESTION_START]]s[[SUGGESTION_END]] it to voice. Reelika listens to the message and thanks him with a video message.
During the night, Reelika initiates an emergency call. Reelika reports to the race organisers that she is with a runner who injured himself while accidentally falling in a pit. The race organisers detect that Reelika may have crossed the Spanish border and launches a request for reliable positioning service in order to determine whether the French or the Spanish public safety organisation should intervene. Reelika's reported position is confirmed to be in Spain and hence the Spanish public safety organisation is alerted who takes over Reelika[[SUGGESTION_START]]’s[[SUGGESTION_END]] call. The on-going emergency call is extended to a Spanish first responder who fortunately is not far from the accident. He is able to reach the spot within a quarter of an hour, thanks to complementary guidance from Reelika.
Editor's note: consistency of terms – emergency call to third party and multi-party emergency call in Service Flow clause?
Reelika is able to continue her race leaving the injured runner in good hands.
As Loïc is monitoring the progress of Reelika, it starts raining and Loïc decides to enter the large shelter where the runners will find some assistance. The rain is so dense that the race organisers decide in coordination with the public safety to alert the runners and all the followers along the track about possible slippage of terrain. An alert message is broadcast to the area with the intent to reach the maximum number of persons, including the ones in indoor conditions. With this alert message, public safety requests all runners to report their position every 10 minutes during the next two hours. Loïc (indoor), Reelika and all race participants/followers in the area receive this alert message.
At a certain point in the race, Reelika sets off on a bad track, due to the de-tagging of local activists. The monitoring system of the race organizer quickly detects that she is going off the planned track. She is immediately alerted with an alert message that triggers an alarm sound in her wrist watch. She consults her wearable device [[SUGGESTION_START]]which shows [[SUGGESTION_END]]where she lost the planned track and how she can rejoin it.
The race organizers inform the race participants behind Reelika about the virtual track to rejoin the planned track as well as [[SUGGESTION_START]]the [[SUGGESTION_END]]field member of the race organization to correct the track. This prevents late runners to lose time with this track issue and no one need[[SUGGESTION_START]]s[[SUGGESTION_END]] to be sent after lost runners.
Fortunately, Reelika can successfully complete her race without further technical incident and all her friends join in a video conference to celebrate with her.
---------- Twenty Fifth Change ----------
8.3.6 Potential New Requirements needed to support the use case
[PR 8.3.6-1]: The 6G system shall be able to ensure service continuity with minimum interruption for UE[[SUGGESTION_START]]s[[SUGGESTION_END]] during the transition between terrestrial and satellite access and vice versa.
[PR 8.3.6-2]: The 6G system via its satellite access, shall support SMS delivery to a high density of UE[[SUGGESTION_START]]s[[SUGGESTION_END]] i.e. up to [1000] UE[[SUGGESTION_START]]s[[SUGGESTION_END]] per km2.
[PR 8.3.6-3]: Subject to regulatory requirements, the 6G system using satellite access, shall be able to support PWS for broadcasting warning notifications to UE[[SUGGESTION_START]]s[[SUGGESTION_END]] in adverse propagation conditions e.g. light indoor conditions, dense forest.
---------- Twenty Sixth Change ----------
8.5.1 Description
The national meteorological centre has detected [[SUGGESTION_START]]an [[SUGGESTION_END]]upcoming [[SUGGESTION_START]]major[[SUGGESTION_END]] storm with heavy rains which are likely going to cause a major flood in the Valencia region. The alert is propagated to the public safety organisations as well as to the population.
All first responders are equipped with a set of wearable devices (i.e. handheld, bodycam, vital signs monitoring sensors, …) and some with drones with regular and thermal (infrared) cameras.
The storm and floods [[SUGGESTION_START]]caused [[SUGGESTION_END]]power supply cuts in the region that resulted in the loss of terrestrial cellular network connectivity. Thanks to the available satellite access, the national public safety organisation is guiding first responders that are already on site (most are volunteers among the population). Additional first responder teams are deployed[[SUGGESTION_START]],[[SUGGESTION_END]] each with their all-terrain vehicles or amphibious vehicles in the harbour. The remaining bandwidth of the satellite access can be used by the population to exchange messages.
Quickly, it is decided to ask assistance from public safety and disaster relief organisations from neighbouring countries.
Local access networks are mounted on each land vehicle with enabled satellite connectivity. Each local access network can be used for communication between the team members, with their headquarters (area, regional, national) as well as with other responder teams. Even civilians could exploit this connectivity if sufficient remaining bandwidth is available. When the wind burst [[SUGGESTION_START]]subsided,[[SUGGESTION_END]] drones are used by team members to assess the disaster, report to headquarter(s) and rescue the population.
As the flood took place in the coastal area, casualties are being spread to the sea and therefore, public safety organisation[[SUGGESTION_START]]s[[SUGGESTION_END]] are deploying boats[[SUGGESTION_START]],[[SUGGESTION_END]] each equipped with an on-board local access network and satellite connectivity.
Responders or drones may have to move beyond the coverage of a local access network. In such cases[[SUGGESTION_START]],[[SUGGESTION_END]] continuity of service is ensured through smooth transition to the satellite access. Several local access networks can be directly connected via satellite to ease the coordination between the teams. User equipment belonging to national first responders can seamlessly communicate with user equipment appertaining to responders of the neighbouring countries.
During the recovery phase, an HIBS (base station on board a HAPS) can be launched to increase the available capacity over the area before the terrestrial base stations are repaired.
---------- Twenty Seventh Change ----------
8.5.3 Service Flows
Given that the terrestrial network is down, all UEs will look for [[SUGGESTION_START]]an [[SUGGESTION_END]]alternative available network in the area, that is the satellite access and later the HAPS based access network.
Satellite or HAPS based access network can be used to support:
- Public warning service to [[SUGGESTION_START]]the entire [[SUGGESTION_END]]population (including 1st responders [[SUGGESTION_START]]and [[SUGGESTION_END]]volunteers) in the impacted area (also in adverse propagation conditions such as light indoor)
- Non real time and real time services to pedestrian[[SUGGESTION_START]]s[[SUGGESTION_END]] or drone mounted UE[[SUGGESTION_START]]s[[SUGGESTION_END]]
- Backhaul connectivity to vehicle/boat mounted local access point
- Connectivity (without usage of satellite feeder link) between two local access points
The satellite or HAPS based access bandwidth can be pre-empted for public safety organisations but the remaining bandwidth if available may be used by the population only for messaging.
Vehicle/boat mounted local access points can be used
- To serve pedestrian or drone mounted UE[[SUGGESTION_START]]s[[SUGGESTION_END]]
- To support UE to UE connectivity
---------- Twenty Eighth Change ----------
8.6.3 Service Flows
1. At some point, the IoT or low-power device may lose its original positioning sources due to one of the following reasons:
- GNSS-originated position: GNSS signal may be lost due to interference, or obstruction, or intentionally disabled to augment its autonomy.
- Terrestrial network-originated position: the IoT or low-power device may have moved beyond terrestrial network coverage, such as in the case of asset tracking sensor installed on a container to be transport[[SUGGESTION_START]]ed[[SUGGESTION_END]] by sea, rail, or road.
In this context, the IoT or low-power device, served by the satellite access, is expected to know an approximate position from prior positioning. This requires the IoT or low-power device to refresh a more accurate position.
Therefore, the IoT or low-power UE initiates the 3GPP positioning method over satellite to estimate its current location.
The 6G system broadcasts to the UE via its satellite access at least the following necessary information, including but not limited to:
- Network assistance data: Satellite ephemeris and additional assistance data to improve accuracy (e.g. ionospheric models to correct the atmospheric delay errors, etc.)
- Reference signal for time of arrival measurements.
---------- Twenty Ninth Change ----------
8.7.1 Description
Many places and people are currently underserved when it comes to Mobile Broadband (MBB) services. From the user's and society's point of view a lot is gained already with a basic internet connection, since many internet services can be delivered with a fairly moderate bitrate, and the most important needs would be met already with a low activity factor. Therefore, the problem is mainly related to providing remote service coverage for basic MBB. Still, such basic services may be the basis of sensitive systems (e.g. related to health or surveillance) and therefore uninterrupted and resilient operation is important.
The use case Global [[SUGGESTION_START]]Mobile [[SUGGESTION_END]][[SUGGESTION_START]]Video [[SUGGESTION_END]]is about provisioning access to basic broadband services, exemplified by the capability to make a video call, at remote places on earth where people live or work, using handheld-type of 3GPP devices. A full global area coverage (e.g. 99.9 %) needs to be provided through NTN access. However, TN needs to handle more populated areas, for instance through very large cells, such that the total traffic to be handled by NTN is not too high.
Based on population density this can be divided into remote and deep rural scenarios.
1) Remote scenario
[[SUGGESTION_START]]This case c[[SUGGESTION_END]]over[[SUGGESTION_START]]s[[SUGGESTION_END]] virtually all people on earth, and virtually all areas including oceans. [[SUGGESTION_START]]Most [[SUGGESTION_END]]cases [[SUGGESTION_START]]involve [[SUGGESTION_END]]very sparsely populated areas (approx. < 1 person/km2). The expectation is that this scenario is covered mainly by NTN access. A lower rate, activity factor, and area traffic can be supported in these areas. The Global [[SUGGESTION_START]]Mobile [[SUGGESTION_END]]Video service might be delivered with reduced quality compared to the Deep Rural scenario.
2) Deep Rural scenario
A deep rural scenario represents sparsely populated areas (approx. 1-10 persons/km2). The expectation is that this scenario is mainly covered by TN access, from large macro cells to very large "boomer" cells, and in addition have NTN coverage to fill gaps between TN cells. A higher rate, activity factor, and area traffic can be supported in these areas.
Key value impact analysis:
Energy resources: Increased energy consumption due to the buildout of networks.
Material resources: The material need increases when building out networks with new 6G sites and satellites. Material resource depletion increases as materials sent into space is considered not recyclable.
Emissions: Internet access provides possibility for various activities (e.g. bank, hospital, work) leading to less travel needs and thereby reduced emissions. Emissions related to producing and operating new equipment can be somewhat mitigated.
Biodiversity and land use: New sites, especially high tower 6G sites, increases the land use for ICT.
Education: Internet access enable access to remote educational material.
Health: Global internet could enable using remote healthcare/first aid leading to better healthcare access in rural areas.
Inclusion and Equality: Providing the possibility [[SUGGESTION_START]]of [[SUGGESTION_END]]internet access facilitating banking, healthcare and education services is positive from an inclusion point of view. However, it is important not to leave anyone behind and thereby risk widening the gap.
Trustworthiness: Increased resilience for ICT services could be provided by satellites included in the use case as back up in unforeseeable situations.
Work and income: This use case could enable the possibility of running businesses from everywhere.
Infrastructure: Increasing access to [[SUGGESTION_START]]the [[SUGGESTION_END]]internet is the main goal of the use case.
---------- Thirtieth Change ----------
8.7.3 Service Flows
1. User initiates or receives video call to handheld device to/from another user.
2. If TN access [[SUGGESTION_START]]is [[SUGGESTION_END]]available the traffic is supported by TN, otherwise NTN access is used.
3. The call is sustained uninterrupted, also under mobility, until either user terminates [[SUGGESTION_START]]the [[SUGGESTION_END]]call.
---------- Thirty First Change ----------
9.2.1 Description
Usually, the limited image rendering capability of mobile devices poses challenges for services that require high-quality image processing capability like high order super-resolution and de-noising algorithm, as well as high-quality rendering capability like ray tracing effect for gaming. As shown in Figure 9.2.1-1, the GPU computing power for image rendering increasing in last ten years and the predication up to 2030 for mobile phone platform as well as the computing power required for advanced services has been summarized as below based on [16], [17] and [18]:
Figure 9.2.1-1: increasing GPU computing power for mobile platform
It can be observed that with the failure of Moore's law, the growth of mobile platform computing power gradually slows down. Offloading rendering task to network/cloud is a promising trend to support advanced multi-media services in the future. However, the latency for offloading rendering task to network/cloud is quite difficult to be always guaranteed in anytime/anywhere due to the varied radio channel conditions and network load/coverage. UE-Network-Cloud synergized multi-media operation is a collaboration mode which allows mobile devices to dynamically offload part of rendering task to the cloud to improve the user experience based on communication link status and maintains deterministic rendering task processing latency by local backup processing in case communication link is not good as shown in Figure 9.2.1-2. There's an expectation for 3GPP systems to consider such dynamic rendering offload framework to adapt to the synergized multi-media service processing trend.
Figure 9.2.1-2: UE-Network-Cloud synergized multi-media operation framework
The rendering task split function is a function provided by the UE operating system (OS) to map the rendering task request from the application (APP) to local or remote rendering resources. Upon [[SUGGESTION_START]]determin[[SUGGESTION_END]][[SUGGESTION_START]]in[[SUGGESTION_END]][[SUGGESTION_START]]g that [[SUGGESTION_END]]a rendering task is requested to be executed, the task split function can select the local and/or the remote rendering resources based on relevant factors, such as required processing quality, radio link status, [[SUGGESTION_START]]and [[SUGGESTION_END]]estimated latency, to ensure [[SUGGESTION_START]]a [[SUGGESTION_END]]deterministic user experience for the requested multi-media service. Typical services which can benefit from UE-Network-Cloud synergized multi-media operation include photo enhancement and ray tracing for gaming and so on.
The UE-Network-Cloud synergized operation raises several new issues, as follows:
- Uplink data rate: The rendering task offloading operation usually requires [[SUGGESTION_START]]a [[SUGGESTION_END]]high uplink data rate to achieve higher service availability, considering the metadata to be uploaded for the rendering task is usually large with tight latency, e.g. 5 Mbits/50 ms, 150 Mbits/1.5 s (see detail in service flow part). However, the current network typically cannot guarantee sufficient uplink data rate especially at cell edge.
- Real time communication link capability awareness: The rendering task split function might not know the exact communication link capability such as data rate, latency, and UE power consumption for data transmission, therefore it is hard for the UE to make the split decision.
- Computing task level PDB requirement guarantee: The uplink metadata burst for a rendering task offloading arrives in a random way, and the latency requirement is intended for the whole data burst (i.e. the metadata of the task) rather than an individual packet. As shown in Figure 9.2.1-3, the current GBR QoS flow framework guarantees the data rate in a fixed averaging window and fulfils latency in a per packet basis which is suitable for the periodic traffic. For the burst data carrying the metadata for rendering tasks, the latency and data rate should be guaranteed toward the whole data set rather than a specific packet. Therefore, the fixed averaging window as for periodic traffic is not suitable for bursty traffic. There is no suitable QoS type to guarantee the deterministic transmission latency for unpredictable uplink bu[[SUGGESTION_START]]r[[SUGGESTION_END]]sty data traffic.
Figure 9.2.1-3: Burst latency requirement
---------- Thirty Second Change ----------
9.2.2 Pre-conditions
1. Rendering resources are deployed in the cloud.
2. The UE operatin[[SUGGESTION_START]]g[[SUGGESTION_END]] system supports map[[SUGGESTION_START]]ping[[SUGGESTION_END]] the rendering task from the APP to the local (in the mobile phone) and/or the remote (in the cloud) rendering resources.
---------- Thirty Third Change ----------
9.2.3 Service Flows
Synergized photo enhancement case:
1. User A is in a [[SUGGESTION_START]]tourist [[SUGGESTION_END]]attraction, taking photos with his mobile phone. Once he clicks the photo button, the phone sen[[SUGGESTION_START]]s[[SUGGESTION_END]]or produces raw data of the photo, including up to 6 frames (4K per frame) of raw files spanning 300 ms for HDR reconstruction. Raw files retain the original uncompressed image data from the camera sensor, which is usually larger than compressed image formats such as JPEG and PNG, and has unique advantages for [[SUGGESTION_START]]image [[SUGGESTION_END]]processing in practice.
NOTE 1: 2 to 10 frames are needed for HDR reconstruction based on [19]; 6 frames are assumed in this use case.
2. Upon reception of the rendering task request from [[SUGGESTION_START]]the [[SUGGESTION_END]]camera APP for photo enhancement, the rendering task split function inside the UE OS determines whether or not to upload the raw data to the cloud for more powerful post-processing to enhance the photo quality, for which some factors would be considered such as potential delay and UE power consumption for data transmission.
3. If the rendering task split function decides to upload the photo to the cloud, the raw data for the photo is compressed to around 150 Mb (based on the assumption of 6 frames × 4K raw data and compressed ratio assumption in [20] [21]) and uploaded to the cloud within around 1.5 s and the processed photo [[SUGGESTION_START]]is downloaded [[SUGGESTION_END]]from the cloud. The UE can reduce the number of frames for uploading based on communication link capability. Therefore, the required uplink data rate in radio layer would be 150 Mb/1.5 s=100 Mbps.
NOTE 2: E2E latency of 3 s is assumed based on users' patience statistics as shown in [22], where 1.5 s is allocated for uploading to the cloud while 1.5 s for processing in the cloud and downloading to the UE.
4. If the rendering task split function decides not to upload the photo to the cloud, the local post-processing will be performed.
5. When the user A views the photos, the downloaded photo or the local enhanced photo can be shown to the user.
Synergized gaming enhancement case:
1. User A starts a gaming application, the gaming APP produces the scene metadata, including 3D objects, lighting data, and materials, user actions, etc. The typical data size after compression can be 5-20 Mb (assuming middle to high complexity 3D model including 0.35 to 2 million vertexes (200 bits per vertex) 3D models and compression ratio assumption for 3D model in [23]).
2. The gaming APP submits the metadata to [[SUGGESTION_START]]the [[SUGGESTION_END]]rendering task split function inside the UE OS for rendering[[SUGGESTION_START]].[[SUGGESTION_END]] [[SUGGESTION_START]]The [[SUGGESTION_END]]rendering task split function can seek to utilize the more powerful computing in the cloud for higher quality such as ray tracing effect. The rendering task split function determines how to split the metadata to the cloud by considering some factors such as potential delay and UE power consumption for data transmission. For example, the UE selects to offload the slow changed scene background rendering to the cloud while performing rendering for fast moving objects using local rendering resources.
3. The UE uploads the metadata allocated for the cloud rendering within 50 ms [24] and download[[SUGGESTION_START]]s[[SUGGESTION_END]] the rendering result.
4. The UE combines the local rendered result and cloud rendered result to user A.
5. Once the radio condition goes worse, the full local rendering can be activated to maintain [[SUGGESTION_START]]the [[SUGGESTION_END]]user experience.
---------- Thirty Fourth Change ----------
9.2.5 Existing features partly or fully covering the use case functionality
The split rendering architectures defined in TR 26.928 [50] and TS 26.565 [51], and the corresponding QoS requirements in TS 22.261 [14], allow a UE to offload rendering requirement[[SUGGESTION_START]]s[[SUGGESTION_END]] to the cloud for XR rendering. These architectures assume that the metadata including 3D models of the gaming and APP logic is pre-installed in the cloud, therefore only user [[SUGGESTION_START]]provided [[SUGGESTION_END]]information is needed to be transferred in the uplink and there is no dynamic offload decision on whether to render a specific scene in the cloud or [[SUGGESTION_START]]via [[SUGGESTION_END]]UE local resources (i.e. fully rely on the rendering result in the cloud). Therefore, the uplink requirement and dynamic offload decision requirement are not covered by the existing split rendering use cases and part of the downlink requirements on rendering result delivery are covered by the existing split rendering use cases.
It is worth noticing that Edge Enabler Client (EEC) is defined in TS 23.558 [52], which provides supporting functions needed for Application Clients. There is some similarity between EEC and the "rendering task split function" described in this use case.
---------- Thirty Fifth Change ----------
9.3.1 Description
There is a growing demand [[SUGGESTION_START]]to support [[SUGGESTION_END]]people [[SUGGESTION_START]]who [[SUGGESTION_END]]use diverse types of devices other than smartphones, which connect to mobile network system [29]. Then, in 6G, a [[SUGGESTION_START]]variety of[[SUGGESTION_END]] devices are expected to be connected to [[SUGGESTION_START]]the [[SUGGESTION_END]]6G system. With [[SUGGESTION_START]]this [[SUGGESTION_END]]trend, wearable devices are expected to be more popular devices for people, but even with such devices like XR devices, users would like to experience immersive applications which require much computing capability for processing application data. However, due to limited computing capability, [[SUGGESTION_START]]the [[SUGGESTION_END]]user experience could be affected. Therefore, there will be strong need for such devices to be able to offload application data processing to the edge/cloud server.
In current computing technology, application data processing can be offloaded to edge/cloud server [[SUGGESTION_START]]wh[[SUGGESTION_END]][[SUGGESTION_START]]ich are[[SUGGESTION_END]] completely separated from [[SUGGESTION_START]]the [[SUGGESTION_END]]mobile network system. However, from [[SUGGESTION_START]]a [[SUGGESTION_END]]user experience point of view, [[SUGGESTION_START]]the [[SUGGESTION_END]]6G system shall support network and/or device control based on computing offload use of user devices.
This use case aims to [[SUGGESTION_START]]describe [[SUGGESTION_END]]a service scenario [[SUGGESTION_START]]where [[SUGGESTION_END]]a user wants a computing offload service supported by the network and [[SUGGESTION_START]]the corresponding [[SUGGESTION_END]]requirements for [[SUGGESTION_START]]the [[SUGGESTION_END]]6G system.
---------- Thirty Sixth Change ----------
11.1.1 Description
Urban air mobility (UAM) is a new safe, secure and more sustainable air transportation system for passengers and cargo in urban environments, enabled by new technologies and integrated into multimodal transportation systems. The transportation is performed by electric vertical take-off and landing (eVTOL) aircrafts, remotely piloted or with a pilot onboard [32]. In February 2024 an eVTOL named as "PROSPERITY" [33] that can contain 5 passengers conducted a Shenzhen-Zhuhai test flight, which was a cross-sea and cross-city route. KT showed, at the MWC2024, their UAM Skypath solution, which can provide 5G service for UAM aircrafts flying at 300 – 600 meters altitude.
Compared with the common UAVs (Uncrewed Aerial Aircraft), the UAM aircrafts have the following major differences:
- Large size and heavy weight, higher AGL (above ground level)
Low-altitude airspace usually refers to the airspace with a vertical distance of less than 1000 m from the ground, and it can be extended to less than 3000 m according to the characteristics and actual needs of different regions [34]. The AGL of UAM aircrafts can be up to 1000 m, while the AGL of small UAVs is less than 300m. Communication for the UAM aircrafts with higher AGL need to be considered.
- Higher reliability and safety requirement with human beings onboard
UAM aircrafts share some common low altitude airspace. As predicted by Professor Shen, there will be about 100,000 UAVs flying in Shenzhen's sky at the same time in the future [36]. Considering the area of Shenzhen is 1997 km2, there will be about 50 UAVs flying in 1 km2 airspace. To ensure high reliability and safety, the UAM aircrafts must be aware of the object information that [[SUGGESTION_START]]are [[SUGGESTION_END]]near its flight trajectory.
To guarantee the safety of aircrafts, Detect and Avoid (DAA) technology is widely used in UAVs by using a combination of sensors, cameras, and radar to continuously monitor the UAV's surroundings [38]. These sensors detect obstacles, other aircraft, and potential hazards in the flight path. The system then processes this information in real-time and adjusts the UAV's flight path to avoid collisions. However, the capabilities of the sensors on UAVs are not able to sense the blockage far away. Since the UAM aircrafts carry human beings on board, UAV's DAA system is not adequate to ensure the safety of passengers onboard. By also utilizing the 3GPP sensing service, the safety of UAM aircrafts flying in the common airspace can be guaranteed, as well as the running efficiency of UAM aircraft can be improved so as to transport more passengers or goods.
Typically, the message size for sensing one object could be 1 Kbyte [37], including information of size/position/speed/direction. Around 25 objects (25 kKbyte) per frame (20 ms) need to be sensed for an aircraft. The reliability requirement for UAM aircrafts is about 99.9 %.
- In addition, with passengers onboard the human communication on board of [[SUGGESTION_START]]the [[SUGGESTION_END]]UAM has to be considered.
---------- Thirty Seventh Change ----------
11.1.3 Service Flows
1. May and Fei plan [[SUGGESTION_START]]to [[SUGGESTION_END]]visit City B from City A by UAM, an aircraft [[SUGGESTION_START]]which [[SUGGESTION_END]]can contain up to 4 passengers.
2. To guarantee the safety of the passengers onboard, the UAM aircraft requests the sensing service from the 6G network. The base station(s) along the flight path will sense the environment information especially other aircrafts within its interesting area, e.g. the interesting area is an airspace with the size of 1 square kilometre and height from 0 to 1000 meters.
3. As part of the sensing service, the 6G network sends the sensing and/or the warning information to the UAM aircraft.
4. Upon receipt of the above information, the UAM aircraft further processes the information to identify [[SUGGESTION_START]]an[[SUGGESTION_END]][[SUGGESTION_START]]y [[SUGGESTION_END]]collision threat[[SUGGESTION_START]]s[[SUGGESTION_END]]. If there are some blockages threatening flight safety, the UAM aircraft will perform collision avoidance in advance.
5. Meanwhile passengers onboard either watch HD video or surf the Internet using their smartphone. The view is very beautiful during the flight, May is very happy to share the scenery with her friends through 4K real-time video on social media by smartphone. Fei is not interested in the scenery during the flight, and he is enjoying a football match live broadcast. The core network sends data of [[SUGGESTION_START]]a [[SUGGESTION_END]]football game with 4K or 8K live broadcast to base station, then Fei can receive the football game data by his smartphone.
---------- Thirty Eighth Change ----------
11.1.6 Potential New Requirements needed to support the use case
[PR 11.1.6-1] The 6G System shall support the transmission of sensing result information to the UAM aircrafts with the following KPI requirements.
Typical transmission interval
Altitude AGL
Typical message Size
End to end Latency
Reliability
Sensing result to a UAM aircraft
[20 ms]
up to 1000 m
[25 kbyte]
[20 ms]
[99.9 %]
NOTE: Typically, the message size for sensing one object is 1 kbyte [x6]. It is assumed that around 25 objects (25 kbyte) per frame (20 ms) are sensed for an aircraft. The reliability requirement for UAM aircrafts is about 99.9 %.
[PR 11.1.6-2] The 6G System shall support services provided to the UAM applications with the following KPI requirements.
Services
Data rate
End to end Latency
Altitude AGL
Service area
8K video live broadcast
100 Mbps
UAM aircraft originated
(note)
200 ms
(note)
up to 1000 m
Urban, scenic area
600 Kbps
UAM aircraft terminated
(note)
20 ms
(note)
up to 1000 m
Video streaming
4 Mbps for 720p video
9 Mbps for 1080p video
UAM aircraft originated
(note)
100 ms
(note)
up to 1000 m
Urban, rural area
100 Mbps for 8K video
UAM aircraft originated
(note)
100 ms
(note)
up to 1000 m
NOTE: These values are aligned with the KPIs for services provided to the UAV applications in TS 22.261 [14], table 7.1-1.
---------- Thirty Ninth Change ----------
W. 1 Use case on coordinating computing and communication for XR rendering
W.1.1 Description
The exploration of ultimate user experience in consumer products, and the digital and intelligent transformation in the industrial fields have promoted the wide use of computing-intensive applications such as AR/XR, cyber-physical systems, industry robots and etc. However, the balance between computing capabilities and the size and cost of the devices raises a big challenge for the deployment of entire applications. The ITU-R report [39] points out that several emerging technologies are being envisioned to address the challenges. One trend is to process data at the network edge close to the data source for real-time response, low data transport costs and energy efficiency by edge computing technologies, while another trend is to scale out device computing capability beyond its physical limitations by splitting computing workload over reachable computing resources. With the development of each technology trend, the independent control, management and orchestration of communication and the computing resources has been identified [[SUGGESTION_START]]as causing a[[SUGGESTION_END]] significant negative impact on the performance of the end-to-end solution.
The 6G system is expected to change the situation by coordinating the communication services and computing resource utilization in the stage of system architecture design. In this way, the 6G network can make the ubiquitous single-point computing resource connected and participate in the scheduling of various types of computing resources such as UE, MEC server, cloud server. For example, the 6G network can select appropriate computing resource[[SUGGESTION_START]]s[[SUGGESTION_END]] based on service characteristics to achieve optimal resource usage. On the other hand, the data transmission for the computation can be more flexible and efficient with additional information (e.g. workload, available capability) about the selected computing resources.
The real-time rendering of 3D scenes in [[SUGGESTION_START]]a [[SUGGESTION_END]]large-scale XR application is a typical case of [[SUGGESTION_START]]a [[SUGGESTION_END]]computing-intensive application, which requires [[SUGGESTION_START]]a [[SUGGESTION_END]]large amount of calculation to handle the complicated model and texture mapping. Sometime[[SUGGESTION_START]]s[[SUGGESTION_END]] limited [[SUGGESTION_START]]by [[SUGGESTION_END]]the capability of [[SUGGESTION_START]]the [[SUGGESTION_END]]hardware, it is impossible for a single device to render the scene individually[[SUGGESTION_START]].[[SUGGESTION_END]] [[SUGGESTION_START]]The [[SUGGESTION_END]]distribution of the computing workload to other computing capable nodes can solve the issue. Sometimes the duplicated rendering of the same XR scene which is being requested by multiple users can be avoided if [[SUGGESTION_START]]they [[SUGGESTION_END]]choose to execute the rendering in the cloud server. However, [[SUGGESTION_START]]as [[SUGGESTION_END]]the XR application can have little information on the status of computing resources[[SUGGESTION_START]],[[SUGGESTION_END]] it is difficult for the planned data transmission and computation to work together efficiently.
This use case illustrates how the 6G system enables better user experience of split XR rendering via the coordination of computing resources and communication resources.
Figure W.1.1-1: Coordinating Computing and Communication for XR rendering
W.1.2 Pre-conditions
Several computing resource nodes (e.g. MEC servers, cloud computing center) for XR services are deployed distributed in different locations and have been enrolled in [[SUGGESTION_START]]the [[SUGGESTION_END]]6G core network. MEC server[[SUGGESTION_START]]s[[SUGGESTION_END]] A, B and C are provisioned as a part of rendering pipelines to execute render engine, engine adaption, rendering acceleration for the rendering task. [[SUGGESTION_START]]The c[[SUGGESTION_END]]loud computing center is capable of all the functions for XR rendering.
XR Application Platform provides the render services to various XR applications.
Task Management Server is responsible for task analysis, sub-task planning, graphic composition and etc.
The UE supporting [[SUGGESTION_START]]the [[SUGGESTION_END]]XR application is the subscriber of the 6G network. It has registered to [[SUGGESTION_START]]the [[SUGGESTION_END]]6G network and been authorized for the computing coordination service.
W.1.3 Service Flows
1. Regarding [[SUGGESTION_START]]the [[SUGGESTION_END]]XR application's need, [[SUGGESTION_START]]the [[SUGGESTION_END]]UE sends a request [[SUGGESTION_START]]for [[SUGGESTION_END]]XR rendering to [[SUGGESTION_START]]the [[SUGGESTION_END]]XR Application Platform.
2. [[SUGGESTION_START]]The [[SUGGESTION_END]]XR application platform [[SUGGESTION_START]]decomposes the [[SUGGESTION_END]]UE's request [[SUGGESTION_START]]in[[SUGGESTION_END]]to different tasks and dispatches the render task to [[SUGGESTION_START]]the [[SUGGESTION_END]]Task Management Server through [[SUGGESTION_START]]the [[SUGGESTION_END]]IP network.
3. [[SUGGESTION_START]]The [[SUGGESTION_END]]Task Management Server analyses the render task and plan[[SUGGESTION_START]]s[[SUGGESTION_END]] the computing task based on the capability of [[SUGGESTION_START]]the [[SUGGESTION_END]]computing resources. Then it sends the requests [[SUGGESTION_START]]for the [[SUGGESTION_END]]computing coordination service and XR communication service to [[SUGGESTION_START]]the [[SUGGESTION_END]]6G core network, including information of computing coordination service QoS (e.g. requested computing capabilities, response time) and [[SUGGESTION_START]]the [[SUGGESTION_END]]communication service QoS (e.g. latency).
4. [[SUGGESTION_START]]The [[SUGGESTION_END]]6G core network selects MEC server B and cloud computing center based on the requested QoS of computing coordination service, and the status information of all enrolled computing resources, and then sends feedback to [[SUGGESTION_START]]the [[SUGGESTION_END]]Task Management Server about the coordination result[[SUGGESTION_START]]s[[SUGGESTION_END]] (e.g. selected computing resources). Meanwhile, [[SUGGESTION_START]]the [[SUGGESTION_END]]6G core network establishes the communication paths between [[SUGGESTION_START]]the [[SUGGESTION_END]]UE and MEC server B and [[SUGGESTION_START]]the [[SUGGESTION_END]]cloud computing center to transmit data for the rendering based on the requested QoS of [[SUGGESTION_START]]the [[SUGGESTION_END]]communication service.
5. [[SUGGESTION_START]]The [[SUGGESTION_END]]Task Management Server distributes the necessary data and associated information to all the selected computing resources via [[SUGGESTION_START]]the [[SUGGESTION_END]]IP network.
6. MEC server B and [[SUGGESTION_START]]the [[SUGGESTION_END]]cloud computing center execute individual rendering sub-tasks, sync-up with [[SUGGESTION_START]]the [[SUGGESTION_END]]Task Management Server about the progress of its sub-tasks and sync-up the status information (e.g. computing workload, congestion status) with [[SUGGESTION_START]]the [[SUGGESTION_END]]6G core network.
7. The 6G core network detects the overload of MEC server B, and informs [[SUGGESTION_START]]the [[SUGGESTION_END]]Task Management Server to replace MEC server B with MEC server C.
8. [[SUGGESTION_START]]The [[SUGGESTION_END]]Task Management Server collects the rendering graphics from MEC server B, MEC server C[[SUGGESTION_START]],[[SUGGESTION_END]] and [[SUGGESTION_START]]the c[[SUGGESTION_END]]loud [[SUGGESTION_START]]computing [[SUGGESTION_END]]center after the sub-tasks are finished and composes the XR image for [[SUGGESTION_START]]the [[SUGGESTION_END]]XR Application Platform. Then [[SUGGESTION_START]]the [[SUGGESTION_END]]XR Application Platform [[SUGGESTION_START]]tran[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]]mits [[SUGGESTION_END]]the rendered XR image to [[SUGGESTION_START]]the [[SUGGESTION_END]]UE.
W.1.4 Post-conditions
The render task is efficiently completed with the collaboration of several edge computing servers under the coordination of [[SUGGESTION_START]]the [[SUGGESTION_END]]6G core network.
The rendered XR image is successfully provided to [[SUGGESTION_START]]the [[SUGGESTION_END]]UE as expected.
W.1.5 Existing features partly or fully covering the use case functionality
None.
W.1.6 Potential New Requirements needed to support the use case
[PR W.1.6-1] The 6G network shall support mechanisms to manage computing resources (i.e. edge server(s) or cloud server(s)). e.g. enrol computing resources, maintain the information of computing resources, etc.
Editor's note: It is FFS to refine the wording of examples.
[PR W.1.6-2] The 6G network shall support mechanisms to collect status information (e.g. computing workload, congestion information, available capability, power consumption) of trusted computing resources (i.e. edge server(s) or cloud server(s)) on-demand or periodically.
[PR W.1.6-3] The 6G network shall provide mechanisms to expose to [[SUGGESTION_START]]a [[SUGGESTION_END]]trusted 3rd party the information (e.g. computing capability, the location, allowed service types, status, power consumption) of computing resources (i.e. edge server(s) or cloud server(s)) .
[PR W.1.6-4] Subject to operator's policy, application needs or both, the 6G network shall support the selection of computing resource(s) (i.e. edge server(s) or cloud server(s)) based on e.g. requested computing capabilities, and support routing of data traffic between a UE and the selected computing resource(s) based on required communication service QoS.
Editor's note: It is FFS to clarify the terminology regarding computing resource and computing node in the requirements.
---------- Fortieth Change ----------
W.2.3 Service Flows
1. A third-party requests access to specific GPU or AI resources via an API provided by Operator A.
2. The 6G network verifies the request against defined policies and authorizes access if conditions are met.
3. The 6G network checks with the policy framework, which in turn may confirm with [[SUGGESTION_START]]the [[SUGGESTION_END]]orchestration system if the resources are available, how many of them are used for NF[[SUGGESTION_START]](s)[[SUGGESTION_END]] and if the load is expected to increase based on the history of network usage. Upon approval, the 6G system allocates the requested resources and establishes secure access for the third-party. This unique network-aware resource exposure capability is enabled by the 6G system's centralized policy framework.
4. The operator could also add its own rules and policy to manage the resource allocation with the help of [[SUGGESTION_START]]a [[SUGGESTION_END]]policy framework.
5. The system continuously monitors resource usage and records data for charging and reporting purposes.
6. The 3rd party is also allowed to analyse the usage in real time.
---------- Forty First Change ----------
W.2.4 Post-conditions
1. Third-party entity completes its tasks using the allocated resources.
2. The 6G network releases the resources and updates the availability status in real-time.
3. Usage data is logged, and relevant charges are generated and sent for billing.
4. [[SUGGESTION_START]]The [[SUGGESTION_END]]Policy function is in control of [[SUGGESTION_START]]the [[SUGGESTION_END]]entire operation.
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S1-250369.zip
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2026-01-13T17:38:50.313194
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S1-254184
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SA1
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TSGS1_112_Dallas
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CR
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revised
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Quality improvement contributions
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3GPP TSG-SA WG1 Meeting #112 S1-254184
17-21 November 2025, DALLAS, USA
CR-Form-v12.2
CHANGE REQUEST
22.261
CR
0853
rev
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Current version:
17.15.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
x
Radio Access Network
x
Core Network
x
Title:
22.261v17.15.0 Correction on the end-to-end latency via satellite in clause 7.4.2
Source to WG:
ZTE
Source to TSG:
SA1
Work item code:
TEI 17, 5GSAT
Date:
2025-10-16
Category:
F
Release:
Rel-17
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-16 (Release 16)
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
T
Reason for change:
With the approved SP-241760,CR 0818 (S1-244784), the value of propagation delay via satellite in table 7.4.1-1 has been updated since version 17.13.0. But the content which has dependency on changed values in 7.4.2 was not change accordingly. Details in below:
In Version 17.12.0,
The yellow highlighted parts in clause 7.4.2 align with table 7.4.1-1.
"One-way delay" used in the note aligns with the colon name in the table 7.4.1-1.
Since version 17.13.0,
The yellow highlighted parts do not align with green highlighted part.
The colon name in the table 7.4.1-1 was changed, but the note keeps unchanged. There is no explanation about "One-way delay" in 22.261.
Summary of change:
Updating end-to-end latency values and notes in clause 7.4.2,
Correcting a typo in table 7.4.2-1,
Updating format of clause 7.4.2 title.
Consequences if not approved:
The end-to-end latency will not align with propagation delay as explained in the notes. Especially, the MEO case has a wrong value.
Clauses affected:
7.4.2
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
***************** First Change ***********************
7.4 KPIs for a 5G system with satellite access
7.4.1 Description
Satellite access networks are based on infrastructures integrated on a minimum of satellites that can be placed in either GEO, MEO or LEO.
The propagation delay associated with these orbit ranges, for the UE to the satellite path, can be summarized in Table 7.4.1-1:
Table 7.4.1-1: UE to satellite propagation delay
UE to serving satellite propagation delay [ms] [NOTE 1]
UE to ground Max propagation delay [ms] [NOTE 2]
Min
Max
LEO
1
13
26
MEO
24
99
198
GEO
120
136
276
NOTE1: The serving satellite provides the satellite radio link to the UE. The delay range for LEO is calculated at elevation angle 90° with 300 km and 10° with 1 500 km. The delay range for MEO is calculated at elevation angle 90° with 7 000 km and 10° with 25 000 km. The delay range for GEO is calculated at elevation angle 90° to 10° with 35 786km.
NOTE2: delay between UE and ground station via satellite link; Inter satellite links are not considered
7.4.2 Requirements
A 5G system providing service with satellite access shall be able to support GEO based satellite access with up to [[SUGGESTION_START]]281[[SUGGESTION_END]] ms end-to-end latency.
NOTE 1: 5 ms network latency is assumed and added to [[SUGGESTION_START]]UE to ground[[SUGGESTION_END]][[SUGGESTION_START]] station via satellite link[[SUGGESTION_END]][[SUGGESTION_START]] propagation dela[[SUGGESTION_END]][[SUGGESTION_START]]y[[SUGGESTION_END]].
A 5G system providing service with satellite access shall be able to support MEO based satellite access with up to [[SUGGESTION_START]]203[[SUGGESTION_END]] ms end-to-end latency.
NOTE 2: 5 ms network latency is assumed and added to [[SUGGESTION_START]]UE to ground station via satellite link propagation delay[[SUGGESTION_END]].
A 5G system providing service with satellite access shall be able to support LEO based satellite access with up to [[SUGGESTION_START]]31[[SUGGESTION_END]] ms end-to-end latency.
NOTE 3: 5 ms network latency is assumed and added to [[SUGGESTION_START]]UE to ground station via satellite link propagation delay[[SUGGESTION_END]].
A 5G system shall support negotiation on quality of service taking into account latency penalty to optimise the QoE for UE.
The 5G system with satellite access shall support high uplink data rates for 5G satellite UEs.
The 5G system with satellite access shall support high downlink data rates for 5G satellite UEs.
The 5G system with satellite access shall support communication service availabilities of at least 99,99%.
Table 7.4.2-1: Performance requirements for satellite access
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
(note 1)
Area traffic capacity
(UL)
(note 1)
Overall user density
Activity factor
UE speed
UE type
Pedestrian
(note 2)
[1] Mbit/s
[100] kbit/s
1,5 Mbit/s/km2
150 kbit/s/km2
[100]/km2
[1,5] %
Pedestrian
Handheld
Public safety
[3,5] Mbit/s
[3,5] Mbit/s
TBD
TBD
TBD
N/A
100 km/h
Handheld
Vehicular connectivity
(note 3)
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
50 %
Up to 250 km/h
Vehicle mounted
Airplanes connectivity
(note 4)
360 Mbit/s/ plane
180 Mbit/s/ plane
TBD
TBD
TBD
N/A
Up to 1000 km/h
Airplane mounted
Stationary
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
N/A
Stationary
Building mounted
Narrowband IoT connectivity
[2] kbit/s
[10] kbit/s
8 kbit/s/km2
40 kbit/s/km2
[400]/km2
[1] %
[Up to 100 km/h]
IoT
Note 1: Area capacity is averaged over a satellite beam.
Note 2: Data rates based on Extreme long-range coverage target values in clause 6.17.2. User density based on rural area in Table 7.1-1.
Note 3: Based on Table 7.1-1
Note 4: Based on an assumption of 120 users per plane 15/7.5 Mbit/s data rate and 20 % activity factor per user
Note 5: All the values in this table are targeted values and not strict requirements.
Note 6: Performance requirements for all the values in this table should be analyzed independently for each scenario.
***************** End of Changes ***********************
|
S1-254184.zip
|
2026-01-13T16:41:54.730221
|
S1-254185
|
SA1
|
TSGS1_112_Dallas
|
CR
|
revised
|
Quality improvement contributions
|
3GPP TSG-SA WG1 Meeting #112 S1-254185
17-21 November 2025, DALLAS, USA
CR-Form-v12.2
CHANGE REQUEST
22.261
CR
0854
rev
-
Current version:
18.18.0
For HELP on using this form: comprehensive instructions can be found at
http://www.3gpp.org/Change-Requests.
Proposed change affects:
UICC apps
ME
x
Radio Access Network
x
Core Network
x
Title:
22.261v18.18.0 Correction on the end-to-end latency via satellite in clause 7.4.2
Source to WG:
ZTE
Source to TSG:
SA1
Work item code:
TEI 17, 5GSAT
Date:
2025-10-16
Category:
A
Release:
Rel-18
Use one of the following categories:
F (correction)
A (mirror corresponding to a change in an earlier release)
B (addition of feature),
C (functional modification of feature)
D (editorial modification)
Detailed explanations of the above categories can
be found in 3GPP TR 21.900.
Use one of the following releases:
Rel-8 (Release 8)
Rel-9 (Release 9)
Rel-10 (Release 10)
Rel-11 (Release 11)
…
Rel-16 (Release 16)
Rel-17 (Release 17)
Rel-18 (Release 18)
Rel-19 (Release 19)
T
Reason for change:
With the approved SP-241760, CR 0819 (S1-244779), the value of propagation delay via satellite in table 7.4.1-1 has been updated since version 18.16.0. But the content which has dependency on changed values in 7.4.2 was not change accordingly. Details in below:
In Version 18.15.0, the yellow highlighted part in clause 7.4.2 align with table 7.4.1-1.
Since version 18.16.0, the yellow highlighted part does not align with green highlighted part.
Summary of change:
Updating end-to-end latency values and notes in clause 7.4.2,
Correcting a typo in table 7.4.2-1.
Consequences if not approved:
The end-to-end latency will not align with propagation delay as explained in the notes. Especially, the MEO case has a wrong value.
Clauses affected:
7.4.2
Y
N
Other specs
X
Other core specifications
TS/TR ... CR ...
affected:
X
Test specifications
TS/TR ... CR ...
(show related CRs)
X
O&M Specifications
TS/TR ... CR ...
Other comments:
This CR's revision history:
***************** First Change ***********************
7.4.2 Requirements
A 5G system providing service with satellite access shall be able to support GEO based satellite access with up to [[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]]77[[SUGGESTION_END]] ms end-to-end latency.
NOTE 1: 5 ms network latency is assumed and added to [[SUGGESTION_START]]UE to ground station via satellite link propagation delay[[SUGGESTION_END]].
A 5G system providing service with satellite access shall be able to support MEO based satellite access with up to [[SUGGESTION_START]]203[[SUGGESTION_END]] ms end-to-end latency.
NOTE 2: 5 ms network latency is assumed and added to [[SUGGESTION_START]]UE to ground station via satellite link propagation delay[[SUGGESTION_END]].
A 5G system providing service with satellite access shall be able to support LEO based satellite access with up to 3[[SUGGESTION_START]]1[[SUGGESTION_END]] ms end-to-end latency.
NOTE 3: 5 ms network latency is assumed and added to [[SUGGESTION_START]]UE to ground station via satellite link propagation delay[[SUGGESTION_END]].
A 5G system shall support negotiation on quality of service taking into account latency penalty to optimise the QoE for UE.
The 5G system with satellite access shall support high uplink data rates for 5G satellite UEs.
The 5G system with satellite access shall support high downlink data rates for 5G satellite UEs.
The 5G system with satellite access shall support communication service availabilities of at least 99,99%.
Table 7.4.2-1: Performance requirements for satellite access
Scenario
Experienced data rate (DL)
Experienced data rate (UL)
Area traffic capacity
(DL)
(note 1)
Area traffic capacity
(UL)
(note 1)
Overall user density
Activity factor
UE speed
UE type
Pedestrian
(note 2)
[1] Mbit/s
[100] kbit/s
1,5 Mbit/s/km2
150 kbit/s/km2
[100]/km2
[1,5] %
Pedestrian
Handheld
Public safety
[3,5] Mbit/s
[3,5] Mbit/s
TBD
TBD
TBD
N/A
100 km/h
Handheld
Vehicular connectivity
(note 3)
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
50 %
Up to 250 km/h
Vehicle mounted
Airplanes connectivity
(note 4)
360 Mbit/s/ plane
180 Mbit/s/ plane
TBD
TBD
TBD
N/A
Up to 1000 km/h
Airplane mounted
Stationary
50 Mbit/s
25 Mbit/s
TBD
TBD
TBD
N/A
Stationary
Building mounted
Video surveillance
(note 4a)
[0,5] Mbit/s
[3] Mbit/s
TBD
TBD
TBD
N/A
Up to 120km/h or
stationary
(note 4b)
Vehicle mounted or fixed installation
Narrowband IoT connectivity
[2] kbit/s
[10] kbit/s
8 kbit/s/km2
40 kbit/s/km2
[400]/km2
[1] %
[Up to 100 km/h]
IoT
Note 1: Area capacity is averaged over a satellite beam.
Note 2: Data rates based on Extreme long-range coverage target values in clause 6.17.2. User density based on rural area in Table 7.1-1.
Note 3: Based on Table 7.1-1
Note 4: Based on an assumption of 120 users per plane 15/7.5 Mbit/s data rate and 20 % activity factor per user
Note 4a: Refer to video surveillance data transmitted (in UL) from a UE on the ground (e.g. picture or video from a camera) using satellite NG-RAN to connect to 5GC, and video surveillance-related configuration or control data sent (in DL) to the UE/device. 0.5 Mbit/s for DL experienced data rate is based on MAVLINK protocol that is widely used for UAV control. 3 Mbit/s for UL experienced data rate is based on the assumed sum from 2.5 Mbit/s for video streaming and 0.5 Mbit/s for data transmission.
Note 4b: Up to 120km/h applies to vehicle mounted while stationary applies to fixed installation.
Note 5: All the values in this table are targeted values and not strict requirements.
Note 6: Performance requirements for all the values in this table should be analyzed independently for each scenario.
***************** End of Changes ***********************
|
S1-254185.zip
|
2026-01-13T16:42:24.859042
|
S1-260011
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
|
Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG-SA WG1 Meeting #112-Ad Hoc-e S1-260011
12-16 January 2026, Online
Source: 6G Study Rapporteurs
Title: Table 14.1.1-1 Support for legacy services and capabilities
Document for: Approval
Agenda item: 1.4
Spec: 3GPP TR 22.870
Version: v1.0.1
Comments
This Table is the outcome of SA1 #112 that was endorsed in S1-254410.
Green indicates there was consensus in SA1 #112 to include the CPR for inclusion into the TR.
Yellow indicates that there the CPR was discussed, and some additional work is needed.
This pCR proposes to update Table 14.1.1-1 (Support for legacy services and capabilities) in TR 22.870 with CPRs for inclusion into the draft TR.
For the ad hoc meeting:
Ascertain that the group agrees to include the CPRs that are “green”?
Resolve “yellow” CPRs/NOTEs.
Proposed Changes
* * * First Change* * *
Table 14.1.1-1: Support for legacy services and capabilities
CPR #
Consolidated Potential Requirement
Original PR #
Comment
14.1.1-1-1
The 6G system shall be able to support the following services:
- Mission Critical Services, i.e. MCPTT, MCData, MCVideo, ref TS 22.179 [53], TS 22.280 [54], TS 22.281 [55]. and TS 22.282 [56],
- Message service, ref TS 22.262 [57],
- Short Message Service (SMS), ref TS 22.101 [58],
- Multimedia communication services, ref TS 22.101 [58], TS 22.261 [14], TS 22.173 [59],
- IMS Multimedia Telephony Service, ref TS 22.261 [14], TS 22.173 [59],
- Roaming services, ref TS 22.011 [60], TS 22.101 [58], TS 22.261 [14],
- Location and positioning services, ref TS 22.261 [14], TS 22.071 [61],
- Broadcast and Multicast Services, ref TS 22.261 [14],
- Emergency Services, ref TS 22.101 [58],
- Public Warning System (PWS), ref TS 22.268 [62],
- Multimedia Priority Service (MPS), ref TS 22.153 [63],
- Lawful Interception, ref TS 22.261 [14] and TS 33.126 [312], and
- Other regulatory services, based on regional/national regulatory requirements.
NOTE: The following services do not need to be supported by the 6G system: CS related telephony services, e.g. CS Fallback, CS based voice call.
Clause 5.4.2
14.1.1-1-2
The 6G system shall be able to support 5G system requirements (functional and performance requirements) defined e.g. in TS 22.261 [14], TS 22.104 [64], TS 22.011 [60], TS 22.173 [59], TS 22.071 [61], TS 22.262 [57], TS 22.185 [65], TS 22.186 [66], TS 22.125 [35], TS 22.263 [67], TS 22.115 [68], TS 22.101 [58], TS 22.153 [63], TS 22.289 [69], TS 22.468 [70], TS 22.368 [71], TS 22.156 [28], TS 22.137 [6].
NOTE: Terms referring to 5G (e.g. “the 5G system”, “NG RAN”) should be implicitly replaced by the corresponding terms for 6G (e.g. “6G system”, “radio access network of the 6G system”) in those requirements.
NOTE: These requirements do not apply to requirements involving the legacy systems/RATs (e.g. E-UTRAN, UTRAN, GERAN).
Requirements mentioned in the above standards that include E-UTRAN, UTRAN, and GERAN are not automatically included.
There was general support for the NOTE, but the text in the NOTE still needs some work/under discussion
14.1.1-1-3
Subject to operator’s policy, the 6G system shall support mobility procedures between the core network (CN) of the 6G system and a 5G core network with minimum impact to the user experience (e.g. Quality of Service (QoS), Quality of Experience (QoE)).
Clause 5.2
14.1.1-1-4
Subject to operator’s policy, the 6G system shall support mobility procedures between the core network of the 6G System and the evolved Packet Core Network (EPC) with minimum impact to the user experience (e.g. QoS, QoE).
Clause 5.2
14.1.1-1-5
Requirements in TS 22.261 clause 5.1.2.2, related to inter-RAT capabilities not to be supported by 5GS, shall apply similarly to 6G System with the following modification:
- voice service continuity from the radio access network of the 6G system to UTRAN CS shall not be supported.
NOTE 2: Terms referring to 5G (e.g. “the 5G system”, “NG RAN”) should be implicitly replaced by the corresponding terms for 6G (e.g. “6G system”, “radio access network of the 6G system”) in those requirements.
Clause 5.2
There was a comment that the NOTE” may need to be revised as TSG RAN is using “6GR”.
14.1.1-1-6
The 6G system shall be able to support a user to access network services via 3GPP and/or non-3GPP access (e.g. WLAN or Wireline).
Clause 5.3
Non-3GPP access
There was a proposal to merge/include this requirement with CPR 14.1.1-1-2
14.1.1-1-7
Subject to operator’s policy, the 6G system shall support mobility between the 6G 3GPP access and non-3GPP access, with minimum impact to the user experience (e.g. QoS, QoE).
Clause 5.3
Non-3GPP access
There was a proposal to merge/include this requirement with CPR 14.1.1-1-2
14.1.1-1-8
Subject to operator’s policy, the 6G system shall be able to provide a suitable means for UEs to determine which access technology to use between 3GPP access technology and non-3GPP access technology if congestion is detected.
NOTE: This requirement is intended for initial access only and is not applicable for ongoing sessions.
Clause 5.2
Non-3GPP access
Access selection
NEW: Agreed in SA1 #112
* * * End of Changes* * *
|
S1-260011.zip
|
2026-01-21T09:24:17.555958
|
S1-260012
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
|
Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG-SA WG1 Meeting #112-Ad Hoc-e S1-260012
12-16 January 2026, Online
Source: 6G Study Rapporteurs
Title: Table 14.1.1-2 Enhancements to legacy services and capabilities
Document for: Approval
Agenda item: 1.4
Spec: 3GPP TR 22.870
Version: v1.0.1
Comments
This Table is the outcome of SA1 #112 that was endorsed in S1-254410.
Green indicates there was consensus in SA1 #112 to include the CPR for inclusion into the TR.
Yellow indicates that there the CPR was discussed, and some additional work is needed.
Comments from S1-254411 were re-inserted to aid discussions.
This pCR proposed to update Table 14.1.1-2 (Enhancements to legacy services and capabilities) with CPRs to which SA1 has reached consensus for inclusion into the draft TR.
For the ad hoc meeting:
Ascertain that the group agrees to include the CPRs that are “green”?
Is there agreement on creating a new table for Localised Networks?
If so, in this clause or in Industry & Verticals clause?
Resolve “yellow” CPRs/comments.
Proposed Changes
* * * First Change * * * *
Table 14.1.1-2: Enhancements to legacy services and capabilities
CPR #
Consolidated Potential Requirement
Original PR #
Comment
14.1.1-2-1
The 6G network shall provide a mechanism to support event triggered network sharing (e.g. disaster occurrence, network failure, overloaded situation, resource constraints).
PR 5.7.4.2-2
Network Sharing
14.1.1-2-2
Subject to regulatory requirements or operator policy, the 6G network shall support sharing of radio access network with sensing capability among operators.
PR-5.7.10.6-1
Network Sharing
[[SUGGESTION_START]][QC/S1-254250]: move to sensing[[SUGGESTION_END]][[SUGGESTION_START]]?[[SUGGESTION_END]]
14.1.1-2-3
The 6G system shall support suitable access categories to manage the access attempt for the new services (such as sensing, AI application, computing)[[SUGGESTION_START]] with respect to its characteristics (e.g. delay-sensitive, delay-tolerant)[[SUGGESTION_END]] in congestion scenarios.
PR 5.7.6.2-2
Unified Access Control
PR modified in SA1 #112
14.1.1-2-4
The 6G system shall provide optimized network capabilities for FWA (e.g. support stationary devices) in relevant bands taking into consideration the regulatory requirements for each specific band.
PR 5.7.1.2-1
PR 5.7.1.2-2
Fixed Wireless Access
[ZTE/S1-254096]: merge into 14.1.1-1-3 (Legacy Spt)
14.1.1-2-5
The 6G system shall enable the means to provide awareness of user service characteristics (e.g. data rate, latency) to support the RAN and CN in making real time resource allocation for FWA.
PR 5.7.1.2-3
Fixed Wireless Access
Alt 14.1.1-2-5
[[SUGGESTION_START]]Based on operator policy, [[SUGGESTION_END]][[SUGGESTION_START]]t[[SUGGESTION_END]]he 6G system shall [[SUGGESTION_START]]support[[SUGGESTION_END]] means to [[SUGGESTION_START]]be [[SUGGESTION_END]]aware of user service characteristics (e.g. data rate, latency[[SUGGESTION_START]], predicted changes to each traffic flow component of its service/application to the 6G network[[SUGGESTION_END]]) to [[SUGGESTION_START]]dynamically adjust and optimse network resources.[[SUGGESTION_END]].
PR 5.7.1.2-3
PR-5.9.8.2-1
PR-5.9.8.2-2
Service awareness including Fixed Wireless Access
[ZTE/S1-254096]
NEW #
The 6G system shall provide mechanisms to support efficient bandwidth utilization by the FWA CPE.
NOTE: An FWA Customer Premises Equipment (CPE) is used to connect to the network, like any other UE, using a 3GPP access.
PR 5.7.1.2-5
Fixed Wireless Access
EN cleared in SA1 #112
14.1.1-2-6
The 6G system shall enhance the Short Message Service to enable a network operator to verify the identity of the SMS sender and information concerning operator verified SMS sender information to the recipient of a SMS.
NOTE 1: Operator-verified SMS sender information is used to inform the recipient of a SMS that the identity of the SMS sender is operator-verified and support displaying additional information (e.g. brand name, logo, etc.) of the SMS sender. Human interface aspects are out of scope of this requirement.
NOTE 2: Indication that the identity of the SMS sender is operator-verified, any additional information about the SMS sender and the message itself is assumed to be integrity protected.
NOTE 3: Based on interworking agreements and trust relationships, the requirements above apply also when the SMS recipient is roaming or receives a SMS from a sender served by other operators.
NOTE 4: The requirements above apply to A2P SMS and may apply to Person-to-Person SMS.
PR 5.7.3.2-1
PR 5.7.3.2-2
SMS
Alt 14.1.1-2-6
The 6G system shall enhance the Short Message Service to enable a network operator to verify the identity of the SMS sender and [[SUGGESTION_START]]provide [[SUGGESTION_END]]operator verified SMS sender information to the recipient of a SMS.
NOTE 1: Operator-verified SMS sender information is used to inform the recipient of a SMS that the identity of the SMS sender is operator-verified and support displaying additional information (e.g. brand name, logo, etc.) of the SMS sender. Human interface aspects are out of scope of this requirement.
NOTE 2: Indication that the identity of the SMS sender is operator-verified, any additional information about the SMS sender and the message itself is assumed to be integrity protected.
NOTE 3: Based on interworking agreements and trust relationships, the requirements above apply also when the SMS recipient is roaming or receives a SMS from a sender served by other operators.
NOTE 4: The requirements above apply to A2P SMS and may apply to Person-to-Person SMS.
PR 5.7.3.2-1
PR 5.7.3.2-2
SMS
[QC/S1-254250]
14.1.1-2-7
Subject to operator’s policy and agreement with 3rd party, the 6G network shall support a mechanism to start and stop offering certain network service(s) in a local area network adapting to the demand of e.g. the users, 3rd party or the network operator.
PR 5.9.6.6-1
6G LAN
[ZTE/S1-254096]: proposed to be moved to new table below)
[QC/S1-254250]: merge w/other Local NW PRs in Verticals
[Huawei/S1-254300]: rename to 6G local area network within a PLMN
14.1.1-2-8
Subject to operator policies, and agreement between the PLMN operator and authorized 3rd party, the 6G network shall support a mechanism to
- authorize PLMN’s users to access a subscribed service provided by an authorized 3rd party via a local area network (deployed by the PLMN operator), and
- minimize service interruption when the serving network changes between the local area network and the PLMN network.
PR 5.9.6.6-2
6G LAN
[ZTE/S1-254096]: proposed to be moved to new table below)
[Huawei/S1-254300r1]: rename to 6G local area network within a PLMN
14.1.1-2-9
Subject to operator’s policy and regulation, for an operator with multiple 6G core networks, when there is UE mobility from one 6G core network to another 6G core network of the same PLMN, the 6G network shall support efficient traffic routing for the traffic from UE to data network.
NOTE 1: The above term does not imply any architectural assumption, e.g. whether 6G CN is a new or evolved CN (compared to 5G).
NOTE 2: This requirement only impacts core network.
PR 5.9.7.6-1
Multiple Core Networks
14.1.1-2-10
Based on operator policy, the 6G network shall support the ability to allow an authorized 3rd party service provider to provide information of the service characteristics for each traffic flow component of its service/application to the 6G network.
PR 5.9.8.2-1
Service Awareness
[ZTE/S1-254096] merged into Service awareness CPR (Alt 14.1.1-2-5)
14.1.1-2-11
Based on operator policy, the 6G network shall support mechanisms to dynamically adjust and optimize network resources based on the service characteristics, including their predicted changes, provided by the service or application.
PR 5.9.8.2-2
Service Awareness
[ZTE/S1-254096] merged into Service awareness CPR (Alt 14.1.1-2-5)
14.1.1-2-12
Subject to operator policy and [[SUGGESTION_START]] local regulation and subscriber permission [[SUGGESTION_END]], the 6G system shall support means to provide users with differentiation of QoS and charging based on users’ digital identity information issued by a third party and users’ subscription information.
PR 5.5.9.6-1
Digital Identity
[[SUGGESTION_START]][[[SUGGESTION_END]]QC/S1-244250] this PR is also in the Charging section…maybe split the QoS and Charging parts?
PR modified in SA1 #112
14.1.1-2-13
The multimedia telephony service [144] provided by IMS shall be able to minimise user perception of the transition during codec modification of an ongoing voice call, e.g. a codec change during communication link fluctuation.
PR 5.7.8.2-1
IMS
Codec change QoE
14.1.1-2-13
The 6G and IMS systems shall provide improved system capabilities for the Multimedia Telephony Service to support an IP-CAN in the 6GS.
PR 5.7.2.2-1
IMS
EN cleared in SA1 #112
14.1.1-2-14
Subject to operator policy, the IMS shall support means to minimize impact on the user experience (e.g. call failure) when a UE is engaged in one IMS session where more than one IMS service is triggered.
NOTE: Typical example of such situation can be a IMS media related service (e.g. play tone, play announcement) in conjunction with an IMS data channel based service.
PR.5.7.7.6-1
IMS
EN cleared in SA1 #112
14.1.1-2-15
Subject to operator’s policy and regulatory requirements, the 6G system shall support a mechanism to enable home operator to authorize UE to access 6G services from home operator’s partner operators when UE only has subscription data of home operator.
PR 5.9.11-1
(was PR 5.5.11.6-1)
Partner PLMNs
Agreed in SA1 #112
Moved from 5.5.11 to 5.9.11
14.1.1-2-16
Subject to operator’s policy and regulatory requirements, the 6G system shall support a UE to be aware of 6G services provided by home operator’s partner operators.
PR 5.9.11-2
(was PR 5.5.11.6-2)
Partner PLMNs
Agreed in SA1 #112
Moved from 5.5.11 to 5.9.11
14.1.1-2-17
Subject to operator’s policy and regulatory requirements, the 6G system shall allow home operator to use, monitor, and update the set of 6G services exposed by its partner operators to the home operator.
PR 5.9.11-3
(was PR 5.5.11.6-3)
Partner PLMNs
Agreed in SA1 #112
Moved from 5.5.11 to 5.9.11
14.1.1-2-18
The 6G system should support potential enhancement of network slicing, e.g.:
- Create and delete a network slice in an optimized manner by leveraging automated operations,
- Modify (e.g. reconfigure or change resources) a network slice efficiently, and
- Improve the mechanism to select, reselect and access network slice(s).
PR 5.7.5.2-1
Network Slicing
EN cleared in SA1 #112
Editor’s Note: ZTE/S1-254191 proposed a new table (below) with PRs from 5.6.2, 5.9.5 and 5.9.6 are grouped. Table 14.1.14-2: Localized network has been endorsed and is found in Industry & Verticals (clause 14.1.14). Should this proposed new table be merged (all/partially) there?
[[SUGGESTION_START]]New [[SUGGESTION_END]][[SUGGESTION_START]]Table [[SUGGESTION_END]][[SUGGESTION_START]]x: [[SUGGESTION_END]][[SUGGESTION_START]]Locali[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]]ed network [[SUGGESTION_END]]
[[SUGGESTION_START]]CPR #[[SUGGESTION_END]]
[[SUGGESTION_START]]Consolidated Potential Requirement[[SUGGESTION_END]]
[[SUGGESTION_START]]Original PR #[[SUGGESTION_END]]
[[SUGGESTION_START]]Comment[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to operator policies and service level agreements, the 6G system shall enable operators to provision network services as part of the operator’s PLMN network on-demand, e.g. in response to an urgent event (e.g. disaster, emergency and DDoS events), with certain level of local control and specific functionalities in a given area during a specific time period.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 1: The level of local control can be based on operator policies and agreements with 3rd party. For example, the authorization and policy control of users to access the provisioned services are not affected by the failure of the operator’s PLMN network. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 2: The enabled functionalities can be based on operator policies and agreements with 3rd party. For example, data connectivity service and voice service are prioritized when an urgent event happens in a residential community; small data transfer service is prioritized when an urgent event happens in an IoT based farmland. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 3: Some situations can target the required network services to be provisioned within hours to serve certain users whose QoE is impacted by an urgent event. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 4: Local control refers to the capability of part of the operator’s PLMN network to operate autonomously and independently, e.g. management of local subscription, local traffic, without interaction with the operator’s PLMN. [[SUGGESTION_END]]
[[SUGGESTION_START]]PR 5.6.2.6-1[[SUGGESTION_END]]
[[SUGGESTION_START]]provision network services on-demand[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to operator policies and service level agreements, the 6G system shall enable a network operator to authorize a UE, that is subscribed to local network services, to access services from the PLMN of the same operator.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 5: This applies to scenarios where a service is not available in the local network services that have been provisioned on-demand, but is available from the PLMN of the same operator.[[SUGGESTION_END]]
[[SUGGESTION_START]]PR 5.6.2.6-2[[SUGGESTION_END]]
[[SUGGESTION_START]]authorize a local UE accessing service in PLMN[[SUGGESTION_END]]
[[SUGGESTION_START]]The 6G system shall support on-demand rollout (e.g. within hours) of new or updated services/capabilities with minimal disruption to existing services, including the ability to efficiently rollback those services/capabilities, as needed (e.g. in case of failures or demand from other services).[[SUGGESTION_END]]
[[SUGGESTION_START]]PR 5.9.5.6-1[[SUGGESTION_END]]
[[SUGGESTION_START]]on-demand rollout service/capability[[SUGGESTION_END]]
[[SUGGESTION_START]]The 6G network shall provide means to minimise the impact to the user experience during the rollout and rollback (if needed) of new and updated services/capabilities.[[SUGGESTION_END]]
[[SUGGESTION_START]]PR 5.9.5.6-2[[SUGGESTION_END]]
[[SUGGESTION_START]]minimise impact to user experience[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to operator’s policy and agreement with 3rd party, the 6G network shall support a mechanism to start and stop offering certain network service(s) in a local area network adapting to the demand of e.g. the users, 3rd party or the network operator.[[SUGGESTION_END]]
[[SUGGESTION_START]]PR 5.9.6.6-1[[SUGGESTION_END]]
[[SUGGESTION_START]]start and stop service in local network[[SUGGESTION_END]]
[[SUGGESTION_START]]Subject to operator policies, and agreement between the PLMN operator and authorized 3rd party, the 6G network shall support a mechanism to[[SUGGESTION_END]]
[[SUGGESTION_START]]- authorize PLMN’s users to access a subscribed service provided by an authorized 3rd party via a local area network (deployed by the PLMN operator) [[SUGGESTION_END]]
[[SUGGESTION_START]]- minimize service interruption when the serving network changes between the local area network and the PLMN network.[[SUGGESTION_END]]
[[SUGGESTION_START]]PR 5.9.6.6-2[[SUGGESTION_END]]
[[SUGGESTION_START]]authorize a PLMN UE accessing service in local network，[[SUGGESTION_END]]
[[SUGGESTION_START]]service continuity between PLMN and local network[[SUGGESTION_END]]
* * * End of Changes * * * *
|
S1-260012.zip
|
2026-01-21T09:24:54.437326
|
S1-260013
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
|
Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG SA WG 1 Meeting #112 Ad Hoc - e S1-260013
12-16 January 2026, electronic meeting (revision of S1-26xxxx)
Source: 6G Study Rapporteurs
pCR Title: Pseudo-CR on Table 14.1.8-1 General AI requirements for 6G system
Draft Spec: 3GPP TR 22.870 v 1.0.1
Agenda item: 1.4
Document for: Approval
Contact: Xiaonan Shi (shixiaonan@chinamobile.com) and Jean Trakinat (jean.trakinat1@t-mobile.com)
Comments
This Table is the outcome of SA1 #112 that was endorsed in S1-254410.
Green indicates there was consensus in SA1 #112 to include the CPR for inclusion into the TR.
Yellow indicates that there the CPR was discussed, and some additional work is needed.
This pCR proposes to update Table 14.1.8-1 (General AI requirements for 6G system) in TR 22.870 with CPRs for inclusion into the draft TR.
For the ad hoc meeting:
Ascertain that the group agrees to include the CPRs that are “green”?
Resolve “yellow” CPRs/NOTEs.
Proposed Changes
* * * First Change * * * *
Table 14.1.8-1 – General AI requirements for 6G system
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 14.1.8-1-1
Based on operator policy, the 6G system shall support AI capabilities.
NOTE: Example of AI capabilities is the system ability to predict the UE behaviour (based on UE type, historical data, mobility patterns, etc.) and use that for the allocation and planning resources efficiently.
PR 6.4.6-1
General, prediction
CPR 14.1.8-1-2
Based on operator policy, the network entities supporting AI capabilities shall be able to collaborate upon request.
PR 6.4.6-2
General
Alternative CPR 14.1.8.-1-1
Subject to operator policy and user preference, the 6G system shall be able to support mechanisms (e.g. AI capabilities) to predict UE behavior (e.g. based on device UE type, historical data, mobility patterns, etc.) for efficient resource allocation and planning.
PR 6.4.6-1
PR 6.4.6-2
Proposed merged CPR on AI capabilities for prediction of UE behavior
CPR 14.1.8-1-3
Based on operator policy, the 6G system shall be able to support mechanisms (e.g. AI capabilities in the network and UEs) allowing the network and UEs to negotiate communication parameters for a communication service.
PR 6.4.6-3
General, performance
CPR 14.1.8-1-4
Subject to operator policy, the 6G network shall support the use of AI capabilities for the operations and management (OAM) of the 6G network for energy efficiency and carbon emissions reduction.
PR 6.16.6-1
Energy
CPR 14.1.8-1-5
Subject to operator policy, regulatory requirements, and subscription-based permission, 6G network shall be able to access 6G System data (e.g. user-related data) and 3rd party application data, to fulfil the requested AI services.
PR 6.17.6-4
General, AI service data
NEW: Agreed in SA1 #112
CPR 14.1.8-1-6
Subject to operator policy and regulatory requirements, the 6G network shall enable the resilience of AI services in disaster area which has limited computing and communication resources.
NOTE 2: resilience can be enabled by using small models to keep the service continuity with some loss on user experience (accuracy of AI service).
PR 6.32.6-5
General, AI service resilience
NEW: Agreed in SA1 #112
CPR 14.1.8-1-7
Subject to operator' policy, subscriber permission and local regulations, the 6G network shall support trajectory prediction (e.g. location, route, destination) of a UE in emergency scenario utilizing information from the 6G network, the UE, and authorized third parties.
PR 6.60.6-1
trajectory prediction
NEW: Agreed in SA1 #112
* * * End of Changes * * * *
|
S1-260013.zip
|
2026-01-21T09:25:25.865434
|
S1-260014
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
|
Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG SA WG 1 Meeting #112 Ad Hoc - e S1-260014
12-16 January 2026, electronic meeting (revision of S1-26xxxx)
Source: 6G Study Rapporteurs
pCR Title: Pseudo-CR on Table 14.1.8-2 Network AI Agent
Draft Spec: 3GPP TR 22.870 v 1.0.1
Agenda item: 1.4
Document for: Approval
Contact: Xiaonan Shi (shixiaonan@chinamobile.com) and Jean Trakinat (jean.trakinat1@t-mobile.com)
Comments
This Table is the outcome of SA1 #112 that was endorsed in S1-254410.
Green indicates there was consensus in SA1 #112 to include the CPR for inclusion into the TR.
Yellow indicates that there the CPR was discussed, and some additional work is needed.
This pCR proposes to update Table 14.1.8-2 (Network AI Agent) in TR 22.870 with CPRs for inclusion into the draft TR.
For the ad hoc meeting:
Ascertain that the group agrees to include the CPRs that are “green”?
Resolve “yellow” CPRs/NOTEs.
Proposed Changes
* * * First Change * * * *
Table 14.1.8-2 – Network AI Agent
NOTE: The mention of AI capabilities such as AI Agent doesn’t imply or preclude any architecture assumption or solution.
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 14.1.8-2-1
Subject to operator’s policy, the 6G network shall support mechanism, e.g. AI capabilities such as AI Agent, to provide suitable 3GPP service or combination of multiple 3GPP services in response to received service request (e.g. expressed via intent(s)) from subscriber/users, an authorized third party.
PR 6.6.6-4
PR 6.20.6-1
PR 6.21.6-2
PR 6.32.6-1
PR 6.43.6-1
PR 6.51.6-1
Proposed merged CPR on Network AI Agent, provide service based on intent
-
Based on operator policy, the 6G network shall support mechanisms (e.g. AI capabilities such as AI Agent) to provide multiple 3GPP services, in response to received intent(s) (e.g. from a third-party AI Agent).
PR 6.6.6-4
Network AI Agent, provide service based on intent
-
Subject to operator’s policy, the 6G system shall be able to support mechanism to provide 3GPP service to applications on one or multiple UEs belonging to a user, based on the received intent from the user.
PR 6.20.6-1
Network AI Agent, provide service based on intent
-
The 6G network shall provide 3GPP services (including communication, sensing and computing) with QoS assurance based on intent received (e.g. from subscribers).
PR 6.21.6-2
Network AI Agent, provide service based on intent
-
The 6G network shall support mechanism, e.g. AI capabilities such as AI Agent, to provide suitable 3GPP service or combination of multiple 3GPP services to subscribers requested by received intent from the user.
NOTE: The mention of AI capabilities such as AI Agent doesn’t imply or preclude any architecture assumption or solutions.
PR 6.32.6-1
Network AI Agent, provide service based on intent
-
Subject to operator’s policy, the 6G network shall be able to provide means for the authorized third party to request 3GPP service by intent.
PR 6.43.6-1
Network AI Agent, provide service based on intent
-
Based on operator policy, the 6G system shall support mechanisms (e.g. AI capabilities such as AI Agent) in the 6G network to provide 3GPP/6G services, which includes coordination of multiple 6G services (e.g. communication, sensing, AI service).
PR 6.51.6-1
Network AI Agent, provide service
CPR 14.1.8-2-2
Based on operator policy and user consent, the 6G network shall be able to take into account information related to user mobility context, subscription information when invoking 3GPP services based on received intent(s) from the user.
PR 6.6.6-2
Network AI Agent, provide service based on intent
SA1#112 comment:
to be separate from CPR 14.1.8-2-1
CPR 14.1.8-2-3
Based on operator policy and user consent, the 6G network shall support mechanisms (e.g. AI capabilities such as Al Agent) to provide 3GPP services on demand based on the received intent(s) from user by taking into account of network-related information and information from trusted third-party.
PR 6.44.6-2
Network AI Agent, provide service based on intent
SA1#112 comment:
to be separate from CPR 14.1.8-2-1
CPR 14.1.8-2-4
Subject to operator’s policy, the 6G network shall be able to support mechanisms (e.g. AI capabilities such as AI Agent) to provide the on-demand 3GPP service at a given time and location area based on the authorized third party’s request by intent.
PR 6.43.6-2
Network AI Agent, provide service based on intent
SA1#112 comment:
to be separate from CPR 14.1.8-2-1
CPR 14.1.8-2-5
Based on operator policy, the 6G network shall support mechanisms (e.g. AI capabilities such as AI Agent) to invoke authorized 3rd party capabilities, when providing 3GPP services based on received intent(s) from the user.
NOTE 2: The authorized 3rd party capabilities help to enhance or complement the results of internal capabilities to achieve more relevant and specific business objectives.
PR 6.6.6-5
Network AI Agent, provide service based on intent and invoke 3rd party capabilities
NEW: Agreed in SA1 #112
CPR 14.1.8-2-6
Subject to operator policy, the 6G network shall support mechanisms (e.g. AI capabilities such as AI Agent) to send intent(s) related to 3GPP services towards a third-party AI Agent (e.g. proactively or in response to a received intent), also taking into account information related to user mobility context, subscription information.
PR 6.6.6-3
Network AI Agent, intent
CPR 14.1.8-2-7
Based on operators’ policy and local regulation and subscriber permissions, the 6G system shall support mechanisms (e.g. AI capabilities such as AI Agent) to translate intent received (e.g. from subscribers) into service and service performance requirements.
PR 6.21.6-1
Network AI Agent, intent translation
CPR 14.1.8-2-8
The 6G system shall support a mechanism to identify and associate the source (e.g. home robot, or specific AI Agent) of the intent with a subscriber and with the actions resulting from the handling/processing of the intent.
PR 6.21.6-3
Network AI Agent, intent
CPR 14.1.8-2-9
Subject to operator policy, the 6G network shall support mechanisms (e.g. Al capabilities such as Al Agent) to authorize the user who sends the intent.
The 6G network shall support mechanisms to ensure the user is authorized to request the intent.
PR 6.44.6-1
Network AI Agent, intent
CPR 14.1.8-2-10
Based on the operator's policy, the 6G system shall support interactions with the user/subscriber (e.g. evaluating intent feasibility, collecting feedback) for processing the intent received from the user/subscriber.
PR 6.56.6-1
Network AI Agent, process intent
NEW: Agreed in SA1 #112
CPR 14.1.8-2-11
Based on the operator's policy, the 6G network shall support a mechanism to perform adaptations considering the interactions with the user/subscriber to improve processing of the received intent.
PR 6.56.6-2
Network AI Agent, process intent
NEW: Agreed in SA1 #112
CPR 14.1.8-2-12
Subject to operator policy, the 6G network shall support a mechanism (e.g. AI capabilities such as AI Agent) to monitor and evaluate the quality of the provided 3GPP service and enhance the service if needed.
PR 6.21.6-4
Network AI Agent, quality optimizations
CPR 14.1.8-2-13
Subject to local regulation and subscriber permission and operator policy, the 6G system shall be able to support mechanisms (e.g. AI capabilities such as AI Agent) to enable real-time call quality analytics and dynamic optimizations.
PR 6.54.6-1
Network AI Agent, quality optimizations
CPR 14.1.8-2-14
Subject to local regulation and subscriber permission, and operator policy, the 6G network shall be able to receive and analyse aggregated call quality data, and apply enhancements using mechanisms (e.g. AI capabilities such as AI Agent) in the 6G network.
PR 6.54.6-2
Network AI Agent, quality optimizations
CPR 14.1.8-2-15
Subject to local regulation and subscriber permission and operator policy, the 6G system shall be able to support continuous enhancement of mechanisms (e.g. AI capabilities such as AI Agent) in 6G network, from user experience feedback.
PR 6.54.6-3
Network AI Agent, quality optimizations
CPR 14.1.8-2-16
The 6G network shall support mechanisms (e.g. Al capabilities such as Al Agent) to enable the interaction with user, e.g. negotiate the service-related aspects, to guarantee the provided services meets the user expectation.
NOTE 2: the user feedback would be e.g. additional info. regarding performance of requested 3GPP services, purpose/intention of user requesting the services.
PR 6.44.6-4
Network AI Agent, quality optimizations
NEW: Agreed in SA1 #112
CPR 14.1.8-2-17
Subject to regulatory requirements and operator policy, the 6G network shall be able to support mechanisms (e.g. AI capabilities such as AI Agent) to minimize the service interruption in various disaster situations by enabling dynamic inter-PLMN cooperation, e.g. using network resources from different operators, adapt to various cases of disasters (e.g. varying number of affected subscribers) and improve the efficiency and speed of disaster recovery through such cooperation.
NOTE 2: The actual agreement of the inter-PLMN cooperation is out of scope of 3GPP, and can be defined in other organizations like GSMA. Example of the inter-PLMN cooperation can be allowing one operator’s subscribers to temporally access the cooperating operator’s network in the same region for basic connectivity or mission critical services, under the agreements of the operators. And the network AI Agent as described in the use case can help negotiate parameters like amount of subscribers, duration, etc.
NOTE 3: Example of the mechanisms could include dynamic enforcement of pre-agreed disaster policies.
PR 6.57.6-1
Network AI Agent, quality optimizations
NEW: Agreed in SA1 #112
CPR 14.1.8-2-18
Subject to operator policy, the 6G network shall support mechanisms (e.g. AI capabilities such as AI Agent) in the 6G network to recover from degradation of services provided by the network.
PR 6.32.6-4
Network AI Agent, network recover
NEW: Agreed in SA1 #112
* * * End of Changes * * * *
|
S1-260014.zip
|
2026-01-21T09:26:03.738422
|
S1-260015
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
|
Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG SA WG 1 Meeting #112 Ad Hoc - e S1-260015
12-16 January 2026, electronic meeting (revision of S1-26xxxx)
Source: 6G Study Rapporteurs
pCR Title: Pseudo-CR on Table 14.1.8-3 3rd party AI Agent
Draft Spec: 3GPP TR 22.870 v 1.0.1
Agenda item: 1.4
Document for: Approval
Contact: Xiaonan Shi (shixiaonan@chinamobile.com) and Jean Trakinat (jean.trakinat1@t-mobile.com)
Comments
This Table is the outcome of SA1 #112 that was endorsed in S1-254410.
Green indicates there was consensus in SA1 #112 to include the CPR for inclusion into the TR.
Yellow indicates that there the CPR was discussed, and some additional work is needed.
This pCR proposes to update Table 14.1.8-3 (3rd party AI Agent) in TR 22.870 with CPRs for inclusion into the draft TR.
For the ad hoc meeting:
Ascertain that the group agrees to include the CPRs that are “green”?
Resolve “yellow” CPRs/NOTEs.
Proposed Changes
* * * First Change * * * *
Table 14.1.8-3 – 3rd party AI Agent
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 14.1.8-3-1
Based on operators’ policy and local regulation and subscriber permission, 6G network shall support trusted network access for 3rd party AI Agent and support a mechanism to expose 3rd party AI Agent’s attributes (e.g. related users, sensing capabilities, AI capabilities, service features) to other 3rd party AI Agents.
PR 6.7.6-1
PR 6.23.6-3
Proposed merged CPR on 3rd party AI Agent, exposure between AI Agents
Modified in SA1 #112
-
Based on regulatory requirements, operators’ policy and user consent, 6G network shall support trusted network access for 3rd party AI Agent and support a mechanism to expose 3rd party AI Agent’s attributes (e.g. related users, sensing capabilities, AI capabilities, service features) to other 3rd party AI Agents.
PR 6.7.6-1
3rd party AI Agent, access, exposure between AI Agent
-
Based on user consent and operator's policy, the 6G network shall support a secure mechanism to expose information of AI application (e.g. AI Agent application) on UE (e.g. related user) to AI application (e.g. AI Agent application) on other UE.
PR 6.23.6-3
3rd party AI Agent, exposure between AI Agent
CPR 14.1.8-3-2
Based on local regulation and subscriber permission, operators’ policy and agreement with authorized 3rd party, the 6G network shall be able to support mechanisms to enable secure identification of 3rd party AI Agents associated with a user (e.g. AI Agents belonging to a customer).
PR 6.7.6-2
PR 6.55.6-1
Proposed merged CPR on 3rd party AI Agent, identification
Modified in SA1 #112
-
Based on regulatory requirements, user consent, operators’ policy and agreement with authorized 3rd party, the 6G network shall be able to support security identification for 3rd party AI Agents provided by authorized 3rd party associated with a user (e.g. AI Agents belonging to a customer).
PR 6.7.6-2
3rd party AI Agent, identification
-
Based on regulatory requirements and operators’ policy, 6G network shall support dynamic identification of 3rd party AI Agents.
NOTE: Dynamic means the identification of this AI Agent can be temporary assigned based on task, and this identification can be assigned by different operators based on different tasks.
PR 6.55.6-1
3rd party AI Agent, identification
CPR 14.1.8-3-3
Based on regulatory requirements, operators’ policy and user preference, 6G network shall support mechanisms for [[SUGGESTION_START]]authorized [[SUGGESTION_END]]3rd party AI Agents to provide their attributes to 6G network, and discover other authorized 3rd party AI Agents.
NOTE: Attributes can include e.g. capabilities, associated authorized users
PR 6.7.6-3
PR 6.46.6-1
[[SUGGESTION_START]]PR 6.62.6-1[[SUGGESTION_END]]
Proposed merged CPR on 3rd party AI Agent, register and discover
-
Based on regulatory requirements, operators’ policy and user consent, 6G network shall support mechanisms for 3rd party AI Agents to provide/register their attributes (e.g. sensing capabilities, AI capabilities, service features, associated authorized users) to 6G network, and discover other authorized 3rd party AI Agents to achieve collaborative task.
PR 6.7.6-3
3rd party AI Agent, register and discover, collaboration
-
Based on the user consent and operator’s policy, the 6G network shall be able to support the discovery of 3rd party AI Agent (application) on the UE.
PR 6.46.6-1
3rd party AI Agent, discover
-
Based on regulatory requirements, operator's policy and agreement with 3rd party, the 6G network shall support a mechanism for an authorized 3rd party to provide the information about the AI Agent application (e.g. authorized users suing the 3rd party AI Agent application, capabilities related to the 3rd party AI Agent application) to the 6G network.
PR 6.62.6-1
3rd party AI Agent, provide information of AI Agent to network
NEW: Agreed in SA1 #112
CPR 14.1.8-3-4
Based on operator's policy and user preference, the 6G network shall support a mechanism to manage the attributes (e.g. sensing capabilities, AI capabilities, service features, associated authorized users) of AI application (e.g. AI Agent application) on UE (e.g. related user).
PR 6.23.6-1
3rd party AI Agent, management
CPR 14.1.8-3-5
Based on local regulation and subscriber permission and operator's policy, the 6G system shall support secure interoperability and efficient mechanism between AI applications (e.g. AI Agents) on multiple UEs.
NOTE 1: Interoperability between AI Agents refers to the ability to discover, authenticate and authorize AI Agents to communicate, exchange data, and work together seamlessly.
NOTE 2: Collaborative task refers to an activity, action requiring the involvement of two or more AI Agents.
PR 6.9.6-2
PR 6.23.6-4
PR 6.30.6-2
PR 6.40.6-1
Proposed merged CPR on 3rd party AI Agent, collaboration
-
The 6G system shall support secure interoperability between AI Agents and between AI Agents and applications to achieve a collaborative task.
NOTE 1: Interoperability between AI Agents refers to the ability to discover, authenticate and authorize AI Agents to communicate, exchange data, and work together seamlessly.
NOTE 2: Collaborative task refers to an activity, action requiring the involvement of two or more AI Agents.
PR 6.9.6-2
3rd party AI Agent, collaboration
-
Based on user consent and operator's policy, the 6G network shall support a secure mechanism to provide communication service between AI applications (e.g., AI Agent applications) on multiple UEs for a collaborative task.
PR 6.23.6-4
3rd party AI Agent, collaboration
-
Subject to operator’s policy, the 6G network shall be able to provide AI service to enable collaborative task for AI applications running on multiple UEs.
PR 6.30.6-2
3rd party AI Agent, collaboration
-
Subject to user consent and operator’s policy, the 6G network shall provide efficient mechanisms to support the collaboration of UEs.
PR 6.40.6-1
3rd party AI Agent, collaboration
CPR 14.1.8-3-6
The 6G network shall provide means to support coordination within and across groups of 3rd party AI Agents to achieve a collaborative task associated with a 3GPP service.
NOTE 2: Coordination can facilitate the discovery of attributes & capabilities across the 3rd party AI Agents, the attribution of roles such as a task coordinator/supervisor of the group, the decision to setup another group for a sub task etc.
PR 6.7.6-6
3rd party AI Agent, collaboration
NEW: Agreed in SA1 #112
CPR 14.1.8-3-7
Based on regulatory requirements and operators’ policy and user preference, the 6G network shall provide means to support efficient and secure communication (including multi-modality exchange) between multiple e.g. AI applications/3rd party AI Agents on UEs over a target area considering data characteristics.
NOTE: This requirement can apply to 3rd party AI Agents of same users or different users. It is expected that the required communication service would be provisioned in the range of minutes to days, depending on use case. Lower for temporary task and higher for long term task.
PR 6.7.6-4
PR 6.8.6-3
PR 6.23.6-4
Proposed merged CPR on 3rd party AI Agent, communication
CPR 14.1.8-3-8
Based on operators’ policy and local regulation and subscriber permission, the 6G system shall support hosting of large amount of 3rd party AI Agents managed and controlled by the 6G network and/or multiple AI Agent applications on a UE.
PR 6.9.6-1
3rd party AI Agent, general
-
The 6G system shall support hosting large amounts of AI applications (e.g. AI Agent applications) managed and controlled by the 6G network and/or multiple AI Agent applications on a UE.
PR 6.9.6-1
PR 6.9.6-1 was modified during SA1 #112
CPR 14.1.8-3-9
Based on operator policy, the 6G network shall be able to support secure means to expose 3GPP services (e.g. 6G computing service in Service Hosting Environment) to the authorised third-party AI Agent based on its intent.
PR 6.6.6-1
PR 6.7.6-5
Proposed merged CPR on 3rd party AI Agent, service exposure
-
Based on operator policy, the 6G network shall be able to support secure means to expose its services to the authorised third-party AI Agent based on its intent.
PR 6.6.6-1
3rd party AI Agent, service exposure
-
Based on operator policy, the 6G network shall be able to support secure means to expose different services, e.g. computing offloading service in Service Hosting Environment, to the authorized third-party AI Agent.
PR 6.7.6-5
3rd party AI Agent, service exposure
CPR 14.1.8-3-10
Based on local regulation and subscriber permission and operator policy, the 6G system shall provide a suitable means for an AI Agent application on UE to invoke some 3GPP services (e.g. IMS service, AI service) upon request.
PR 6.8.6-1
PR 6.14.6-2
PR 6.30.6-1
Proposed merged CPR on 3rd party AI Agent, service invoke
Modified in SA1 #112
-
Based on user consent and operator policy, the 6G system shall provide a suitable means for an AI Agent application on UE to invoke some 3GPP services (e.g. IMS service).
PR 6.8.6-1
3rd party AI Agent, service invoke
-
Subject to operator’s policy, the 6G network shall support mechanisms for a 3rd party AI-based application on UE (e.g. UAV) to invoke an AI service upon request.
PR 6.14.6-2
3rd party AI Agent, service invoke
-
Subject to operator’s policy and user consent, the 6G network shall be able to support mechanism for AI application on UE to invoke AI services provided by 6G network.
PR 6.30.6-1
3rd party AI Agent, service invoke
CPR 14.1.8-3-11
The 6G network shall be able to provide a suitable means to allocate network resources (e.g., network slice) to a group of trusted third parties (e.g., applications running on multiple UEs/robots or third party AI Agents), considering dynamic changes of traffic demand and QoS characteristics.
PR 6.52.6-2
3rd party AI Agent, resource
CPR 14.1.8-3-12
Based on the operator's policy and local regulation and subscriber permission, the 6G network shall support a mechanism to authenticate and authorize 3rd party AI Agent on the UE.
PR 6.23.6-2
PR 6.41.6-1
PR 6.41.6-2
Proposed merged CPR on 3rd party AI Agent, authentication and authorization
-
The 6G network shall support a mechanism to authorize AI application (e.g. AI Agent application) on UE to invoke 3GPP services.
PR 6.23.6-2
3rd party AI Agent, service invoke, authorization
-
The 6G network shall support a mechanism to authenticate and authorize 3rd party AI Agent.
PR 6.41.6-1
3rd party AI Agent, authentication and authorization
-
Based on the operator's policy, the 6G network shall support a secure mechanism for authenticated and authorized 3rd party AI Agent to invoke 3GPP services.
PR 6.41.6-2
3rd party AI Agent, service invoke, authentication and authorization
CPR 14.1.8-3-13
Based on local regulation and subscriber permission, operator policy and regulatory requirements, the 6G system shall provide an efficient way to expose information (e.g. change of QoS) to authorized 3rd party AI Agents (e.g. by means suitable for prompt augmentation).
PR 6.8.6-2
PR 6.13.6-1
PR 6.9.6-3
Proposed merged CPR on 3rd party AI Agent, exposure to AI Agent
-
Based on user consent, operator policy and regulatory requirements, the 6G system shall provide an efficient way to expose information (e.g. change of QoS) to the application on the UE.
PR 6.8.6-2
3rd party AI Agent, exposure to AI Agent
[[SUGGESTION_START]]Subject to operator policies, the 6G network shall be able to provide authorized AI applications (e.g. AI Agents applications) in the UE and in the 6G network) with communication service performance information (e.g. throughput, latency) relevant for their operation, including to achieve a collaborative task.[[SUGGESTION_END]]Agent
PR 6.9.6-3
3rd party AI Agent, exposure to AI Agent
[[SUGGESTION_START]]PR 6.9.6-3 was Modified in SA1 #112[[SUGGESTION_END]]
-
Subject to operator’s policy, the 6G network shall be able to expose information from the network to authorized 3rd party AI Agents by means suitable for prompt augmentation.
NOTE: Prompt augmentation is an approach of adding further information, or instructions, to a prompt in order to enhance the AI-generated response, e.g. in terms of quality or relevance.
PR 6.13.6-1
3rd party AI Agent, exposure to AI Agent
CPR 14.1.8-3-14
Based on the local regulation and subscriber permission and operator’s policy, the 6G system shall be able to support means for the network to invoke the 3rd party AI Agent (application) on the UE.
PR 6.46.6-2
3rd party AI Agent, network invoke AI Agent
CPR 14.1.8-3-15
Subject to operator’s policy, regulatory requirements and local regulation and subscriber permission, the 6G system shall support a mechanism for a user-authorized AI application (e.g. AI Agent) in the Service Hosting Environment to autonomously initiate communication with emergency services on behalf of the user, i.e., the communication session is associated with the user's identity and location.
PR 6.47.6-1
3rd party AI Agent, emergency service
CPR 14.1.8-3-16
Subject to operator’s policy, regulatory requirements and local regulation and subscriber permission , the 6G network shall support a mechanism to log the data (e.g., sensor data, context information) used by an AI application (e.g., AI Agent) in the Service Hosting Environment, which autonomously decided to initiate communication with emergency services on behalf of the user.
PR 6.47.6-2
3rd party AI Agent, emergency service
CPR 14.1.8-3-17
Subject to operator’s policy, regulatory requirements and local regulation and subscriber permission, the 6G system shall support a mechanism for a user to provide policies which define the autonomous actions that can be taken by their authorized AI applications (e.g. AI Agents) in the Service Hosting Environment.
NOTE: For example, a user could provide a policy authorizing their AI Agent to share location data with a pre-defined family member. A separate policy could authorize their AI Agent to initiate communication with emergency services on the user's behalf when the Agent identifies a critical safety event
PR 6.47.6-3
3rd party AI Agent, emergency service
* * * End of Changes * * * *
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S1-260015.zip
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2026-01-21T09:26:30.793664
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S1-260016
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SA1
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TSGS1_112-Ad_Hoc-e
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pCR
|
available
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Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
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3GPP TSG-SA WG1 Meeting #112-Ad Hoc-e S1-260016
12-16 January 2026, Online
Source: 6G Study Rapporteurs
Title: Table 14.1.10-1 ISAC
Document for: Approval
Agenda item: 1.4
Spec: 3GPP TR 22.870
Version: v1.0.1
Comments
This Table is the outcome of SA1 #112 that was endorsed in S1-254410.
Green indicates there was consensus in SA1 #112 to include the CPR for inclusion into the TR.
Yellow indicates that there the CPR was discussed, and some additional work is needed.
Comments from S1-254328 were re-inserted to aid discussions.
This pCR proposed to update Table 14.1.10-1 (ISAC) with CPRs to which SA1 has reached consensus for inclusion into the draft TR.
For the ad hoc meeting:
Ascertain that the group agrees to include the CPRs that are “green”?
Resolve “yellow” CPRs/comments.
Proposed Changes
* * * First Change * * * *
14.1.10 Integrated Sensing and Communication (ISAC)
Table 14.1.10-1: ISAC
CPR #
Consolidated Potential Requirement
Original PR #
Comment
14.1.10-1-1
Subject to regulatory requirements, operator’s policy, the 6G network should support suitable means to collect non-3GPP sensing data from third party if available.
PR 7.16.6-1
[ZTE] Non-3GPP sensing data collection
Considering the sensing data collection is specific, it is suggested to remain it in 14.1.10
14.1.10-1-2
Subject to operator policies, the 6G Network shall
provide sensing service to detect, classify and count one or more sensing targets (e.g. detect UAVs, distinguish UAVs from birds, identify specific UAV characteristics.).
NOTE: Classification refers to the identification of specific characteristics of the detected target objects and grouping together of the detected target objects with similar characteristics.
PR 7.23.6-1
Sensing modes
Classification of objects
14.1.10-1-3
Subject to operator policies, the 6G Network shall provide mechanisms for configuring a sensing operation with a single or multiple sensing modes from all sensing modes supported (e.g. bistatic, monostatic, multistatic).
PR 7.14.6-1
Sensing modes configuration
14.1.10-1-4
Subject to regulation and operator’s policy, the 6G system should provide mechanisms to ensure sensing service is able to be provided with a given sensing system capacity or/and a latency upper-bound to nearby UEs (e.g. AMRs), requested by the trusted third party.
NOTE: The term 'sensing system capacity' is the maximum number of targets that can be detected per unit area given sensing QoS requirements per target, which include localization accuracy and sensing service latency [11].
NOTE 2: The latency depends on different types of applications in various verticals, such as factory, mining and on how fast the AMR is moving in the zone of interest.
PR 7.5.6-3
PR 7.7.6-1
Sensing Service Capacity
Multiple targets
[ZTE]: Sensing QoS: capacity/density/latency upper-bound
Subject to operator policy, local regulation and subscriber permission, the 6G system shall mechanisms to protect data privacy during the processing of sensing data.
PR 7.12.6-2
[[SUGGESTION_START]]PR modified in SA1 #112[[SUGGESTION_END]]
14.1.10-1-5
Subject to regulation and operator's policy, 6G system shall support sensing target density requested by the third party for a given sensing service.
PR 7.7.6-1
Target Density
14.1.10-1-6
Based on operator policy, regional and/or national regulations, the 6G network in the area of the disaster shall provide secure mechanisms to collect sensing results with a requested level of accuracy that can be used to generate real time maps.
PR 7.2.6-1
Accuracy
Target Density
Latency
3rd Party Support
14.1.10-1-7
Subject to operator’s policy and regulation, the 6G network shall be able to provide a sensing service to derive predicted location and/or velocity of sensing target(s).
PR 7.10.6-1
Prediction
14.1.10-1-8
Subject to regulation and operator policy, the 6G network shall provide a target prediction capability to derive predicted target characteristics (e.g. size, shape, location, velocity), while maintaining the privacy of the sensing target(s) and means to expose the prediction of location and/or velocity of sensing target(s) to a trusted third-party.
PR 7.10.6-1
PR 7.10.6-2
Prediction
Exposure
Privacy
Third Party support
14.1.10-1-9
Subject to regulatory requirements, operator’s policy, local regulation and subscriber permission, 6G network shall support the use of stored sensing data to provide a sensing service and ensure that only authorised entities are able to access the stored sensing data and results.
PR 7.11.6-1
PR 7.12.6-1
Sensing Data Storage
Usage & Security
PR modified in SA1 #112
14.1.10-1-10
The 6G system shall be able to prioritize communication, sensing and positioning together used in Network Assisted Smart Transportation.
PR 7.19.6-2
Prioritization
14.1.10-1-11
Subject to operator’s policy, the 6G system shall provide exposure mechanism(s) to activate and deactivate exposing sensing results to a UE (AMR) that are used for prediction in a given sensing area of interest at a particular time of interest to nearby UEs at the request of a trusted third party.
PR 7.8.6-1
Exposure
Third party support
Delivery synchronization
14.1.10-1-12
The 6G Network shall provide suitable mechanisms for the exposure of sensing results in a synchronised manner with other types of traffic (e.g. audio, video, haptics) to the sensing service consumer.
PR 7.14.6-2
Sensing result exposure sync.with other traffic,
14.1.10-1-13
Subject to operator’s policy,[[SUGGESTION_START]] local regulation and subscriber permission[[SUGGESTION_END]], the 6G network shall be able to authorise UE and expose sensing results to an application on the UE for a specific service.
NOTE: As an example, UE could use the provided sensing results (e.g. environment characteristics around UE) to optimize communication service.
PR 7.20.6-1
PR 7.21.6-1
PR 7.5.6-4
Exposure to UE
PR modified in SA1 #112
14.1.10-1-14
Subject to operator’s policy, local regulation and subscriber permission, the 6G network shall be able to provide sensing results to a UE for a specific service, where the UE is authorized by mobile network operator providing sensing service.
PR 7.5.6-4
PR modified in SA1 #112
14.1.10-1-15
Subject to operator’s policy and regulation, the 6G system shall be able to link sensing results with communication service area for communication service.
PR 7.20.6-2
ISAC Linking
14.1.10-1-16
Subject to regulatory requirements and user permission, the 6G network shall be able to use the 6G sensing service to monitor and recognize human gestures.
PR 7.24.6-1
Gesture Recognition
14.1.10-1-17
Subject to operator’s policy, [[SUGGESTION_START]] local regulation and subscriber permission[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]] the 6G network shall be able to provide secure means to expose the prediction of location and/or velocity of sensing target(s) to a trusted third-party, while maintaining the privacy of the sensing target(s).
PR 7.10.6-1
PR 7.10.6-2
Sensing Data Security and Privacy Protection
14.1.10-1-18
The 6G system shall be able to support energy-efficient sensing operations.
PR 7.5.6-2
(To be consolidated with PR 7.8.6-2
Energy-efficient Sensing Operations
[ZTE} PR 7.5.6-2 move to EE part 14.1.4
14.1.10-1-19
Subject to operators’ policies, regulations, when offering sensing service, if the assistance information (e.g. the actual sensing target characteristics) from a trusted 3rd party is available, the 6G network shall provide means for a mobile network operator to monitor and validate the sensing result (e.g. by comparing the sensing results with the actual sensing target characteristics etc.).
PR.7.26.6-1
Assistance Info from trusted 3rd party
NEW: Agreed in SA1 #112
14.1.10-1-20
Subject to network operator policy, the 6G network shall be able to provide secure means to an authorized 3rd party for providing dynamic re-configuration requests of communication services in order to ensure continuous reliable connectivity to mobile UEs with the required Quality of Service as provided in Table 7.27.6-2 in a changing environment.
PR 7.27.6-1
Dynamic reconfiguration requests for reliability
NEW: Agreed in SA1 #112
PR-2 and -3 are KPIs
* * * End of Changes* * *
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S1-260016.zip
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2026-01-21T09:27:08.182840
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S1-260041
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SA1
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TSGS1_112-Ad_Hoc-e
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pCR
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available
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Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
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3GPP TSG-SA WG1 Meeting #112 Ad Hoc - e S1-260041
12-16 January 2026, electronic meeting (revision of S1-26XXXX)
Title: Updated consolidation of KPI requirements on immersive section
Agenda Item:
Source: Moderator (China Unicom)
Contact: Qun Wei, weiqun5@chinaunicom.cn
Abstract: Prepare and propose a way for consolidation on KPI requirements on immersive section.
1. Introduction
Based on the meeting preparations, this document only provides the consolidation way forward and discussion regarding the immersive KPIs, based on TR 22.870 and do not include performance requirements with editor’s note.
2. Reason for Change
To provide communication performance requirements contribution and reflect key points regarding the performance requirements of immersive sections.
3. Proposal
It is proposed to agree the following changes to new version of 3GPP TR 22.870.
Discussion Part
Besides the basic template consolidation, [[SUGGESTION_START]]the following comments have been taken into consideration for the latest updates[[SUGGESTION_END]]：
General：
CM1: It needs to be clarified whether immersive and AI KPI definitions (already in agreed KPI tables) can be directly used during the consolidation work.
CM2: Some of this definition already exists in other specs can we just point to those specs instead of introducing the definitions here. Also, it would help see which definitions new and which ones are not New.
CM3: Which existing KPI table of TR 22.870 can be merged this time?
• Comments: Need to list merged table at the beginning.
• Tables with EN need to put into Part II.
CM4: consolidate the immersive KPIs and AI KPIs in separate documents.
CM5: Putting existing use cases and definitions in separate files to simplify consolidation.
CM6: “Characteristic parameter”, “Influence quantity”, are suggested to be removed from the tables at this point.
[[SUGGESTION_START]]CM7：[[SUGGESTION_END]] [[SUGGESTION_START]]Do we need a separate column for the "number of UEs"? This parameter is only applicable to UC 9.6 and[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] not [[SUGGESTION_END]][[SUGGESTION_START]]every [[SUGGESTION_END]][[SUGGESTION_START]]other use cases[[SUGGESTION_END]][[SUGGESTION_START]]?[[SUGGESTION_END]][[SUGGESTION_START]] “Other KPIs” is used to cover number of UEs and other values [[SUGGESTION_END]][[SUGGESTION_START]]with further check the reference of "Area traffic capacity", "Communication service availability", "Overall user density", "Activity factor".[[SUGGESTION_END]]
UC 9.12 Personalized interactive immersive guided tour
[[SUGGESTION_START]]CM1[[SUGGESTION_END]]: the word “lag” in the synchronization threshold should be either defined or removed, change to delay? Or advanced?
[[SUGGESTION_START]]CM2: Probably[[SUGGESTION_END]][[SUGGESTION_START]] consider aligning the terminology for "lag" and "delay". Changing "lag" to "delay" would be a[[SUGGESTION_END]][[SUGGESTION_START]]n[[SUGGESTION_END]][[SUGGESTION_START]] option, for the sake of consistency with the term in 22.261.[[SUGGESTION_END]]
[[SUGGESTION_START]]Keep the [[SUGGESTION_END]][[SUGGESTION_START]]o[[SUGGESTION_END]][[SUGGESTION_START]]ption of s[[SUGGESTION_END]][[SUGGESTION_START]]eparat[[SUGGESTION_END]][[SUGGESTION_START]]ing[[SUGGESTION_END]][[SUGGESTION_START]] UC[[SUGGESTION_END]][[SUGGESTION_START]]9.12[[SUGGESTION_END]][[SUGGESTION_START]] and UC[[SUGGESTION_END]][[SUGGESTION_START]]9.3[[SUGGESTION_END]][[SUGGESTION_START]] into two independent tables[[SUGGESTION_END]][[SUGGESTION_START]] to solve the above CMs[[SUGGESTION_END]][[SUGGESTION_START]], maintaining the original parameter usage[[SUGGESTION_END]] [[SUGGESTION_START]]to reflect the use case itself, based on the discussion.[[SUGGESTION_END]][[SUGGESTION_START]] Added a CPR o[[SUGGESTION_END]][[SUGGESTION_START]]f UC9.12 table.[[SUGGESTION_END]]
UC 9.2 Immersive Gaming
[[SUGGESTION_START]]CM1[[SUGGESTION_END]]: note A-6 is not referenced by the table. [[SUGGESTION_START]]Author added[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
Change1: note A-3 can be merged to table. Need author and companies further check. [[SUGGESTION_START]]Author accepted[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
UC 9.6 Immersive Gaming Immersive media content production via the wireless link
[[SUGGESTION_START]]Change1[[SUGGESTION_END]]: note B-1 can be merged to table. Need author and companies further check.[[SUGGESTION_START]] Author suggested to keep it.[[SUGGESTION_END]]
[[SUGGESTION_START]]UC 9.7 Use case on multiple application media synchronization [[SUGGESTION_END]]
[[SUGGESTION_START]]CM[[SUGGESTION_END]][[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]]：UC 9.7 [[SUGGESTION_END]][[SUGGESTION_START]]can be [[SUGGESTION_END]][[SUGGESTION_START]]in [[SUGGESTION_END]][[SUGGESTION_START]]a[[SUGGESTION_END]][[SUGGESTION_START]] separate table[[SUGGESTION_END]][[SUGGESTION_START]], as[[SUGGESTION_END]][[SUGGESTION_START]] it is distinct from UCs 9.12 and 9.3[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]9.18 Use Case on Immersive Audio Production in Live Events [[SUGGESTION_END]]
[[SUGGESTION_START]]Table 9.18.6-1 was newly consolidated[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]9.5 Use case on seamless immersive reality in education[[SUGGESTION_END]]
[[SUGGESTION_START]]B[[SUGGESTION_END]][[SUGGESTION_START]]rackets removed from [[SUGGESTION_END]][[SUGGESTION_START]]table [[SUGGESTION_END]][[SUGGESTION_START]]values.[[SUGGESTION_END]]
[[SUGGESTION_START]]9.8 Use case on holographic telepresence in healthcare[[SUGGESTION_END]]
[[SUGGESTION_START]]Table 9.8.6-1 was newly consolidated.[[SUGGESTION_END]] [[SUGGESTION_START]]With the author's assistance, the [[SUGGESTION_END]][[SUGGESTION_START]]data rate parameter [[SUGGESTION_END]][[SUGGESTION_START]]has been corrected[[SUGGESTION_END]][[SUGGESTION_START]] to the right pla[[SUGGESTION_END]][[SUGGESTION_START]]ce[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
* * * Begin of changes * *
Y Consolidated potential KPIs
y.1 KPIs for immersive communication service
Editor's Note: the following potential performance requirements from immersive session are considered for consolidation.
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 9.2.6-1] (Table 9.2.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 9.3.6-1] (Table 9.3.6-1)
- TR 22.870 [[SUGGESTION_START]]1.0.[[SUGGESTION_END]][[SUGGESTION_START]]1[[SUGGESTION_END]] [PR 9.5.6-5] (Table 9.5.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 9.6.6-3] (Table 9.6.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 9.7.2-1] (Table 9.7.2-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 9.9.6-2] (Table 9.9.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 9.12.6-6] (Table 9.12.6-1)
[[SUGGESTION_START]]- TR 22.870 [[SUGGESTION_END]][[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]][[SUGGESTION_START]]0[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]][[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]] [PR 9.18.6-1] (Table 9.18.6-1)[[SUGGESTION_END]]
[[SUGGESTION_START]]- TR 22.870 1.0.[[SUGGESTION_END]][[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]] [PR 9.8.6-1] (Table 9.8.6-1)[[SUGGESTION_END]]
The following potential performance requirements from AI session are considered for consolidation.
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 6.10.6-6] (Table 6.10.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 6.26.6-1] (Table 6.26.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 6.48.6-1] (Table 6.48.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 6.49.6-1] (Table 6.49.6-1)
[[SUGGESTION_START]]- TR 22.870 1.0.1 [PR 9.3.6-1] (Table 9.3.6-1)[[SUGGESTION_END]]
The following potential performance requirements are excluded due to the remaining ENs and will be taken into consideration after the ENs are cleaned up:
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].1 [PR 6.38.6-3] (Table 6.38.6-1)
[CPR y.1-1] The 6G system [[SUGGESTION_START]]including IMS [[SUGGESTION_END]]shall support various immersive communication services with the following KPIs.
Editor’s Note: Unless stated otherwise, the definition and understanding "Max allowed end-to-end latency", "Service bit rate: user-experienced data rate ", "Reliability", "# of UEs", "UE Speed", "Service Area", [[SUGGESTION_START]]"Area traffic capacity"[[SUGGESTION_END]][[SUGGESTION_START]], "Communication service availability[[SUGGESTION_END]][[SUGGESTION_START]]",[[SUGGESTION_END]][[SUGGESTION_START]] "Overall user density[[SUGGESTION_END]][[SUGGESTION_START]]",[[SUGGESTION_END]] [[SUGGESTION_START]]"[[SUGGESTION_END]][[SUGGESTION_START]]Activity factor[[SUGGESTION_END]][[SUGGESTION_START]]" [[SUGGESTION_END]]and "Positioning accuracy" refers to [22.261] and [22.[[SUGGESTION_START]]156[[SUGGESTION_END]]].
Table y.1-1: Performance requirements for immersive communication services
Use Cases
Max allowed end-to-end latency
Service bit rate: user-experienced data rate
Reliability
UE Speed
Service Area
Other KPIs
[[SUGGESTION_START]]Downlink[[SUGGESTION_END]]
[[SUGGESTION_START]]Uplink[[SUGGESTION_END]]
Immersive Gaming
(UC 9.2 A)
Compute flows: [5 - 20ms]
Conversational and game state flows:
[50 - 100ms]
Streaming flows:
[200 - 300ms]
[[SUGGESTION_START]](note A-6)[[SUGGESTION_END]]
Player/Cheerleader[[SUGGESTION_START]]:[[SUGGESTION_END]]
(note A-1)
[640 Mbps] (8K, 120fps, compression ratio of 300 and 8 bits per color)
[240 Mbps] (8K, 120fps, compression ratio of 400 and 12 bits per pixel)
[200 Mbps] (8K, 90fps, compression ratio of 400 and 12 bits per pixel)
Spectator:
(note A-3)
[320 Mbps] (2D 8K[[SUGGESTION_START]] video[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]] 120fps, compression ratio of 300 and 8 bits per color)
[120 Mbps] (2D 8K[[SUGGESTION_START]] video[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]] 120fps, compression ratio of 400 and 12 bits per pixel)
[100 Mbps] (2D 8K[[SUGGESTION_START]] video[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]] 90fps, compression ratio of 400 and 12 bits per pixel)
[[SUGGESTION_START]](note A-6)[[SUGGESTION_END]]
[[SUGGESTION_START]]Player/Cheerleader[[SUGGESTION_END]][[SUGGESTION_START]]:[[SUGGESTION_END]]
[[SUGGESTION_START]](note A-2)[[SUGGESTION_END]]
[[SUGGESTION_START]][100 Mbps][[SUGGESTION_END]]
[[SUGGESTION_START]]Spectator: [[SUGGESTION_END]]
[[SUGGESTION_START]](note A-4)[[SUGGESTION_END]]
[[SUGGESTION_START]][10 Mbps][[SUGGESTION_END]]
[99.9 – 99.99 %]
Stationary, pedestrian
[38 m x 15 m]
(note A-5)
Positioning accuracy: [≤10 cm]
Immersive media content production via the wireless link
(UC 9.6 B)
[100ms]
(note B-1)
[[SUGGESTION_START]](note B-2)[[SUGGESTION_END]]
[[SUGGESTION_START]][2.65 Gbps] (4K, 60fps, 10 bits per color, compression ratio of 4) [[SUGGESTION_END]]
[[SUGGESTION_START]][1.1 Gbps] (4K, 60fps, 10 bits per color, compression ratio of 10) [[SUGGESTION_END]]
[[SUGGESTION_START]][7.96 Gbps] (8K, 60fps, 10 bits per color, compression ratio of 6) [[SUGGESTION_END]]
[[SUGGESTION_START]][4.8 Gbps] (8K, 60fps, 10 bits per color,[[SUGGESTION_END]]
[[SUGGESTION_START]]compression ratio of 10)[[SUGGESTION_END]]
[99,99 %]
Stationary, Pedestrian
30 m x 30 m
# of UEs:
4
(note B-3)
Seamless Immersive Reality in Education
(UC 9.5 D)
Split rendering：< 10ms
Voice：< 50ms
Collaboration：< 150ms
< 250 Mbps
[[SUGGESTION_START]]< 250 Mbps[[SUGGESTION_END]]
N/A
Pedestrian
N/A
Area traffic capacity:
Indoor: < 250 Mb/s/m2
Outdoor (Wide Area)
: < 20 Mb/s/m2
Positioning accuracy:
Horizontal: ≤ 10cm
Vertical: ≤ 10cm
Mixed Reality gaming
(UC 9.9 E)
[20ms]
(note E-1)
[50 Mbps]
(note E-2)
[[SUGGESTION_START]][50-100 Mbps][[SUGGESTION_END]]
[[SUGGESTION_START]] (note E-3)[[SUGGESTION_END]]
N/A
5 km/h
N/A
Communication service availability:
Dense Urban [99%]
Urban [99%]
Rural [98%]
Overall user density:
Dense Urban [25,000 /km2]
Urban [1000-10,000 /km2]
Rural [100 /km2]
Activity factor: 2%
(note E-4)
[[SUGGESTION_START]]Holographic telepresence in Healthcare[[SUGGESTION_END]]
[[SUGGESTION_START]](UC 9.8 F)[[SUGGESTION_END]]
[[SUGGESTION_START]]Presence:[[SUGGESTION_END]]
[[SUGGESTION_START]][<100 ms][[SUGGESTION_END]]
[[SUGGESTION_START]]Movement:[[SUGGESTION_END]]
[[SUGGESTION_START]][<20 ms][[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]F-[[SUGGESTION_END]][[SUGGESTION_START]]1）[[SUGGESTION_END]]
[[SUGGESTION_START]][~ 240 Mbps][[SUGGESTION_END]]
[[SUGGESTION_START]](4K, 30fps, 8 bits per colour, 6DoF, compression ratio 100)[[SUGGESTION_END]]
[[SUGGESTION_START]][~ 950 Mbps][[SUGGESTION_END]]
[[SUGGESTION_START]](8K, 30fps,8 bits per colour, 6DoF, compression ratio 100)[[SUGGESTION_END]]
[[SUGGESTION_START]][500 Mbps-1 Gbps][[SUGGESTION_END]]
[[SUGGESTION_START]](3D Point cloud, 30fps,8 bits per colour, 6DoF, AI-based)[[SUGGESTION_END]]
[[SUGGESTION_START]](note F-2)[[SUGGESTION_END]]
[[SUGGESTION_START]][99.9%-99.999%][[SUGGESTION_END]]
[[SUGGESTION_START]]Stationary, Pedestrian[[SUGGESTION_END]]
[[SUGGESTION_START]][5 m x 5 m][[SUGGESTION_END]]
[[SUGGESTION_START]](note F-3)[[SUGGESTION_END]]
[[SUGGESTION_START]]# of UEs:[[SUGGESTION_END]]
[[SUGGESTION_START]]1[[SUGGESTION_END]]
NOTE A-1: It is important to note that the data rates may change under different assumptions. Data Rate = Video resolution * (Bits per color * 3) * Refresh rate * # of eye buffer / (compression ratio), with the following assumptions: 2 eye buffers; 8K video resolution; refresh rate of 120 fps or 90 fps. The frame rate of 120 fps is assumed as it has been shown that such high frame rate helps reduce the probability of simulator sickness [15]. 90 fps is the typical frame rate in current display device. compression ratio of 300 and 8 bits per color; or compression ratio of 400 and 12 bits per pixel [160]. A resolution of 8K (8192 x 4320) per eye can help to remove graphics pixelation and provide good XR user experience [159]. High refresh rates (e.g. 120 fps) are very correlated and inter prediction between frames increase compression. Some codecs (e.g. MV-HEVC) may further drop the bitrate requirement.
NOTE A-2: Significantly higher compared to 5G to enable sensor sharing for split computation.
NOTE A-3: DL data rate for a spectator is assumed to be 2D 8K video.
NOTE A-4: UL tracking for a spectator not as intensive as the player/cheerleader.
NOTE A-5: Average basketball court is 28 m x 15 m (adding 5 m on both sides for the spectator seating) to make a 38 m x 15 m basketball gymnasium.
NOTE A-6: The provided values are targeted values and not strict requirements.
NOTE B-1: One-way delay that is from camera to holographic player.
NOTE B-2: This bit rate is assuming to use the JPEG-XS [352] pre-compression encoding method to ensure both high-quality images and high encoding efficiency, while also realizing lower encoding delays [161]. Which is intended as a targeted value per UE and not a strict requirement.
NOTE B-3: 4 cameras are used in simple stage [353], more cameras may be needed for condition of larger number of performers, with complex stage and lighting conditions. It depends on the deployment.
NOTE E-1: The local encoding and processing on the glasses is assumed.
NOTE E-2: Remote or split rendering for gaming applications, such as rendering 360-degree video tiles in the cloud and transmitting the images to device.
NOTE E-3: Envisioned 1080P resolution 2 grayscale cameras
1920 x 1080 x 8 bit, a depth sensor 1920 x 1080 x 8 bit with 60 FPS means 1920 * 1080 * 24 * 60
bps = 3Gbps uncompressed. Depending on compression ratios that would lead to 50 Mbps to
100 Mbps
NOTE E-4: Activity factor 2% is based on 20% uptake of XR service and 10% activity factor for the XR application.
[[SUGGESTION_START]]NOTE F-1: For real time presence of hologram, refer to [169]; for more interaction, the motion-to-photon delay will consider the effect of cyber sickness [170][[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE F-2: For volumetric-based holography, the bandwidth is impacted by the effective pixel count, which is related to the resolution, colour quality and bit-depth [106]. 4-parallax is used for entry-level 6DoF and can extend to more viewpoints for higher accuracy. The data rate can be estimated based on [105] for uncompressed raw data and optimized by different compression algorithms with different compression rate stated in [171], such as H.265/HEVC, H.266/VCC, MV-HEVC. With the help of AI technologies such as Neural Holographic Video Compression (NHVC), the bandwidth for transmitting hologram can be optimized a lot [173]. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE F-3: The size of a small room/office.[[SUGGESTION_END]]
Editor’s Note: “UC 9.2 A”, the "A" has no technical meaning and serves solely to maintain correspondence with the respective use case and NOTEs during the consolidation work. [[SUGGESTION_START]]It will be cancelled if the consolidation is agreed.[[SUGGESTION_END]]
[[SUGGESTION_START]][CPR y.1-[[SUGGESTION_END]][[SUGGESTION_START]]x[[SUGGESTION_END]][[SUGGESTION_START]]] The 6G system [[SUGGESTION_END]][[SUGGESTION_START]]including IMS [[SUGGESTION_END]][[SUGGESTION_START]]shall [[SUGGESTION_END]][[SUGGESTION_START]]support [[SUGGESTION_END]][[SUGGESTION_START]]media synchronization for single applications [[SUGGESTION_END]][[SUGGESTION_START]]with the [[SUGGESTION_END]][[SUGGESTION_START]]following [[SUGGESTION_END]][[SUGGESTION_START]]KPI[[SUGGESTION_END]][[SUGGESTION_START]]:[[SUGGESTION_END]]
Table y.1-2: Performance requirements for media synchronization[[SUGGESTION_START]] for single applications[[SUGGESTION_END]]
Use Cases
Max allowed end-to-end latency
Service bit rate: user-experienced data rate
Synchronization threshold
Other KPIs
Personalized interactive immersive guided tour
(note 1)
(note 2)
(note 3)
Audio (UL/DL): [10ms]
Audio UL/DL:
[5-512 kbps]
Audio-to-haptics lag: [25ms];
Haptics-to-audio lag: [12ms]
N/A
Immersive video (DL): [200-300ms]
Immersive video DL:
[10-20 Mbps]
Visual-to-haptics lag: [20ms];
Haptics-to-visual lag: [30ms]
Avatar between remote guide and UEs: [20ms]
Avatar: [0.1-30] Mbps
(depending on the format)
Avatar animation: 2 Mbps uncompressed.
1 Mbps compressed
Audio-to-avatar lag: [25ms];
Avatar-to-audio lag: [12ms]
Pose & action data (UL): [5ms]
Pose & action data UL:
[100 – 400 kbps]
Avatar-to-haptics lag: [20ms];
Haptics-to-avatar lag: [30ms]
Environment sensing data (UL): [5ms]
Environment sensing data UL:
[10 – 50 Mbps]
Pose-to-visual lag: [50ms] (pose UL, visual DL)
Visual-to-pose lag: [20ms] (visual DL, pose UL)
Haptic (DL): [5ms]
Haptic DL:
[0.25 – 160 kbps]
for parametric compressed format
[up to 6400 kbps]
for sample format.
See. TR 26.854 [165] Table 5.4-1.
Audio-to-pose lag: [50ms]
Pose-to-audio lag: [20ms]
NOTE 1: Synchronization threshold values vary for active versus passive engagement scenarios. Scenarios other than the ones listed may require other synchronization thresholds for the same media combinations.
NOTE 2: “Media X to media Y lag” refers to the positive time difference between the reference media X component and the specified media Y component. For example, an “audio-to-haptics lag” of 25ms means that haptics media arriving within 25ms after the audio is acceptable.
NOTE 3: Delay, Packet loss, Update rate, Packet size and Throughput for each media type based on TR 26.854 [165] Table 10.3-1.
[[SUGGESTION_START]]Editor’s Note: “UC 9.12 A”, the "A" has no technical meaning and serves solely to maintain correspondence with the respective use case and NOTEs during the consolidation work.[[SUGGESTION_END]]
[[SUGGESTION_START]]Editor’s Note: The word “lag” of UC 9.12 needs to be clarified.[[SUGGESTION_END]]
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]CPR y.1-2[[SUGGESTION_END]][[SUGGESTION_START]]]: The 6G system [[SUGGESTION_END]][[SUGGESTION_START]]including IMS [[SUGGESTION_END]][[SUGGESTION_START]]shall be able to provide deterministic user experience for multi-party call with the[[SUGGESTION_END]][[SUGGESTION_START]] following[[SUGGESTION_END]][[SUGGESTION_START]] KPI[[SUGGESTION_END]][[SUGGESTION_START]]:[[SUGGESTION_END]]
[[SUGGESTION_START]]Table y.1-[[SUGGESTION_END]][[SUGGESTION_START]]X[[SUGGESTION_END]][[SUGGESTION_START]]: [[SUGGESTION_END]][[SUGGESTION_START]]Performance requirements for multi-party call with deterministic user experience[[SUGGESTION_END]]
[[SUGGESTION_START]]Use case[[SUGGESTION_END]]
[[SUGGESTION_START]]Max. mouth-to-ear delay[[SUGGESTION_END]]
[[SUGGESTION_START]]Audio-video synchronisation thresholds[[SUGGESTION_END]]
[[SUGGESTION_START]]Max. duration of consecutive packet losses [[SUGGESTION_END]]
[[SUGGESTION_START]]Throughput[[SUGGESTION_END]]
[[SUGGESTION_START]](UL and DL)[[SUGGESTION_END]]
[[SUGGESTION_START]]Availability[[SUGGESTION_END]]
[[SUGGESTION_START]]UE speed[[SUGGESTION_END]]
[[SUGGESTION_START]]multi-party call[[SUGGESTION_END]]
[[SUGGESTION_START]][100ms][[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[[SUGGESTION_START]][- in the range of [125 ms to 5 ms] for audio delayed [[SUGGESTION_END]]
[[SUGGESTION_START]]- in the range of [45 ms to 5 ms] for audio advanced][[SUGGESTION_END]]
[[SUGGESTION_START]](note 2)[[SUGGESTION_END]]
[[SUGGESTION_START]][100 ms][[SUGGESTION_END]]
[[SUGGESTION_START]](note 3)[[SUGGESTION_END]]
[[SUGGESTION_START]][>=30Mbps][[SUGGESTION_END]]
[[SUGGESTION_START]](note 4)[[SUGGESTION_END]]
[[SUGGESTION_START]][99%][[SUGGESTION_END]]
[[SUGGESTION_START]](note 5)[[SUGGESTION_END]]
[[SUGGESTION_START]]up to [500km/h][[SUGGESTION_END]]
[[SUGGESTION_START]](note 6)[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 1: one-way delay [102].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 2: as defined in TS 22.261 [14] clause 7.6.1.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 3: it is referring to the capable of recovering the missing audio packets as long as 100ms, based on the assumption of 20ms voice samples encapsulated into one audio packet [101].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 4: it is derived based on 4K 60 fps video encoded with HEVC [103].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 5: it means the probability to provide the above KPIs during the time that a user intends to use the above services.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 6: it is to consider the high-speed train scenario as in TS 22.261 [14] clause 7.1, which is intended as a targeted value and not a strict requirement.[[SUGGESTION_END]]
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]CPR y.1-3[[SUGGESTION_END]][[SUGGESTION_START]]] The 6G system shall be able to provide deterministic low-latency [[SUGGESTION_END]][[SUGGESTION_START]]for immersive audio production [[SUGGESTION_END]][[SUGGESTION_START]]with the KPI requirements summarized below:[[SUGGESTION_END]]
[[SUGGESTION_START]]Table [[SUGGESTION_END]][[SUGGESTION_START]]y.1-X [[SUGGESTION_END]][[SUGGESTION_START]]: Performance requirements for immersive audio production [[SUGGESTION_END]][[SUGGESTION_START]]w[[SUGGESTION_END]][[SUGGESTION_START]]ith [[SUGGESTION_END]][[SUGGESTION_START]]deterministic low-latency[[SUGGESTION_END]]
[[SUGGESTION_START]]Use case[[SUGGESTION_END]]
[[SUGGESTION_START]](UC 9.18)[[SUGGESTION_END]]
[[SUGGESTION_START]]# of active UEs[[SUGGESTION_END]]
[[SUGGESTION_START]]UE speed
[km/h][[SUGGESTION_END]]
[[SUGGESTION_START]]service area [m²][[SUGGESTION_END]]
[[SUGGESTION_START]]Synchro-
nicity [µs][[SUGGESTION_END]]
[[SUGGESTION_START]](Note 2)[[SUGGESTION_END]]
[[SUGGESTION_START]]Max allowed end-to-end latency[[SUGGESTION_END]][[SUGGESTION_START]] [ms][[SUGGESTION_END]]
[[SUGGESTION_START]](Note 3)[[SUGGESTION_END]]
[[SUGGESTION_START]]Positioning accuracy [m][[SUGGESTION_END]]
[[SUGGESTION_START]](Note 4)[[SUGGESTION_END]]
[[SUGGESTION_START]]Min. [[SUGGESTION_END]][[SUGGESTION_START]]P[[SUGGESTION_END]][[SUGGESTION_START]]acket error rate[[SUGGESTION_END]]
[[SUGGESTION_START]]Packet size [kbit][[SUGGESTION_END]]
[[SUGGESTION_START]]User [[SUGGESTION_END]][[SUGGESTION_START]]D[[SUGGESTION_END]][[SUGGESTION_START]]ata rate UL [Mbit/s][[SUGGESTION_END]]
[[SUGGESTION_START]](Note 5)[[SUGGESTION_END]]
[[SUGGESTION_START]]User d[[SUGGESTION_END]][[SUGGESTION_START]]D[[SUGGESTION_END]][[SUGGESTION_START]]ata rate DL [Mbit/s][[SUGGESTION_END]]
[[SUGGESTION_START]](Note 6)[[SUGGESTION_END]]
[[SUGGESTION_START]]immersive audio [[SUGGESTION_END]][[SUGGESTION_START]]microphone array[[SUGGESTION_END]][[SUGGESTION_START]] in [[SUGGESTION_END]][[SUGGESTION_START]]large-scale [[SUGGESTION_END]][[SUGGESTION_START]]live [[SUGGESTION_END]][[SUGGESTION_START]]event[[SUGGESTION_END]]
[[SUGGESTION_START]]15[[SUGGESTION_END]][[SUGGESTION_START]] - [[SUGGESTION_END]][[SUGGESTION_START]]80[[SUGGESTION_END]]
[[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]n[[SUGGESTION_END]][[SUGGESTION_START]]ote 1)[[SUGGESTION_END]]
[[SUGGESTION_START]]<[[SUGGESTION_END]][[SUGGESTION_START]]50[[SUGGESTION_END]][[SUGGESTION_START]]km/h[[SUGGESTION_END]]
[[SUGGESTION_START]]<[[SUGGESTION_END]][[SUGGESTION_START]]500[[SUGGESTION_END]][[SUGGESTION_START]] m[[SUGGESTION_END]][[SUGGESTION_START]] x 500[[SUGGESTION_END]][[SUGGESTION_START]] m[[SUGGESTION_END]]
[[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]]µs[[SUGGESTION_END]] [[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]] 10[[SUGGESTION_END]][[SUGGESTION_START]]µs[[SUGGESTION_END]]
[[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]n[[SUGGESTION_END]][[SUGGESTION_START]]ote 2)[[SUGGESTION_END]]
[[SUGGESTION_START]][0.5[[SUGGESTION_END]][[SUGGESTION_START]]ms[[SUGGESTION_END]][[SUGGESTION_START]]][[SUGGESTION_END]]
[[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]n[[SUGGESTION_END]][[SUGGESTION_START]]ote 3)[[SUGGESTION_END]]
[[SUGGESTION_START]]0.5[[SUGGESTION_END]][[SUGGESTION_START]]m[[SUGGESTION_END]] [[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]] 1[[SUGGESTION_END]][[SUGGESTION_START]]m[[SUGGESTION_END]]
[[SUGGESTION_START]](note 4)[[SUGGESTION_END]]
[[SUGGESTION_START]]>[[SUGGESTION_END]][[SUGGESTION_START]]10-6[[SUGGESTION_END]]
[[SUGGESTION_START]]0.2[[SUGGESTION_END]][[SUGGESTION_START]]kbit [[SUGGESTION_END]][[SUGGESTION_START]]-2[[SUGGESTION_END]][[SUGGESTION_START]]kbit [[SUGGESTION_END]]
[[SUGGESTION_START]]5[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]] [[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]] 20[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]n[[SUGGESTION_END]][[SUGGESTION_START]]ote 5)[[SUGGESTION_END]]
[[SUGGESTION_START]]0.5[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]][[SUGGESTION_START]] - 2[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]](note 6)[[SUGGESTION_END]]
[[SUGGESTION_START]]immersive audio microphone array in [[SUGGESTION_END]][[SUGGESTION_START]]small-scale
event[[SUGGESTION_END]]
[[SUGGESTION_START]]1 [[SUGGESTION_END]][[SUGGESTION_START]]- [[SUGGESTION_END]][[SUGGESTION_START]]15[[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[[SUGGESTION_START]]<[[SUGGESTION_END]][[SUGGESTION_START]]10[[SUGGESTION_END]][[SUGGESTION_START]]km/h[[SUGGESTION_END]]
[[SUGGESTION_START]]<[[SUGGESTION_END]][[SUGGESTION_START]]50 [[SUGGESTION_END]][[SUGGESTION_START]]m [[SUGGESTION_END]][[SUGGESTION_START]]x 50[[SUGGESTION_END]][[SUGGESTION_START]] m[[SUGGESTION_END]]
[[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]]µs[[SUGGESTION_END]][[SUGGESTION_START]] - 10[[SUGGESTION_END]][[SUGGESTION_START]]µs[[SUGGESTION_END]]
[[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]n[[SUGGESTION_END]][[SUGGESTION_START]]ote 2)[[SUGGESTION_END]]
[[SUGGESTION_START]][0.5[[SUGGESTION_END]][[SUGGESTION_START]]ms[[SUGGESTION_END]][[SUGGESTION_START]]][[SUGGESTION_END]]
[[SUGGESTION_START]](note 3)[[SUGGESTION_END]]
[[SUGGESTION_START]]0.5[[SUGGESTION_END]][[SUGGESTION_START]]m[[SUGGESTION_END]] [[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]] 1[[SUGGESTION_END]][[SUGGESTION_START]]m[[SUGGESTION_END]]
[[SUGGESTION_START]](note 4)[[SUGGESTION_END]]
[[SUGGESTION_START]]>[[SUGGESTION_END]][[SUGGESTION_START]]10-6[[SUGGESTION_END]]
[[SUGGESTION_START]]0.2[[SUGGESTION_END]][[SUGGESTION_START]]kbit [[SUGGESTION_END]][[SUGGESTION_START]]-2[[SUGGESTION_END]][[SUGGESTION_START]]kbit [[SUGGESTION_END]]
[[SUGGESTION_START]]5[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]] [[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]] 20[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]](Note 5)[[SUGGESTION_END]]
[[SUGGESTION_START]]0.5[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]][[SUGGESTION_START]] - 2[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]](note 6)[[SUGGESTION_END]]
[[SUGGESTION_START]]immersive audio [[SUGGESTION_END]][[SUGGESTION_START]]single microphone[[SUGGESTION_END]][[SUGGESTION_START]] in [[SUGGESTION_END]][[SUGGESTION_START]]large-scale event[[SUGGESTION_END]]
[[SUGGESTION_START]]50 [[SUGGESTION_END]][[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]] 300[[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[[SUGGESTION_START]]<[[SUGGESTION_END]][[SUGGESTION_START]]50[[SUGGESTION_END]][[SUGGESTION_START]]km/h[[SUGGESTION_END]]
[[SUGGESTION_START]]<[[SUGGESTION_END]][[SUGGESTION_START]]500 [[SUGGESTION_END]][[SUGGESTION_START]]m [[SUGGESTION_END]][[SUGGESTION_START]]x 500[[SUGGESTION_END]][[SUGGESTION_START]] m[[SUGGESTION_END]]
[[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]]µs[[SUGGESTION_END]][[SUGGESTION_START]] - 10[[SUGGESTION_END]][[SUGGESTION_START]]µs[[SUGGESTION_END]]
[[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]n[[SUGGESTION_END]][[SUGGESTION_START]]ote 2)[[SUGGESTION_END]]
[[SUGGESTION_START]][0.5[[SUGGESTION_END]][[SUGGESTION_START]]ms[[SUGGESTION_END]][[SUGGESTION_START]]][[SUGGESTION_END]]
[[SUGGESTION_START]](note 3)[[SUGGESTION_END]]
[[SUGGESTION_START]]0.5[[SUGGESTION_END]][[SUGGESTION_START]]m[[SUGGESTION_END]] [[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]] 1[[SUGGESTION_END]][[SUGGESTION_START]]m[[SUGGESTION_END]]
[[SUGGESTION_START]](note 4)[[SUGGESTION_END]]
[[SUGGESTION_START]]>[[SUGGESTION_END]][[SUGGESTION_START]]10-6[[SUGGESTION_END]]
[[SUGGESTION_START]]0.2[[SUGGESTION_END]][[SUGGESTION_START]]kbit [[SUGGESTION_END]][[SUGGESTION_START]]-2[[SUGGESTION_END]][[SUGGESTION_START]]kbit [[SUGGESTION_END]]
[[SUGGESTION_START]]1.2[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]][[SUGGESTION_START]] - 2.5[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]](Note 5)[[SUGGESTION_END]]
[[SUGGESTION_START]]0.5[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]][[SUGGESTION_START]] - 2[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]](note 6)[[SUGGESTION_END]]
[[SUGGESTION_START]]immersive audio single microphone in [[SUGGESTION_END]][[SUGGESTION_START]]small-scale
event[[SUGGESTION_END]]
[[SUGGESTION_START]]4 [[SUGGESTION_END]][[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]] 50[[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[[SUGGESTION_START]]<[[SUGGESTION_END]][[SUGGESTION_START]]10[[SUGGESTION_END]][[SUGGESTION_START]]km/h[[SUGGESTION_END]]
[[SUGGESTION_START]]<[[SUGGESTION_END]][[SUGGESTION_START]]50 [[SUGGESTION_END]][[SUGGESTION_START]]m [[SUGGESTION_END]][[SUGGESTION_START]]x 50[[SUGGESTION_END]][[SUGGESTION_START]] m[[SUGGESTION_END]]
[[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]]µs[[SUGGESTION_END]][[SUGGESTION_START]] - 10[[SUGGESTION_END]][[SUGGESTION_START]]µs[[SUGGESTION_END]]
[[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]n[[SUGGESTION_END]][[SUGGESTION_START]]ote 2)[[SUGGESTION_END]]
[[SUGGESTION_START]][0.5[[SUGGESTION_END]][[SUGGESTION_START]]ms[[SUGGESTION_END]][[SUGGESTION_START]]][[SUGGESTION_END]]
[[SUGGESTION_START]](note 3)[[SUGGESTION_END]]
[[SUGGESTION_START]]0.5[[SUGGESTION_END]][[SUGGESTION_START]]m[[SUGGESTION_END]] [[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]] 1[[SUGGESTION_END]][[SUGGESTION_START]]m[[SUGGESTION_END]]
[[SUGGESTION_START]](note 4)[[SUGGESTION_END]]
[[SUGGESTION_START]]>[[SUGGESTION_END]][[SUGGESTION_START]]10-6[[SUGGESTION_END]]
[[SUGGESTION_START]]0.2[[SUGGESTION_END]][[SUGGESTION_START]]kbit [[SUGGESTION_END]][[SUGGESTION_START]]-2[[SUGGESTION_END]][[SUGGESTION_START]]kbit [[SUGGESTION_END]]
[[SUGGESTION_START]]1.2[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]][[SUGGESTION_START]] - 2.5[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]](Note 5)[[SUGGESTION_END]]
[[SUGGESTION_START]]0.5[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]][[SUGGESTION_START]] - 2[[SUGGESTION_END]][[SUGGESTION_START]]Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]](note 6)[[SUGGESTION_END]]
[[SUGGESTION_START]]N[[SUGGESTION_END]][[SUGGESTION_START]]OTE[[SUGGESTION_END]][[SUGGESTION_START]] 1: the figures are estimated assuming a steady increase in the use of wireless microphones based on the development of the last decades[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE[[SUGGESTION_END]][[SUGGESTION_START]] 2: according to [363].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE[[SUGGESTION_END]][[SUGGESTION_START]] 3: more stringent values compared to TS 22.263 [67], Table 6.2.1-1 because immersive audio adds latency for encoding in the range of several hundred µs compared to conventional compression methods.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE[[SUGGESTION_END]][[SUGGESTION_START]] 4: estimated range based on experience with current immersive audio productions, sufficient to reproduce immersive audio images.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE[[SUGGESTION_END]][[SUGGESTION_START]] 5: range from uncompressed audio with 24 bit / 48 kHz (resulting in approximately 1.2 Mbit/s per transducer including some overhead and metadata) up to 24 bit / 96 kHz (resulting in approx. 2.5 Mbit/s per transducer including some overhead and metadata). For immersive audio, encoding of uncompressed audio streams is needed to maintain the required audio quality at production side. UE device type A contains a minimum of 4 transducers and a maximum of 8 transducers with a data rate of 2.5 Mbit/s, or a maximum of 16 transducers with a data rate of 1.2 Mbit/s.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE[[SUGGESTION_END]][[SUGGESTION_START]] 6: DL is a binaural or stereo mix in the range from compressed audio (approximately 500 kbit/s) up to uncompressed audio with 16 bit / 48 kHz (resulting in 1.53 Mbit/s + some overhead = approximately 2 Mbit/s) for the use in IEMs. Similar values can be found in [67].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE: This table is derived from Table 6.2.1-1 of TS 22.263 [67] but specifies more stringent values for E2E latency and user data rates and specifies the additional KPIs synchronicity and position accuracy as this is required for immersive audio production.[[SUGGESTION_END]]
[CPR y.1-[[SUGGESTION_START]]4[[SUGGESTION_END]]] Subject to operator policy, the 6G system including IMS shall support the synchronization of independent traffic flows of one or more applications, to be delivered to more than one device (i.e. UE or tethered devices).
[[SUGGESTION_START]]NOTE: It is assumed that there is association between the applications whose traffic flows are synchronized and that the association is known to the 6G system.[[SUGGESTION_END]]
Table y.1-[[SUGGESTION_START]]X[[SUGGESTION_END]]: Performance requirements for media synchronization for multiple applications
Use case
Audio-Haptic synchronization thresholds
Video- Haptic synchronization thresholds
Audio-video synchronisation thresholds
Remotely controlled repair
- In the range of [50ms to 0ms] for audio delayed (NOTE 1)
- In the range of [25ms to 0ms] for audio advanced (NOTE 1)
- In the range of [15ms to 0ms] for video delayed (NOTE 1)
- In the range of [50ms to 0ms] for video advanced (NOTE 1)
- In the range of [125ms to 5ms] for audio delayed (NOTE 2)
- In the range of [45ms to 5ms] for audio advanced] (NOTE 2)
NOTE 1: as defined in TS 22.261 [14] clause 6.43.1.
NOTE 2: as defined in TS 22.261 [14] clause 7.6.1.
* * * End of changes * * *
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S1-260041.zip
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2026-01-21T09:27:53.073893
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S1-260042
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SA1
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TSGS1_112-Ad_Hoc-e
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pCR
|
available
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Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
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3GPP TSG SA WG 1 Meeting #112 Ad Hoc - e S1-260042
12-16 January 2026, electronic meeting (revision of S1-26xxxx)
Source: Telefonica (Moderator)
pCR Title: Consolidation of KPI requirements on ISAC
Draft Spec: 3GPP TR 22.870 1.0.1 r1
Agenda item: 1.4
Document for: Approval
Contact: Jesus Martin, jesusmaria dot martingarcia at telefonica dot com
Abstract: Update of the KPI table of Consolidated performance requirements for ISAC
1. Introduction
Based on the approved pCRs from SA1#112, this contribution provides an update of the KPI table of Consolidated performance requirements for Integrated Sensing and Communication. Another KPI table of Consolidated performance requirements is included for the scenario of Network assisted transportation.
2. Reason for Change
As requested by the SA1 Chairman for KPIs consolidation, moderators need to update their KPI tables with new input from SA1#112
3. Conclusions
<Conclusion part (optional)>
4. Proposal
It is proposed to approve the following changes to 3GPP TR 22.870.
* * * First Change * * * *
All new text
14.2.2 Consolidated performance requirements for Integrated Sensing and Communication
The 6G system shall be able to provide sensing with the following performance requirements:
NOTE: The definitions of the terms used in the following KPI tables are defined in [6].
Table 14.2.2-1: Consolidated performance requirements for Integrated Sensing and Communication.
Scenario
Sensing service category
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Sensing area (sensing target and/or description)
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
High topology mapping
(NOTE 1)
N/A
0.10
0.10
-
[0.4]
-
50
≤ 0.2
≤10
<1
Outdoor
Low-altitude UAV supervision
≥90
1-2
1-2
3-5
3-5
N/A
N/A
100~1000
≤1
≤5
≤5
Outdoor (UAV trajectory tracking)
≥95
≤10
≤10
N/A
N/A
[10]
[≥5]
[≤1000]
[≤1]
[≤5]
[≤5]
Outdoor (UAV intrusion detection)
Environment object reconstruction
95
[0.5-5]
(NOTE 3)
[0.5-5]
(NOTE 3)
N/A
N/A
[0.5]
N/A
[10,000-600,000]
[10-60]
[1-5]
[1-5]
Outdoor (Building (NOTE 2) as in Table 7.6.1-1)
0.5
0.5
1.5
N/A
[0.5]
[0.5]
100
0.1
5
5
Outdoor (Vehicle (NOTE 2) as in Table 7.6.1-1)
Road digitalization
0.5 (vehicle)
(NOTE 4)
1 (pedestrian)
N/A
0.5
N/A
0.2
0.2
m/s
1~5
≤ 0.1
≤ 5
≤ 5
Outdoor (crossroad, as in Table 7.7.1-1) (NOTE 5)
3 (NOTE 4)
N/A
3
N/A
1
1 m/s
1~5
≤ 0.1
≤ 5
≤ 5
Outdoor (highway, as in Table 7.7.1-1) (NOTE 6)
Ship detection and tracking
(NOTE 7)
≤100, (NOTE 8)
NA
NA
NA
≤100
NA
5000
1
5
5
Outdoor, (Detection of ship on the near shore waters and offshore waters)
≤10
(NOTE 8)
NA
NA
NA
≤10
NA
5000
1
5
5
Outdoor, (Detection of ship on the harbour entrances, harbour approaches, and port. and coastal waters)
≤2~10
(NOTE 9)
NA
NA
NA
≤2
NA
5000
1
5
5
Outdoor, (Detection of ship on the inland waterway (e.g. river, lake))
Structural health monitoring
0.1,
(NOTE 19)
0.1
N/A
N/A
N/A
N/A
5000
60
5
5
Outdoor (e.g. detection of corner reflectors on bridges, buildings in urban scenario)
UAV detection, classification and counting
1
(NOTE 20)
1
(NOTE 20)
1
(NOTE 20)
1
(NOTE 20)
[0.3 – 1]
(NOTE 20)
1
(NOTE 20)
≤1000
(NOTE 20)
≤1
(NOTE 20)
2
(NOTE 20)
2
(NOTE 20)
Outdoor
Safety Assistance for vulnerable pedestrian
[95]
[1]
N/A
[0.5]
N/A
[0.2]
[0.5] x N/A
≤ [500]
≤ [0.1]
≤ [5]
≤ [5]
Outdoor (Crossing)
Collaborative Robots (NOTE 12) (NOTE 24)
[≤ 0.1]
[≤ 0.1]
N/A
Indoor/outdoor
Infrastructure collapse monitoring
[4]
[N/A]
[N/A]
[N/A]
[4]
[N/A]
TBD
[ 1]
[1~2]
[1~2]
Outdoor (e.g. Detecting sudden collapse on infrastructure such as highway, railway, road, flyover, rural areas, farmland)
UAV takeoff and landing (NOTE 24)
[0.1]
(NOTE 13)
[0.1]
(NOTE 13)
[1]
[1]
[0.5]
[1]
[100-500]
[0.2]
[5]
[5]
Outdoor (NOTE 14)
Gestures Recognition
(NOTE 23) (NOTE 24)
99
0.1[[SUGGESTION_START]] (NOTE 30)[[SUGGESTION_END]]
0.1[[SUGGESTION_START]] (NOTE 30)[[SUGGESTION_END]]
n/a
n/a
[[SUGGESTION_START]][0.3 to 0.5][[SUGGESTION_END]][[SUGGESTION_START]] (NOTE 30) [[SUGGESTION_END]]
n/a
≤20
≤0.02
≤ 1% (NOTE 22)
≤ 10% (NOTE 22)
Indoor, Factory Environment, (hand gesture)[[SUGGESTION_START]] (NOTE 31)[[SUGGESTION_END]]
[0.1]
[0.1]
n/a
n/a
[≤0.002]
n/a
≤20
[≤0.02]
≤ 1% (NOTE 22)
≤ 10% (NOTE 22)
Indoor, Factory Environment, (facial gesture)
0.1[[SUGGESTION_START]] (NOTE 30)[[SUGGESTION_END]]
0.1[[SUGGESTION_START]] (NOTE 30)[[SUGGESTION_END]]
[[SUGGESTION_START]][0.3][[SUGGESTION_END]]
[[SUGGESTION_START]](note 6)[[SUGGESTION_END]]
[[SUGGESTION_START]][0.3][[SUGGESTION_END]]
[[SUGGESTION_START]](note 6)[[SUGGESTION_END]]
[[SUGGESTION_START]][0.1][[SUGGESTION_END]][[SUGGESTION_START]] (NOTE 30) [[SUGGESTION_END]]
[[SUGGESTION_START]][0.3][[SUGGESTION_END]]
[[SUGGESTION_START]]([[SUGGESTION_END]][[SUGGESTION_START]]NOTE 33[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
≤20
≤0.02
≤ 1% (NOTE 22)
≤ 10% (NOTE 22)
Indoor, Factory Environment, (head gesture)[[SUGGESTION_START]] (NOTE 32)[[SUGGESTION_END]]
0.1[[SUGGESTION_START]] (NOTE 30)[[SUGGESTION_END]]
0.1[[SUGGESTION_START]] (NOTE 30)[[SUGGESTION_END]]
n/a
n/a
0.1[[SUGGESTION_START]] (NOTE 30)[[SUGGESTION_END]]
n/a
≤20
≤0.02
≤ 1% (NOTE 22)
≤ 10% (NOTE 22)
Indoor, Factory Environment, (body and limb gesture)
[[SUGGESTION_START]]Robots collaborating[[SUGGESTION_END]][[SUGGESTION_START]] in Sensing[[SUGGESTION_END]][[SUGGESTION_START]]. Sensing of Surroundings (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]6[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]](NOTE [[SUGGESTION_END]][[SUGGESTION_START]]29[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]0.1 (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]7[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]n/a (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]7[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]0.1[[SUGGESTION_END]]
[[SUGGESTION_START]]n/a[[SUGGESTION_END]]
[[SUGGESTION_START]][0.5 to 1] (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]7[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]1.5[[SUGGESTION_END]]
[[SUGGESTION_START]]≤100[[SUGGESTION_END]]
[[SUGGESTION_START]]0.1[[SUGGESTION_END]]
[[SUGGESTION_START]]< 10% (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]5[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]< 10% (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]5[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]Indoor, Factory Environment[[SUGGESTION_END]]
[[SUGGESTION_START]]Robots collaborating[[SUGGESTION_END]][[SUGGESTION_START]] in Sensing[[SUGGESTION_END]][[SUGGESTION_START]]. Detection of Pathways, Openings (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]6[[SUGGESTION_END]][[SUGGESTION_START]]) (NOTE [[SUGGESTION_END]][[SUGGESTION_START]]29[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]0.25[[SUGGESTION_END]]
[[SUGGESTION_START]](NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]7[[SUGGESTION_END]][[SUGGESTION_START]]) (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]8[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]0.25[[SUGGESTION_END]]
[[SUGGESTION_START]](NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]7[[SUGGESTION_END]][[SUGGESTION_START]]) (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]8[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]0.1[[SUGGESTION_END]]
[[SUGGESTION_START]]n/a[[SUGGESTION_END]]
[[SUGGESTION_START]][0.75] (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]7[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]1.5[[SUGGESTION_END]]
[[SUGGESTION_START]]≤100[[SUGGESTION_END]]
[[SUGGESTION_START]]0.1[[SUGGESTION_END]]
[[SUGGESTION_START]]< 10% (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]5[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]< 10% (NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]5[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]Indoor, Factory Environment[[SUGGESTION_END]]
Enhanced XR navigation
99.9
(NOTE 11)
≤0.5
(NOTE 10)
0.5
(NOTE 10)
0.5
N/A
0.1- 1
(NOTES 10, 11)
0.5
(NOTE 10)
≤100
0.1 – 1
(NOTE 11)
1%
1%
Indoor, Factory Environment, (10 m2)
Safe and economic UAV transport
99.99
≤ 50
50 (NOTE 15)
5 (NOTE 15)
5
10 (NOTE 15)
0.5 (NOTE 15)
≤ 100
0.1 – 1
0.1%
(NOTES 16, 17)
1%
(NOTES 16)
Outdoor (NOTE 18)
NOTE 1: The accuracy KPIs in Scenario “High Topology mapping” are extrapolated from [323] Table C2 as considered for autonomous navigation and remote driving.
NOTE 2: A sensing target in Scenario “Environment object reconstruction” refers to a [segment/part] of the target object to be detected and/or tracked, whereas the size of each part is comparable to corresponding spatial resolution of object reconstruction. The percentage of missed detection/false alarm represent missed/falsely reconstruction of parts of the object statistically
NOTE 3: Considering a variety of dimensions, shapes, and functionalities of urban buildings, a range of KPI values are needed to measure and provide the flexibility of reconstruction accuracy.
NOTE 4: Vehicle, with the assumptions in Table 7.7.1-1.
NOTE 5: Sensing target density for vehicles ≤ 750 per 1000 m x 24 m; sensing target density for pedestrians ≤100 per 1000 m x 24 m. Sensing target density is described in Clause 3.1 of the present document.
NOTE 6: Sensing target density for vehicles ≤ 72 per 1000 m x 24 m. Sensing target density is described in Clause 3.1 of the present document.
NOTE 7: The typical size (Length x Width x Height) of ship is 270 m x 30 m x 30 m in both sensing service category#1 and #2, and 100 m x 15 m x 15 m in sensing service category #3, according to reference [91]
NOTE 8: The KPI values for detection of ship on these sensing service categories are referred to [192].
NOTE 9: The KPI values for detection of ship on this sensing service category are referred to [193].
NOTE 10: This positioning accuracy and sensing resolution are required for the detection of proximal objects within a safety area of the user
NOTE 11: The KPIs are based on the Hazard Prevention in Industrial Environments for “Prediction of workers’ and machines’ actions” and “Detection of worker’s location” in [94]
NOTE 12: Related usecases are captured in clause 7.2 of 3GPP TR 22.837 [9] and clause 6.2 of 3GPP TS 22.137 [6].
NOTE 13: The KPI values for accuracy of positioning are centimetre-level sourced from reference [199], [200]
NOTE 14: The KPI values of this use case are effective for sensing in specific area for UAV takeoff and landing.
NOTE 15: The listed positioning accuracy and sensing resolution are required for the detection of proximal objects within a safety area of the UAV and validate the reported location of the UAV itself.
NOTE 16: Safety impact of missed detection is substantially higher compared to the false alarm, here fore the Missed Detection is assigned a more stringent value compared to the False Alarm.
NOTE 17: To enable safe operation of BVLOS UAV flights in mixed airspace (manned & unmanned) and in high risk areas (e.g. above cities) the value for missed detection of 0.1% corresponds to the lower value of the missed detection range specified in Table 6.2-1 of TS 22.137 [6] for sensing service category 2.
NOTE 18: Sensing service availability [%] is 99.999
NOTE 19: The typical length of corner reflector is 0.5m [327].
NOTE 20: Most of the requirements presented here are related to the sensing category 3 in [6]
NOTE 21: Detection, classification of UAV features such as propellers may require more stringent velocity and range resolution due to the dimensions of propellers with respect to the UAVs. Some propeller diameters to UAV body ratio could be between ½ to 1/3 [330], therefore, the range resolution values are modified accordingly.
NOTE 22: Depending on the actual application to be controlled by the gestures, Missed detection and False alarm may have different KPI values from < 1% to 10%.
NOTE 23: References for hand gesture recognition are [331][332], for facial and head gesture recognition [333], and for body and limb gesture recognition [334][335].
NOTE 24: The relative distance measurement obtained through 3GPP Sensing is referenced to the surface of the sensed object (e.g., the closest physical boundary of a neighboring vehicle, robot, or some obstacle) from the Reference Point of Sensing (RPS) on the reference entity (e.g., regular UE, vehicle, and robot).
[[SUGGESTION_START]]NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]5[[SUGGESTION_END]][[SUGGESTION_START]]: Only valid for non-time-critical applications, < 1% if integrated in time-critical processes.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 2[[SUGGESTION_END]][[SUGGESTION_START]]6[[SUGGESTION_END]][[SUGGESTION_START]]: A maximum sensing range of 10 m is required.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]27[[SUGGESTION_END]][[SUGGESTION_START]]: In this table, accuracy of positioning estimate is the horizontal or vertical minimum distance between distinguishable objects, range resolution is the minimum distance between distinguishable objects regarding depth sensing.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]28[[SUGGESTION_END]][[SUGGESTION_START]]: Operational practice of autonomous mobile devices (AMRs, AGVs) in industrial settings requires openings on the path to be at least 50 cm wider/taller than the width/height of the devices. The KPI value is half of this additional width/height.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]29[[SUGGESTION_END]][[SUGGESTION_START]]: The sensing KPIs are derived from discussions with application experts. Physical realization has been checked.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 30: In this table, accuracy of positioning estimate is the horizontal or vertical minimum distance between distinguishable objects, range resolution is the minimum distance between distinguishable objects regarding depth sensing.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 31: The sensing KPIs for Hand Gestures Recognition are derived from being able to distinguish the position of a hand.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 32: The sensing KPIs for Head Gestures Recognition are derived from different Head Gestures such as shaking, nodding, or turning one’s head.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 33: Detection of intentional movement of one’s head (e.g. intentional shaking or nodding)[[SUGGESTION_END]]
Table 14.2.2-2: Consolidated performance requirements for Integrated Sensing and Communication in Network assisted transportation
Communication KPIs
Spatial KPIs
Scenario
User experienced data rate
[[SUGGESTION_START]]End-to-end[[SUGGESTION_END]] latency
Communication Service availability
Connection density
Location accuracy
Sensing accuracy
Network assisted smart transportation
[1-10 Mb/s]
[40 ms]
99.99%
(NOTE 1)
104 devices/km2
[1m] * [1m] * [1m] with 90% probability
(NOTE 2)
category 2 or 3
(NOTES 2, 3)
NOTE 1: within service volume
NOTE 2: within 99% of the service volume
NOTE 3: Category 2 or 3 in Table 6.2-1 in TS 22.137 [6]
NOTE 4: KPIs for communication, location and sensing in this table are fulfilled simultaneously.
[[SUGGESTION_START]]Table 7.[[SUGGESTION_END]][[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]][[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]]3[[SUGGESTION_END]][[SUGGESTION_START]]:[[SUGGESTION_END]][[SUGGESTION_START]] Communication service p[[SUGGESTION_END]][[SUGGESTION_START]]erformance requirements for robots collaborating in sensing in smart factories[[SUGGESTION_END]]
[[SUGGESTION_START]]Characteristic parameter[[SUGGESTION_END]]
[[SUGGESTION_START]]Influence quantity[[SUGGESTION_END]]
[[SUGGESTION_START]]CS availability: target value (%)[[SUGGESTION_END]]
[[SUGGESTION_START]]CS reliability: mean time between failures[[SUGGESTION_END]]
[[SUGGESTION_START]]End-to-end latency: maximum[[SUGGESTION_END]]
[[SUGGESTION_START]]Service bit rate: user experienced data rate[[SUGGESTION_END]]
[[SUGGESTION_START]]Message size [byte][[SUGGESTION_END]]
[[SUGGESTION_START]]Transfer interval: target value[[SUGGESTION_END]]
[[SUGGESTION_START]]Survival time[[SUGGESTION_END]]
[[SUGGESTION_START]]UE
speed[[SUGGESTION_END]]
[[SUGGESTION_START]]# of UEs[[SUGGESTION_END]]
[[SUGGESTION_START]]Service area[[SUGGESTION_END]]
[[SUGGESTION_START]]Remarks[[SUGGESTION_END]]
[[SUGGESTION_START]]99.999[[SUGGESTION_END]]
[[SUGGESTION_START]]4 h[[SUGGESTION_END]]
[[SUGGESTION_START]]< 20 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]Sensing Result: 1 Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]Sensing Data: 2.5 Gbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]15 kByte[[SUGGESTION_END]]
[[SUGGESTION_START]]50 kByte[[SUGGESTION_END]]
[[SUGGESTION_START]]100 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]Not con-sidered[[SUGGESTION_END]]
[[SUGGESTION_START]]1.5 m/s[[SUGGESTION_END]]
[[SUGGESTION_START]]≤ 10[[SUGGESTION_END]]
[[SUGGESTION_START]]100 m x 50 m x 10 m[[SUGGESTION_END]]
[[SUGGESTION_START]]Sensing Information: Robot third party application[[SUGGESTION_END]]
[[SUGGESTION_START]]99.999[[SUGGESTION_END]]
[[SUGGESTION_START]]4 h[[SUGGESTION_END]]
[[SUGGESTION_START]]5 ms to 10 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]10 Mbit/s per video camera stream[[SUGGESTION_END]]
[[SUGGESTION_START]]4 MByte[[SUGGESTION_END]]
[[SUGGESTION_START]]33 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]Not con-sidered[[SUGGESTION_END]]
[[SUGGESTION_START]]1.5 m/s[[SUGGESTION_END]]
[[SUGGESTION_START]]≤ 10[[SUGGESTION_END]]
[[SUGGESTION_START]]100 m x 50 m x 10 m[[SUGGESTION_END]]
[[SUGGESTION_START]]Video Sensing: (Robot third party application)[[SUGGESTION_END]]
[[SUGGESTION_START]]99.999[[SUGGESTION_END]]
[[SUGGESTION_START]]4 h[[SUGGESTION_END]]
[[SUGGESTION_START]]5 ms to 10 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]10 Mbit/s per stream[[SUGGESTION_END]]
[[SUGGESTION_START]]1 MByte to 5 MByte[[SUGGESTION_END]]
[[SUGGESTION_START]]100 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]Not con-sidered[[SUGGESTION_END]]
[[SUGGESTION_START]]1.5 m/s[[SUGGESTION_END]]
[[SUGGESTION_START]]≤ 10[[SUGGESTION_END]]
[[SUGGESTION_START]]100 m x 50 m x 10 m[[SUGGESTION_END]]
[[SUGGESTION_START]]Application data derived from aggregated sensing results: (third party application Robots)[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE: References are for communication between robot and third party application [y2] and for video sensing [y1].[[SUGGESTION_END]]
* * * End of changes * * * *
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S1-260042.zip
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2026-01-21T09:28:21.959506
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S1-260043
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
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Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG SA WG 1 Meeting #112 Ad Hoc - e S1-260043
12-16 January 2026, electronic meeting (revision of S1-254327)
Source: Nokia (Moderator)
pCR Title: Consolidation of KPI requirements on Ubiquitous Connectivity
Draft Spec: 3GPP TR 22.870
Agenda item: 1.4
Document for: Approval
Contact: feifei.lou@nokia.com
Abstract: this contribution provides an update of the performance requirements consolidation on ubiquitous connectivity, based on the approved pCRs from SA1#112.
1. Introduction
The consolidated performance requirements for clause 8 are categorised into two parts.
The consolidated performance requirements for positioning services covers the following use cases and PRs. The scenarios in the KPI table are sorted from less demanding to more demanding cases.
[PR 8.5.6-1] from use case on resilient positioning in satellite networks
[PR 8.7.6-2] from use case on low-energy positioning in satellite networks (updated in SA1#112)
[PR 8.10.6-1] from use case on hybrid TN and NTN positioning
[PR 8.11.6-2] from use case on hybrid NTN and GNSS positioning
The consolidated performance requirements for NTN covers the following use cases and PRs. Based on SA1#112 discussion, all of the KPIs in TS 22.261 Table 7.4.2-1 Performance requirements for satellite access are included.
[PR 8.2.6-1] from use case on ubiquitous and resilient network (EN addressed in SA1#112)
[PR 8.4.6-2] from use case on service continuity for wearable mobile devices
[PR 8.6.6-2] from use case on disaster relief
[PR 8.8.6-2] from use case on global mobile video (updated in SA1#112)
[PR 8.9.6-2] from use case on low-altitude logistics supported by NTN
[PR 11.5.6-1] from use case on immersive media services for advanced air mobility (AAM) enabled by 6G NTN (from industry and verticals)
[PR 11.6.6-1] from use case on high-rate aircraft communication services in 6G (from industry and verticals)
2. Summary of changes
The update is from the following PRs, including the performance requirements for satellite access for UAM and Airplane from industry and verticals.
[PR 8.7.6-2]
[PR 8.2.6-1]
[PR 8.8.6-2]
[PR 11.5.6-1]
[PR 11.6.6-1]
3. Proposal
It is proposed to approve the following changes to 3GPP TR 22.870.
* * * First Change * * * *
All new texts
[[SUGGESTION_START]]Y.2[[SUGGESTION_END]].x Consolidated performance requirements for Ubiquitous Connectivity
[[SUGGESTION_START]]Y.2[[SUGGESTION_END]].x.1 Consolidated performance requirements for positioning services
Table [[SUGGESTION_START]]Y.2[[SUGGESTION_END]].x.1-1: Consolidated performance requirements for positioning services
Scenario
Accuracy
(95 % confidence level)
Positioning service availability
Positioning service latency
[[SUGGESTION_START]]Positioning service area/[[SUGGESTION_END]]Environment of use
MAX UE speed
UE type
Others
Horizontal Accuracy
Vertical Accuracy
Containers
[100] m
N/A
[95] %
N/A
Outdoor
[37] km/h
Container mounted
Battery life
expectancy
(note 1)
12 years
Pallets
[100] m
N/A
[95] %
N/A
Outdoor
[100] km/h
Vehicle mounted
Battery life
expectancy
(note 1)
7 years
Wagons
[100] m
N/A
[95] %
N/A
Outdoor
[350] km/h
Train mounted
Battery life
expectancy
(note 1)
20 years
Airplane en-route (note 2)
[50] m
[50] m
[99] %
[1] s
Outdoor
[1500] km/h
Airplane mounted
-
Airplane landing (note 2)
[10] m
[10] m
[99] %
[1] s
Outdoor
[350] km/h
Airplane mounted
-
UE with partly obstructed sky
[10] m
[3] m
[95] %
[1] s
Outdoor rural and urban
[250] km/h
Handheld, vehicle mounted or IoT
-
[[SUGGESTION_START]]Agriculture & farming (e.g. livestock, equipment tracking)[[SUGGESTION_END]]
[[SUGGESTION_START]][5-10] m [376][[SUGGESTION_END]]
[[SUGGESTION_START]][95 %][[SUGGESTION_END]]
[[SUGGESTION_START]]Outdoor (Rural/remote areas)[[SUGGESTION_END]]
[[SUGGESTION_START]][30] km/h[[SUGGESTION_END]]
[[SUGGESTION_START]]Animal mounted (e.g. collar) or passive asset/equipment mounted (e.g. non-powered harvesting crates, trailers, wheelbarrows)[[SUGGESTION_END]]
[[SUGGESTION_START]]Battery life[[SUGGESTION_END]]
[[SUGGESTION_START]]expectancy[[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[[SUGGESTION_START]]10 years[[SUGGESTION_END]]
Maritime
[3] m
[3] m
[99] %
[1] s
Outdoor rural
[500] km/h
Maritime mounted
-
UAV facility monitoring (note 3)
[3] m
[3] m
[99] %
[0,1[[SUGGESTION_START]]-[[SUGGESTION_END]]0,5] s
Outdoor
[160] km/h
UAV mounted
-
UAV positioning (note 2)
[1] m
[1] m
[99] %
[1] s
Outdoor
[160] km/h
UAV mounted
-
Airplane taxiway (note 3, 4)
[1] m
N/A
[99] %
[0,1[[SUGGESTION_START]]-[[SUGGESTION_END]]0,5] s
Outdoor
[100] km/h
Airplane mounted
-
NOTE 1: Battery life expectancy is to be assumed in all coverage conditions and is based on typical message size value and typical frequency.[[SUGGESTION_START]] The frequency assumed here is the number of times per day a device is involved in a positioning operation using terrestrial/6G Satellite Positioning, with the required accuracy anywhere within the required coverage area, which could be followed by a communication request from the UE to send this position into a message to a third party application server, unless this information can be obtained from the 6G network itself. In this table, frequency is assumed to be 1h (that is approximately 24 operations per day) and typical message size around 200 bytes.[[SUGGESTION_END]]
NOTE 2: Positioning services are provided with 3GPP technologies, independently of non-3GPP positioning technologies (e.g. GNSS). Multiple satellites can be used to support 3GPP positioning technologies.
NOTE 3: Requirements for Airplane taxiway and UAV facility monitoring are in [25].
NOTE 4: Airplane taxiway refers to an airplane on ground during taxi operations.
Editor’s Note: maritime mounted UE speed is FFS.
[[SUGGESTION_START]]Y.2[[SUGGESTION_END]].x.2 Consolidated performance requirements for [[SUGGESTION_START]]NTN[[SUGGESTION_END]]
[[SUGGESTION_START]]Table Y.2.x.2-1: Consolidated performance requirements for NTN[[SUGGESTION_END]]
[[SUGGESTION_START]]Scenario[[SUGGESTION_END]]
[[SUGGESTION_START]]Experienced data rate[[SUGGESTION_END]]
[[SUGGESTION_START]]Area traffic capacity[[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[[SUGGESTION_START]]Overall user density [[SUGGESTION_END]]
[[SUGGESTION_START]]Activity factor[[SUGGESTION_END]]
[[SUGGESTION_START]]UE speed[[SUGGESTION_END]]
[[SUGGESTION_START]]UE type[[SUGGESTION_END]]
[[SUGGESTION_START]]Reliability[[SUGGESTION_END]]
[[SUGGESTION_START]]Service availability[[SUGGESTION_END]]
[[SUGGESTION_START]]End-to-end latency[[SUGGESTION_END]]
[[SUGGESTION_START]]Others[[SUGGESTION_END]]
[[SUGGESTION_START]]Disaster [[SUGGESTION_END]]
[[SUGGESTION_START]]relief[[SUGGESTION_END]]
[[SUGGESTION_START]](note 2)[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]Up to [100] devices/km2 for SMS only[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]3 km/h[[SUGGESTION_END]]
[[SUGGESTION_START]]Handheld[[SUGGESTION_END]]
[[SUGGESTION_START]][99,9-99,999] %[[SUGGESTION_END]]
[[SUGGESTION_START]]Up to 99,9 %[[SUGGESTION_END]]
[[SUGGESTION_START]]Up to [600] ms[[SUGGESTION_END]]
[[SUGGESTION_START]]Peak data rate[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: Up to [20] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: Up to [2] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]Ubiquitous and Resilient Network[[SUGGESTION_END]]
[[SUGGESTION_START]](note 3)[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: [0,1-25] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [2] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]](note 4)[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]][0,1] devices/m2[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]up to [120] km/h[[SUGGESTION_END]]
[[SUGGESTION_START]]Handheld[[SUGGESTION_END]]
[[SUGGESTION_START]][99.9-99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][98.5] %[[SUGGESTION_END]]
[[SUGGESTION_START]][10-100] ms[[SUGGESTION_END]]
[[SUGGESTION_START]](note 4)[[SUGGESTION_END]]
[[SUGGESTION_START]][10-500] ms[[SUGGESTION_END]]
[[SUGGESTION_START]]Coverage [99.9] %[[SUGGESTION_END]]
[[SUGGESTION_START]]Global video service in remote area[[SUGGESTION_END]]
[[SUGGESTION_START]](note 5)[[SUGGESTION_END]]
[[SUGGESTION_START]]High video resolution DL: [2-5] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [1] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]Low video resolution DL: [2] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [250] kbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: Up to [10] kbit/s/km2[[SUGGESTION_END]]
[[SUGGESTION_START]](note 6)[[SUGGESTION_END]]
[[SUGGESTION_START]][< 0,1] person/km2[[SUGGESTION_END]]
[[SUGGESTION_START]]2%[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]Handheld[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]Global video service in deep rural area[[SUGGESTION_END]]
[[SUGGESTION_START]](note 5)[[SUGGESTION_END]]
[[SUGGESTION_START]]High video resolution DL: [2-10] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [2] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]Low video resolution[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: [2] Mbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [250] kbit/s[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: [1] Mbit/s/km2[[SUGGESTION_END]]
[[SUGGESTION_START]](note 7)[[SUGGESTION_END]]
[[SUGGESTION_START]][0,1-10] person/km2[[SUGGESTION_END]]
[[SUGGESTION_START]]4%[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]Handheld[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]N/A[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]Immersive media service via AAM/UAM[[SUGGESTION_END]]
[[SUGGESTION_START]](direction: from network to user, uncompressed) [[SUGGESTION_END]]
[[SUGGESTION_START]]DL: [[SUGGESTION_END]][[SUGGESTION_START]][1[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]]3] Gbit/s [[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]9[[SUGGESTION_END]][[SUGGESTION_START]]) (note [[SUGGESTION_END]][[SUGGESTION_START]]11[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: TBD[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]Up to 260 km/h[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]14[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]UAM mounted[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]Service interruption time[[SUGGESTION_END]]
[[SUGGESTION_START]][10] ms for 60 fps,[[SUGGESTION_END]]
[[SUGGESTION_START]][20] ms for 30 fps[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]8[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]Immersive media service via AAM/UAM[[SUGGESTION_END]]
[[SUGGESTION_START]](direction: from network to user, compressed)[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: [[SUGGESTION_END]][[SUGGESTION_START]][25] Mbit/s [[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]10[[SUGGESTION_END]][[SUGGESTION_START]]) (note [[SUGGESTION_END]][[SUGGESTION_START]]11[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: TBD[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]Up to 260 km/h[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]14[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]UAM mounted[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]Service interruption time[[SUGGESTION_END]]
[[SUGGESTION_START]][10] ms for 60 fps,[[SUGGESTION_END]]
[[SUGGESTION_START]][20] ms for 30 fps[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]8[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]Airplanes connectivity[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]12[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: [[SUGGESTION_END]][[SUGGESTION_START]]6 Gb[[SUGGESTION_END]][[SUGGESTION_START]]it/s[[SUGGESTION_END]][[SUGGESTION_START]]/plane[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: 3 G[[SUGGESTION_END]][[SUGGESTION_START]]bit/s[[SUGGESTION_END]][[SUGGESTION_START]]/plane[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]Up to [1500] km/h[[SUGGESTION_END]]
[[SUGGESTION_START]]Airplane mounted[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]13[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]-[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 1: Area capacity is averaged over a satellite beam.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 2: Positioning accuracy is low (≈ [100] m). Positioning service availability is up to [99] %. Positioning service latency is [1] s.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 3: Location accuracy is low (≈ [10] m). Positioning availability is [99] %. Positioning latency is [1] s.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 4: When using base station on board HAPS for NTN, end-to-end latency should be up to 10 ms (operating at 20 km altitude) and user experienced data [[SUGGESTION_END]]
[[SUGGESTION_START]] rate should be up to [500] Mb/s for DL and [50] Mb/s for UL to enable eMBB service [374].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 5: Reduced video quality is acceptable in the remote areas and deep rural area, see chapter 3.9.1.3 and 3.9.1.5 in [31].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 6: The DL area traffic capacity corresponds to an average user density of 0,1 person/km2.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 7: The DL area traffic capacity corresponds to an average user density of 5 persons/km2 equally shared by two operators. The area traffic is here split between two operators, with [2] Mbit/s/km2 in total.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]8[[SUGGESTION_END]][[SUGGESTION_START]]: It is assumed that the interruption time is less than a single frame (16ms for 60 fps, 32 ms for 30 fps) plus a margin of an order of millisecond (e.g. for other processing time).[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]9[[SUGGESTION_END]][[SUGGESTION_START]]: Uncompressed (0.8 M points per frame, total 300 frames are considered: i.e. 43.2 Mbits/s, 30 fps) [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]10[[SUGGESTION_END]][[SUGGESTION_START]]: Compressed (Video-based Point Cloud Compression (V-PCC), bpip (bits per input points) total 1.14 (Color 0.9, Geometry 0,11, Occupancy 0.13) are considered: 25 Mbits/s) [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]11[[SUGGESTION_END]][[SUGGESTION_START]]: It is assumed that the number of passengers using immersive media service per UAM is up to 2.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]12[[SUGGESTION_END]][[SUGGESTION_START]]: [[SUGGESTION_END]]
[[SUGGESTION_START]]Required experienced peak data rate corresponding to the aggregated passenger traffic at aircraft level[[SUGGESTION_END]]
[[SUGGESTION_START]]Based on an assumption of 450 seats, average take rate of 75% (free model) and load factor of 85%[[SUGGESTION_END]]
[[SUGGESTION_START]]Assumption of 2:1 Downlink / Uplink ratio, anticipating future usages[[SUGGESTION_END]]
[[SUGGESTION_START]]The Downlink & Uplink throughput can be achieved using one or multiple satellite links [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]13[[SUGGESTION_END]][[SUGGESTION_START]]: This airplane mounted UE is operated as a mobile base station relay, providing connectivity to normal UEs used by passengers. These KPIs enable the case of XR UEs for applications in highly dynamic environments, where UEs offload processing to an edge-cloud.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]14[[SUGGESTION_END]][[SUGGESTION_START]]: The number is based on [408], [409].[[SUGGESTION_END]]
[CPR [[SUGGESTION_START]]Y.2[[SUGGESTION_END]].x.2-1]: The 6G system with satellite access, shall support SMS delivery to UEs with up to [1000] devices/km2 density.
[CPR [[SUGGESTION_START]]Y.2[[SUGGESTION_END]].x.2-2] The 6G system with satellite access shall be able to support communication service for UEs (e.g. UAV) at the altitudes from 0 to 3 km via satellite access and/or terrestrial access.
* * * End of changes * * * *
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S1-260043.zip
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2026-01-21T09:28:44.947365
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S1-260044
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SA1
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TSGS1_112-Ad_Hoc-e
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pCR
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available
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Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
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3GPP TSG-SA WG1 Meeting #112 Ad Hoc - e S1-260044
12-16 January 2026, electronic meeting (revision of S1-26XXXX)
Title: Updated consolidation of KPI requirements on AI section
Agenda Item:
Source: Moderator (China Unicom)
Contact: Qun Wei, weiqun5@chinaunicom.cn
Abstract: Prepare and propose a way for consolidation on KPI requirements on AI section.
1. Introduction
Based on the meeting preparations, this document only provides the consolidation way forward and discussion regarding the AI KPIs, based on TR 22.870 and do not include performance requirements with editor’s note.
2. Reason for Change
To provide communication performance requirements contribution and reflect key points regarding the performance requirements of AI sections.
3. Proposal
It is proposed to agree the following changes to new version of 3GPP TR 22.870.
Discussion Part
Besides the basic template consolidation, the following comments need to be considered：
[[SUGGESTION_START]]General:[[SUGGESTION_END]]
CM1: [[SUGGESTION_START]]Table y.1-2 [[SUGGESTION_END]]the “joint e2e latency” is probably to be properly defined or decoupled?
CM2: [[SUGGESTION_START]]Table y.1-2 [[SUGGESTION_END]]Joint E2E latency value needs to be divided into a communication value and a compute value.
[[SUGGESTION_START]]CM[[SUGGESTION_END]][[SUGGESTION_START]]3[[SUGGESTION_END]][[SUGGESTION_START]]: Table y.1-[[SUGGESTION_END]][[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]] primarily consists of communication KPIs. Reflect the [[SUGGESTION_END]][[SUGGESTION_START]]table[[SUGGESTION_END]][[SUGGESTION_START]] name.[[SUGGESTION_END]]
[[SUGGESTION_START]]CM[[SUGGESTION_END]][[SUGGESTION_START]]4[[SUGGESTION_END]][[SUGGESTION_START]]: The latency title in [[SUGGESTION_END]][[SUGGESTION_START]]t[[SUGGESTION_END]][[SUGGESTION_START]]able y.1-2 need to align with “Max allowed end-to-end latency” but also need to highlight the difference from existing KPIs.[[SUGGESTION_END]]
[[SUGGESTION_START]]CM[[SUGGESTION_END]][[SUGGESTION_START]]5[[SUGGESTION_END]][[SUGGESTION_START]]:[[SUGGESTION_END]][[SUGGESTION_START]] Is t[[SUGGESTION_END]][[SUGGESTION_START]]he [[SUGGESTION_END]][[SUGGESTION_START]]table [[SUGGESTION_END]][[SUGGESTION_START]]y.1-2 [[SUGGESTION_END]][[SUGGESTION_START]]relat[[SUGGESTION_END]][[SUGGESTION_START]]ed[[SUGGESTION_END]][[SUGGESTION_START]] with comput[[SUGGESTION_END]]ing [[SUGGESTION_START]]service[[SUGGESTION_END]][[SUGGESTION_START]]?[[SUGGESTION_END]]
[[SUGGESTION_START]]CM6: A definition needed for Joint E2E latency[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]CM7: [[SUGGESTION_END]][[SUGGESTION_START]]“[[SUGGESTION_END]][[SUGGESTION_START]]J[[SUGGESTION_END]][[SUGGESTION_START]]oint e2e latency” is more from service level of [[SUGGESTION_END]][[SUGGESTION_START]]SA1[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]UC 6.48 Use case on service robot for power grid[[SUGGESTION_END]]
[[SUGGESTION_START]]The value[[SUGGESTION_END]][[SUGGESTION_START]] and note 3 updated based on approved S1-254342.[[SUGGESTION_END]]
* * * First Change * * *
3 Definitions, symbols and abbreviations
3.1 Definitions
For the purposes of the present document, the terms and definitions given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
……
[[SUGGESTION_START]]OP1: [[SUGGESTION_END]]
[[SUGGESTION_START]]Joint E2E latency: [[SUGGESTION_END]][[SUGGESTION_START]]Joint latency of [[SUGGESTION_END]][[SUGGESTION_START]]round-trip [[SUGGESTION_END]][[SUGGESTION_START]]end-to-end[[SUGGESTION_END]][[SUGGESTION_START]] latency, and AI inference latency in Service Hosting Environment, and UE is only considered to contribute to the communication service latency.[[SUGGESTION_END]]
[[SUGGESTION_START]]New wording[[SUGGESTION_END]][[SUGGESTION_START]]: [[SUGGESTION_END]]
[[SUGGESTION_START]]Max-allowed Joint Latency[[SUGGESTION_END]][[SUGGESTION_START]]:[[SUGGESTION_END]][[SUGGESTION_START]] this refers to [[SUGGESTION_END]][[SUGGESTION_START]]latency[[SUGGESTION_END]] [[SUGGESTION_START]]of[[SUGGESTION_END]][[SUGGESTION_START]] 6G AI service under the operator’s control[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]] [[SUGGESTION_START]]including[[SUGGESTION_END]] [[SUGGESTION_START]]the end-to-end [[SUGGESTION_END]][[SUGGESTION_START]]communication [[SUGGESTION_END]][[SUGGESTION_START]]latency (from the UE sending inference input to the network), plus the processing latency (e.g. AI inference latency) in the Service Hosting Environment, plus the end-to-end [[SUGGESTION_END]][[SUGGESTION_START]]communication [[SUGGESTION_END]][[SUGGESTION_START]]latency (from the network sending the inference result back to the [[SUGGESTION_END]][[SUGGESTION_START]]UE)[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]OP2: [[SUGGESTION_END]]
[[SUGGESTION_START]]joint E2E latency: the performance parameter for 6G AI Service when performing AI inference, comprised of round-trip communication latency (e.g. communication latency for UE transferring inference input to AI model in Service Hosting Environment, as well as the communication latency for transferring inference result from the AI model back to the UE), plus the AI inference latency (e.g. TTFT) inside the Service Hosting Environment.[[SUGGESTION_END]]
[[SUGGESTION_START]]OP3: [[SUGGESTION_END]]
[[SUGGESTION_START]]User experienced roundtrip time: Maximum acceptable roundtrip time for the user, which may combine the latencies of multiple 3GPP services under the operator’s control eg communication , computing, AI, sensing, in both directions between the UE and the network/SHE. The latency of each service contributing to this roundtrip time can vary [or “is flexible”].[[SUGGESTION_END]]
[[SUGGESTION_START]]Editor’s Note: The definition of Joint E2E latency is FFS.[[SUGGESTION_END]]
* * * Second Change * * *
Y Consolidated potential KPIs
y.1. Performance requirements for AI service
Editor's Note: the following potential performance requirements from immersive session are considered for consolidation.
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 9.2.6-1] (Table 9.2.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 9.3.6-4] (Table 9.3.6-2)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 9.5.6-5] (Table 9.5.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 9.6.6-3] (Table 9.6.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 9.7.2-1] (Table 9.7.2-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 9.9.6-2] (Table 9.9.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 9.12.6-6] (Table 9.12.6-1)
[[SUGGESTION_START]]- TR 22.870 1.0.1 [PR 9.18.6-1] (Table 9.18.6-1)[[SUGGESTION_END]]
[[SUGGESTION_START]]- TR 22.870 1.0.1 [PR 9.8.6-1] (Table 9.8.6-1)[[SUGGESTION_END]]
The following potential performance requirements from AI session are considered for consolidation.
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 6.10.6-6] (Table 6.10.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 6.26.6-1] (Table 6.26.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 6.48.6-1] (Table 6.48.6-1)
- TR 22.870 [[SUGGESTION_START]]1[[SUGGESTION_END]].[[SUGGESTION_START]]0[[SUGGESTION_END]].[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 6.49.6-1] (Table 6.49.6-1)
[[SUGGESTION_START]]- TR 22.870 1.0.1 [PR 9.3.6-1][[SUGGESTION_END]][[SUGGESTION_START]] (Table 9.3.6-1)[[SUGGESTION_END]]
[[SUGGESTION_START]]The following potential performance requirements are excluded due to the remaining ENs and will be taken into consideration after the ENs are cleaned up:[[SUGGESTION_END]]
- TR 22.870 0.4.[[SUGGESTION_START]]1 [[SUGGESTION_END]][PR 6.38.6-3] (Table 6.38.6-1)
[CPR y.1-1] The 6G system shall provide various communication and AI services with the following KPIs.
Editor’s Note: Unless stated otherwise, the definition and understanding "Reliability", "Service bit rate: user-experienced data rate", and "Transfer interval" refers to [22.261] and [22.256].
Table y.1-1: [[SUGGESTION_START]]Communication [[SUGGESTION_END]][[SUGGESTION_START]]performance [[SUGGESTION_END]]requirements for burst traffic
Use Cases
Burst size
Max Allowed [[SUGGESTION_START]]end-to-end [[SUGGESTION_END]]latency for a burst
Service bit rate: user-experienced data rate
transmission latency of a packet
Average packet size
UE speed
Service Area
Synergized photo enhancement
(UC 9.3 A)
UL: [150 Mbps]
(note A-1)
UL: [1500ms]
(note A-2)
UL: [100 Mbps]
N/A
N/A
Stationary or Pedestrian
Countrywide
Synergized gaming enhancement
(UC 9.3 A)
UL: [5-20 Mbps]
(note A-1)
UL: [50ms]
(note A-2)
UL: [100 – 400 Mbps]
N/A
N/A
Stationary or Pedestrian
Countrywide
Image based GenAI app
(UC 6.26 B)
(note B-1)
400 KB
50ms
64 Mbps
20ms
> 800B
N/A
N/A
Video based GenAI app
(UC 6.26 B)
(note B-1)
20 MB
400ms
400 Mbps
20ms
> 800B
N/A
N/A
Chatbot
(UC 6.26 B)
(note B-1)
0.5 KB
20ms
200 Kbps
30ms
< 800B
N/A
N/A
NOTE A-1: Assuming 6 x 4K raw pictures and compression ratio in [21] for photo enhancement. Assuming 3D models including 0.35 to 2 million vertexes and compression ratio assumption for 3D model in [23] for gaming enhancement.
NOTE A-2: 1500 ms is derived from the E2E latency of 3 s (based on users' patience statistics as shown in [22]) and 1.5 s for processing in the cloud and downloading. 50ms uplink latency is derived from [24].
NOTE B-1: Max allowed latency for a burst: max latency for sending out the whole packets within a burst.
[[SUGGESTION_START]]OP1: [[SUGGESTION_END]][[SUGGESTION_START]]Adding table NOTE 1 for communication part latency reference[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
Table y.1-2: Performance requirements for [[SUGGESTION_START]]joint [[SUGGESTION_END]]communication and AI services
Use Cases
Traffic type
Average packet//frame size (byte)
Transfer interval
Service bit rate: user-experienced data rate
Joint E2E latency
(note 1)
Reliability
Home robot perceiving overall context of the scene
(UC 6.10 A)
(note A-5)
UL camera data
(note A-1)
[<1000]
[10ms]
[20-60 Mbps]
(note A-2)
[150ms]
(note A-3)
[99.9 %]
Identifying individual objects in the scene
(UC 6.10 A)
(note A-5)
UL camera data
(note A-1)
[<1000]
[10ms]
[20-60 Mbps]
(note A-2)
[200-300ms]
(note A-4)
[99.9 %]
Service robot
(UC 6.48 B)
UL sensor data (without LiDAR)
(note B-1)
1250-12500
10ms
UL: 1-10 Mbps
100-150ms
(note B-4)
99.99%
UL LiDAR
[[SUGGESTION_START]]345600[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]B[[SUGGESTION_END]][[SUGGESTION_START]]-3)[[SUGGESTION_END]]
100ms
UL: 27.6 Mbps
(note B-3)
100-150ms
(note B-4)
99.99%
Control command (high level task, action plan, etc.)
(note B-2)
625-12500
50ms
DL: 0.1-2 Mbps
100-150ms
(note B-4)
99.99%
Support robot conscious awareness for interacting with human user
(UC 6.49 C)
UL cameras data
(note C-1)
[<1000]
[10ms]
UL: [20-60 Mbps]
(note C-2)
[<200ms]
(note C-3)
(note C-5)
[99.9%]
NOTE 1: Joint E2E latency (i.e. round-trip communication latency, and AI inference latency in Service Hosting Environment), and UE is only considered to contribute to the communication service latency.
[[SUGGESTION_START]]NOTE 1：The Max allowed end-to-end latency (UL and DL) [[SUGGESTION_END]][[SUGGESTION_START]]for [[SUGGESTION_END]][[SUGGESTION_START]]the [[SUGGESTION_END]][[SUGGESTION_START]]communication [[SUGGESTION_END]][[SUGGESTION_START]]part [[SUGGESTION_END]][[SUGGESTION_START]]needs to comply with [[SUGGESTION_END]][[SUGGESTION_START]]TS 22.261 [14] [[SUGGESTION_END]][[SUGGESTION_START]]Table 7.10.1-1.[[SUGGESTION_END]]
NOTE A-1: 6 RGB cameras are equipped for robot “Figure 02” [180].
NOTE A-2: 20 Mbps is for six 1080p cameras, and 60 Mbps is for four 1080p cameras plus two 4K cameras. Data Rate is calculated from video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, and around 24Mbps for 4K 30Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real-time video uploading of a vehicle as per YD/T 4778-2024 [182].
NOTE A-3: Based on psychophysical and neurophysiological studies [281], [282], human beings can perceive the gist (e.g. overall meaning) of complex visual scenes within around 150ms after stimulus onset. This rapid initial perception enables us to grasp the general context of a scene, even if the stimulus is briefly presented (around 10ms).
NOTE A-4: For human beings, it takes longer for a human being to identify individual objects, e.g. finer object identification requires larger than 200ms [283] and around 300ms [284].
NOTE A-5: In the target scenarios, robots are expected to have similar perception time as an average human being.
NOTE B-1: Refers to the kinematic state, environment perception, manipulation status info except LiDAR to be sent from the service robot to the network to enable effective motion planning, object interaction and navigation.
NOTE B-2: Refers to the control command towards service robot, e.g. high-level task, action plans, motion strategy, gripper command, etc.
NOTE B-3: [[SUGGESTION_START]]t[[SUGGESTION_END]][[SUGGESTION_START]]he frame size and data rate of LiDAR are based on frame rate 10Hz, 28800 points/frame, 12 byte for one point. The frame size is calculated by points/frame * bytes per point whereas the data rate is calculated by points/frame * bytes per point * bits per byte * frame rate (i.e. 28800*12*8*10).[[SUGGESTION_END]]
NOTE B-4: E2E latency includes two parts: the round-trip latency for communication service and the latency for AI inference within the service hosting environment. The typical robot control loops require 100-150ms latency [273] for AI inference, communication and control. For example, the communication may take about 40ms while the AI inference may take about 100ms [274] for the service robot.
NOTE C-1: 6 RGB cameras are equipped for robot “Figure 02” [180].
NOTE C-2: 20 Mbps is for six 1080p cameras, and 60 Mbps is for four 1080p cameras plus two 4K cameras. Data Rate is calculated from video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, and around 24Mbps for 4K 30Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real-time video uploading of a vehicle as per YD/T 4778-2024 [182].
NOTE C-3: For physical AI robot interacting with a human user, the robot is expected to mimic the similar basic human brain reaction time including conscious awareness/recognition and decision-making based on various stimuli. Such human brain reaction time ranges from mean auditory reaction time 140-160ms, touch 155ms, to visual reaction time 180-200ms [279].
NOTE C-5: The human to robot latency (vice versa) is not included.
OP2: Split latency into Communication part and Non-Communication part, with EN.
Table y.1-2: Performance requirements for joint communication and AI services
Use Cases
Traffic type
Average packet//frame size (byte)
Transfer interval
Service bit rate: user-experienced data rate
Joint E2E latency
Reliability
[[SUGGESTION_START]]Max allowed end-to-end latency [[SUGGESTION_END]]
[[SUGGESTION_START]]Non-Communication part[[SUGGESTION_END]]
Home robot perceiving overall context of the scene
(UC 6.10 A)
(note A-5)
UL camera data
(note A-1)
[<1000]
[10ms]
[20-60 Mbps]
(note A-2)
[99.9 %]
Identifying individual objects in the scene
(UC 6.10 A)
(note A-5)
UL camera data
(note A-1)
[<1000]
[10ms]
[20-60 Mbps]
(note A-2)
[99.9 %]
Service robot
(UC 6.48 B)
UL sensor data (without LiDAR)
(note B-1)
1250-12500
10ms
UL: 1-10 Mbps
UL:
[[SUGGESTION_START]][1[[SUGGESTION_END]][[SUGGESTION_START]]3[[SUGGESTION_END]][[SUGGESTION_START]]ms][[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]3[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]][8[[SUGGESTION_END]][[SUGGESTION_START]]7[[SUGGESTION_END]][[SUGGESTION_START]]-13[[SUGGESTION_END]][[SUGGESTION_START]]7[[SUGGESTION_END]][[SUGGESTION_START]]ms][[SUGGESTION_END]]
99.99%
UL LiDAR
[[SUGGESTION_START]]345600[[SUGGESTION_END]]
[[SUGGESTION_START]](note B-3)[[SUGGESTION_END]]
100ms
UL: 27.6 Mbps
(note B-3)
99.99%
Control command (high level task, action plan, etc.)
(note B-2)
625-12500
50ms
DL: 0.1-2 Mbps
DL:
[[SUGGESTION_START]][12ms][[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]][88-138ms][[SUGGESTION_END]]
99.99%
Support robot conscious awareness for interacting with human user
(UC 6.49 C)
UL cameras data
(note C-1)
[<1000]
[10ms]
UL: [20-60 Mbps]
(note C-2)
[99.9%]
NOTE 1: Joint E2E latency (i.e. round-trip communication latency, and AI inference latency in Service Hosting Environment), and UE is only considered to contribute to the communication service latency.
[[SUGGESTION_START]]NOTE 1: The [[SUGGESTION_END]][[SUGGESTION_START]]value of [[SUGGESTION_END]][[SUGGESTION_START]]latency[[SUGGESTION_END]][[SUGGESTION_START]] in both directions (UL/DL) [[SUGGESTION_END]][[SUGGESTION_START]]of each service contributing[[SUGGESTION_END]] [[SUGGESTION_START]]can vary or “is flexible”.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 2：Refer to TS 22.261 [14] Table 7.10.1-1.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 3：Refer to TS 22.874 [14] clause 5.4.6.[[SUGGESTION_END]]
NOTE A-1: 6 RGB cameras are equipped for robot “Figure 02” [180].
NOTE A-2: 20 Mbps is for six 1080p cameras, and 60 Mbps is for four 1080p cameras plus two 4K cameras. Data Rate is calculated from video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, and around 24Mbps for 4K 30Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real-time video uploading of a vehicle as per YD/T 4778-2024 [182].
NOTE A-3: Based on psychophysical and neurophysiological studies [281], [282], human beings can perceive the gist (e.g. overall meaning) of complex visual scenes within around 150ms after stimulus onset. This rapid initial perception enables us to grasp the general context of a scene, even if the stimulus is briefly presented (around 10ms).
NOTE A-4: For human beings, it takes longer for a human being to identify individual objects, e.g. finer object identification requires larger than 200ms [283] and around 300ms [284].
NOTE A-5: In the target scenarios, robots are expected to have similar perception time as an average human being.
NOTE B-1: Refers to the kinematic state, environment perception, manipulation status info except LiDAR to be sent from the service robot to the network to enable effective motion planning, object interaction and navigation.
NOTE B-2: Refers to the control command towards service robot, e.g. high-level task, action plans, motion strategy, gripper command, etc.
NOTE B-3: [[SUGGESTION_START]]the frame size and data rate of LiDAR are based on frame rate 10Hz, 28800 points/frame, 12 byte for one point. The frame size is calculated by points/frame * bytes per point whereas the data rate is calculated by points/frame * bytes per point * bits per byte * frame rate (i.e. 28800*12*8*10).[[SUGGESTION_END]]
NOTE B-4: E2E latency includes two parts: the round-trip latency for communication service and the latency for AI inference within the service hosting environment. The typical robot control loops require 100-150ms latency [273] for AI inference, communication and control. For example, the communication may take about 40ms while the AI inference may take about 100ms [274] for the service robot.
NOTE C-1: 6 RGB cameras are equipped for robot “Figure 02” [180].
NOTE C-2: 20 Mbps is for six 1080p cameras, and 60 Mbps is for four 1080p cameras plus two 4K cameras. Data Rate is calculated from video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, and around 24Mbps for 4K 30Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real-time video uploading of a vehicle as per YD/T 4778-2024 [182].
NOTE C-3: For physical AI robot interacting with a human user, the robot is expected to mimic the similar basic human brain reaction time including conscious awareness/recognition and decision-making based on various stimuli. Such human brain reaction time ranges from mean auditory reaction time 140-160ms, touch 155ms, to visual reaction time 180-200ms [279].
NOTE C-5: The human to robot latency (vice versa) is not included.
[[SUGGESTION_START]]Editor’s Note: Split the Joint E2E latency is FFS.[[SUGGESTION_END]]
OP3: Adding 3 column with OP1 and OP2 together with NOTE.
Table y.1-2: Performance requirements for joint communication and AI services
Use Cases
Traffic type
Average packet//frame size (byte)
Transfer interval
Service bit rate: user-experienced data rate
[[SUGGESTION_START]]Max-allowed [[SUGGESTION_END]]Joint latency
Reliability
[[SUGGESTION_START]]Expected [[SUGGESTION_END]][[SUGGESTION_START]]end-to-end latency [[SUGGESTION_END]][[SUGGESTION_START]]（UL[[SUGGESTION_END]][[SUGGESTION_START]],[[SUGGESTION_END]][[SUGGESTION_START]] or DL）[[SUGGESTION_END]]
[[SUGGESTION_START]]Non-Communication part[[SUGGESTION_END]]
Total
Home robot perceiving overall context of the scene
(UC 6.10 A)
(note A-5)
UL camera data
(note A-1)
[<1000]
[10ms]
[20-60 Mbps]
(note A-2)
[[SUGGESTION_START]][30ms - 50ms][[SUGGESTION_END]]
[[SUGGESTION_START]][50[[SUGGESTION_END]][[SUGGESTION_START]]ms [[SUGGESTION_END]][[SUGGESTION_START]]-[[SUGGESTION_END]] [[SUGGESTION_START]]90[[SUGGESTION_END]][[SUGGESTION_START]]ms[[SUGGESTION_END]][[SUGGESTION_START]]][[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[150ms]
(note A-3)
[99.9 %]
Identifying individual objects in the scene
(UC 6.10 A)
(note A-5)
UL camera data
(note A-1)
[<1000]
[10ms]
[20-60 Mbps]
(note A-2)
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]100[[SUGGESTION_END]][[SUGGESTION_START]]ms - [[SUGGESTION_END]][[SUGGESTION_START]]140[[SUGGESTION_END]][[SUGGESTION_START]]ms][[SUGGESTION_END]][[SUGGESTION_START]] -[[SUGGESTION_END]]
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]200[[SUGGESTION_END]][[SUGGESTION_START]]ms - [[SUGGESTION_END]][[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]]40ms][[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[200-300ms]
(note A-4)
[99.9 %]
Service robot
(UC 6.48 B)
UL sensor data (without LiDAR)
(note B-1)
1250-12500
10ms
UL: 1-10 Mbps
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]<[[SUGGESTION_END]][[SUGGESTION_START]]40ms][[SUGGESTION_END]][[SUGGESTION_START]] -[[SUGGESTION_END]]
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]50[[SUGGESTION_END]][[SUGGESTION_START]]ms - [[SUGGESTION_END]][[SUGGESTION_START]]90[[SUGGESTION_END]][[SUGGESTION_START]]ms][[SUGGESTION_END]]
[[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
100-150ms
(note B-4)
99.99%
UL LiDAR
[[SUGGESTION_START]]345600[[SUGGESTION_END]]
[[SUGGESTION_START]](note B-3)[[SUGGESTION_END]]
100ms
UL: 27.6 Mbps
(note B-3)
99.99%
Control command (high level task, action plan, etc.)
(note B-2)
625-12500
50ms
DL: 0.1-2 Mbps
99.99%
Support robot conscious awareness for interacting with human user
(UC 6.49 C)
UL cameras data
(note C-1)
[<1000]
[10ms]
UL: [20-60 Mbps]
(note C-2)
[[SUGGESTION_START]]<[100ms - 140ms] [[SUGGESTION_END]][[SUGGESTION_START]](note 1)[[SUGGESTION_END]]
[<200ms]
(note C-3)
(note C-5)
[99.9%]
NOTE 1: Joint E2E latency (i.e. round-trip communication latency, and AI inference latency in Service Hosting Environment), and UE [[SUGGESTION_START]] is only considered to contribute to the communication service latency.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 1: The [[SUGGESTION_END]][[SUGGESTION_START]]value of [[SUGGESTION_END]][[SUGGESTION_START]]latency[[SUGGESTION_END]][[SUGGESTION_START]] in both directions (UL/DL) [[SUGGESTION_END]][[SUGGESTION_START]]of each service contributing[[SUGGESTION_END]] [[SUGGESTION_START]]can vary or “is flexible”.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 2：Refer to TS 22.261 [14] Table 7.10.1-1.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]3[[SUGGESTION_END]][[SUGGESTION_START]]：Refer to T[[SUGGESTION_END]][[SUGGESTION_START]]R[[SUGGESTION_END]][[SUGGESTION_START]] 22.874 [14] [[SUGGESTION_END]][[SUGGESTION_START]]clause[[SUGGESTION_END]] [[SUGGESTION_START]]5.4.6[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 1: [[SUGGESTION_END]][[SUGGESTION_START]]The target value for this parameter is derived from the total Max-allowed Joint Latency, considering the portion typically allocated to communication latency. For instance, given a [[SUGGESTION_END]][[SUGGESTION_START]]total latency[[SUGGESTION_END]][[SUGGESTION_START]] of 150ms and a typical one-way communication latency of 30ms, a non-communication latency of 90ms (calculated as 150ms - 30ms * 2) is expected. Respectively the Max allowed Joint latency ‘s Lower and upper boundary[[SUGGESTION_END]][[SUGGESTION_START]] ([[SUGGESTION_END]][[SUGGESTION_START]]100ms-150ms[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]][[SUGGESTION_START]], [[SUGGESTION_END]][[SUGGESTION_START]]subtracted by the expected communication latency (30ms-50ms) results in the indicative non-communication Latency.[[SUGGESTION_END]]
NOTE A-1: 6 RGB cameras are equipped for robot “Figure 02” [180].
NOTE A-2: 20 Mbps is for six 1080p cameras, and 60 Mbps is for four 1080p cameras plus two 4K cameras. Data Rate is calculated from video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, and around 24Mbps for 4K 30Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real-time video uploading of a vehicle as per YD/T 4778-2024 [182].
NOTE A-3: Based on psychophysical and neurophysiological studies [281], [282], human beings can perceive the gist (e.g. overall meaning) of complex visual scenes within around 150ms after stimulus onset. This rapid initial perception enables us to grasp the general context of a scene, even if the stimulus is briefly presented (around 10ms).
NOTE A-4: For human beings, it takes longer for a human being to identify individual objects, e.g. finer object identification requires larger than 200ms [283] and around 300ms [284].
NOTE A-5: In the target scenarios, robots are expected to have similar perception time as an average human being.
NOTE B-1: Refers to the kinematic state, environment perception, manipulation status info except LiDAR to be sent from the service robot to the network to enable effective motion planning, object interaction and navigation.
NOTE B-2: Refers to the control command towards service robot, e.g. high-level task, action plans, motion strategy, gripper command, etc.
NOTE B-3: [[SUGGESTION_START]]the frame size and data rate of LiDAR are based on frame rate 10Hz, 28800 points/frame, 12 byte for one point. The frame size is calculated by points/frame * bytes per point whereas the data rate is calculated by points/frame * bytes per point * bits per byte * frame rate (i.e. 28800*12*8*10).[[SUGGESTION_END]]
NOTE B-4: [[SUGGESTION_START]]The typical robot control loops require 100-150ms latency [273] for AI inference, communication and control. For example, the communication may take about 40ms while the AI inference may take about 100ms [274] for the service robot.[[SUGGESTION_END]]
NOTE C-1: 6 RGB cameras are equipped for robot “Figure 02” [180].
NOTE C-2: 20 Mbps is for six 1080p cameras, and 60 Mbps is for four 1080p cameras plus two 4K cameras. Data Rate is calculated from video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, and around 24Mbps for 4K 30Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real-time video uploading of a vehicle as per YD/T 4778-2024 [182].
NOTE C-3: For physical AI robot interacting with a human user, the robot is expected to mimic the similar basic human brain reaction time including conscious awareness/recognition and decision-making based on various stimuli. Such human brain reaction time ranges from mean auditory reaction time 140-160ms, touch 155ms, to visual reaction time 180-200ms [279].
NOTE C-5: The human to robot latency (vice versa) is not included.
[[SUGGESTION_START]]NOTE: The latency attributes doesn’t imply or preclude any architecture assumption or solution.[[SUGGESTION_END]]
[[SUGGESTION_START]]Editor’s Note: The definition of Joint E2E latency is FFS.[[SUGGESTION_END]]
[[SUGGESTION_START]]OP[[SUGGESTION_END]][[SUGGESTION_START]]4[[SUGGESTION_END]][[SUGGESTION_START]]: 3 column[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]] [[SUGGESTION_START]]combined[[SUGGESTION_END]][[SUGGESTION_START]] OP1 and OP2 with NOTE of [[SUGGESTION_END]][[SUGGESTION_START]]spited[[SUGGESTION_END]] [[SUGGESTION_START]]columns clarification[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]Table y.1-2: Performance requirements for joint communication and AI services[[SUGGESTION_END]]
[[SUGGESTION_START]]Use Cases[[SUGGESTION_END]]
[[SUGGESTION_START]]Traffic type[[SUGGESTION_END]]
[[SUGGESTION_START]]Average packet//frame size (byte)[[SUGGESTION_END]]
[[SUGGESTION_START]]Transfer interval[[SUGGESTION_END]]
[[SUGGESTION_START]]Service bit rate: user-experienced data rate[[SUGGESTION_END]]
[[SUGGESTION_START]]Joint E2E latency[[SUGGESTION_END]]
[[SUGGESTION_START]]Reliability[[SUGGESTION_END]]
[[SUGGESTION_START]]Max allowed end-to-end latency [[SUGGESTION_END]]
[[SUGGESTION_START]]Non-Communication part[[SUGGESTION_END]]
[[SUGGESTION_START]]Total[[SUGGESTION_END]]
[[SUGGESTION_START]]Home robot perceiving overall context of the scene[[SUGGESTION_END]]
[[SUGGESTION_START]](UC 6.10 A)[[SUGGESTION_END]]
[[SUGGESTION_START]](note A-5)[[SUGGESTION_END]]
[[SUGGESTION_START]]UL camera data[[SUGGESTION_END]]
[[SUGGESTION_START]](note A-1)[[SUGGESTION_END]]
[[SUGGESTION_START]][<1000][[SUGGESTION_END]]
[[SUGGESTION_START]][10ms][[SUGGESTION_END]]
[[SUGGESTION_START]][20-60 Mbps][[SUGGESTION_END]]
[[SUGGESTION_START]](note A-2)[[SUGGESTION_END]]
[[SUGGESTION_START]][150ms][[SUGGESTION_END]]
[[SUGGESTION_START]](note A-3)[[SUGGESTION_END]]
[[SUGGESTION_START]][99.9 %][[SUGGESTION_END]]
[[SUGGESTION_START]]Identifying individual objects in the scene[[SUGGESTION_END]]
[[SUGGESTION_START]](UC 6.10 A)[[SUGGESTION_END]]
[[SUGGESTION_START]](note A-5)[[SUGGESTION_END]]
[[SUGGESTION_START]]UL camera data[[SUGGESTION_END]]
[[SUGGESTION_START]](note A-1)[[SUGGESTION_END]]
[[SUGGESTION_START]][<1000][[SUGGESTION_END]]
[[SUGGESTION_START]][10ms][[SUGGESTION_END]]
[[SUGGESTION_START]][20-60 Mbps][[SUGGESTION_END]]
[[SUGGESTION_START]](note A-2)[[SUGGESTION_END]]
[[SUGGESTION_START]][200-300ms][[SUGGESTION_END]]
[[SUGGESTION_START]](note A-4)[[SUGGESTION_END]]
[[SUGGESTION_START]][99.9 %][[SUGGESTION_END]]
[[SUGGESTION_START]]Service robot[[SUGGESTION_END]]
[[SUGGESTION_START]](UC 6.48 B)[[SUGGESTION_END]]
[[SUGGESTION_START]]UL sensor data (without LiDAR)[[SUGGESTION_END]]
[[SUGGESTION_START]](note B-1)[[SUGGESTION_END]]
[[SUGGESTION_START]]1250-12500[[SUGGESTION_END]]
[[SUGGESTION_START]]10ms[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: 1-10 Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]](note B-4)[[SUGGESTION_END]]
[[SUGGESTION_START]]100-150ms[[SUGGESTION_END]]
[[SUGGESTION_START]]99.99%[[SUGGESTION_END]]
[[SUGGESTION_START]]UL LiDAR[[SUGGESTION_END]]
[[SUGGESTION_START]]28800[[SUGGESTION_END]]
[[SUGGESTION_START]]100ms[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: 27.6 Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]](note B-3)[[SUGGESTION_END]]
[[SUGGESTION_START]]99.99%[[SUGGESTION_END]]
[[SUGGESTION_START]]Control command (high level task, action plan, etc.)[[SUGGESTION_END]]
[[SUGGESTION_START]](note B-2)[[SUGGESTION_END]]
[[SUGGESTION_START]]625-12500[[SUGGESTION_END]]
[[SUGGESTION_START]]50ms[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: 0.1-2 Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]]99.99%[[SUGGESTION_END]]
[[SUGGESTION_START]]Support robot conscious awareness for interacting with human user[[SUGGESTION_END]]
[[SUGGESTION_START]](UC 6.49 C)[[SUGGESTION_END]]
[[SUGGESTION_START]]UL cameras data[[SUGGESTION_END]]
[[SUGGESTION_START]](note C-1)[[SUGGESTION_END]]
[[SUGGESTION_START]][<1000][[SUGGESTION_END]]
[[SUGGESTION_START]][10ms][[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [20-60 Mbps][[SUGGESTION_END]]
[[SUGGESTION_START]](note C-2)[[SUGGESTION_END]]
[[SUGGESTION_START]][<200ms][[SUGGESTION_END]]
[[SUGGESTION_START]](note C-3)[[SUGGESTION_END]]
[[SUGGESTION_START]](note C-5)[[SUGGESTION_END]]
[[SUGGESTION_START]][99.9%][[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 1: Joint E2E latency (i.e. round-trip communication latency, and AI inference latency in Service Hosting Environment), and UE is only considered to contribute to the communication service latency.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE A-1: 6 RGB cameras are equipped for robot “Figure 02” [180]. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE A-2: 20 Mbps is for six 1080p cameras, and 60 Mbps is for four 1080p cameras plus two 4K cameras. Data Rate is calculated from video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, and around 24Mbps for 4K 30Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real-time video uploading of a vehicle as per YD/T 4778-2024 [182]. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE A-3: Based on psychophysical and neurophysiological studies [281], [282], human beings can perceive the gist (e.g. overall meaning) of complex visual scenes within around 150ms after stimulus onset. This rapid initial perception enables us to grasp the general context of a scene, even if the stimulus is briefly presented (around 10ms). [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE A-4: For human beings, it takes longer for a human being to identify individual objects, e.g. finer object identification requires larger than 200ms [283] and around 300ms [284].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE A-5: In the target scenarios, robots are expected to have similar perception time as an average human being.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE B-1: Refers to the kinematic state, environment perception, manipulation status info except LiDAR to be sent from the service robot to the network to enable effective motion planning, object interaction and navigation.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE B-2: Refers to the control command towards service robot, e.g. high-level task, action plans, motion strategy, gripper command, etc. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE B-3: The data rate of LiDAR is based on frame rate 10Hz, 28800 points/frame, 12byte for one point cloud, i.e. 28800*16*8*10=27.6Mbps. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE B-4: E2E latency includes two parts: the round-trip latency for communication service and the latency for AI inference within the service hosting environment. The typical robot control loops require 100-150ms latency [273] for AI inference, communication and robot control. For example, the communication may take about 40ms while the AI inference may take about 100ms [274] for the service robot.[[SUGGESTION_END]][[SUGGESTION_START]] The value[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]] of each latency contributing to this round-trip time [[SUGGESTION_END]][[SUGGESTION_START]]latency [[SUGGESTION_END]][[SUGGESTION_START]]can [[SUGGESTION_END]][[SUGGESTION_START]]vary.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE C-1: 6 RGB cameras are equipped for robot “Figure 02” [180]. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE C-2: 20 Mbps is for six 1080p cameras, and 60 Mbps is for four 1080p cameras plus two 4K cameras. Data Rate is calculated from video resolution*(Bits per colour*3) *Refresh rate/Compression ratio. Compression ratio of 240 and 8 bits per colour is assumed, thus data rate is around 3 Mbps for 1080p 15Hz, and around 24Mbps for 4K 30Hz. Compression ratio, bits per colour and refresh rate for 1080p refers to the similar cases for real-time video uploading of a vehicle as per YD/T 4778-2024 [182]. [[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE C-3: For physical AI robot interacting with a human user, the robot is expected to mimic the similar basic human brain reaction time including conscious awareness/recognition and decision-making based on various stimuli. Such human brain reaction time ranges from mean auditory reaction time 140-160ms, touch 155ms, to visual reaction time 180-200ms [279].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE C-5: The human to robot latency (vice versa) is not included.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE: The latency attributes doesn’t imply or preclude any architecture assumption or solution.[[SUGGESTION_END]]
[[SUGGESTION_START]]Editor’s Note: The definition of Joint E2E latency is FFS.[[SUGGESTION_END]]
* * * End of changes * * *
|
S1-260044.zip
|
2026-01-21T09:29:49.174530
|
S1-260045
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
|
Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG SA WG 1 Meeting #112 Ad Hoc - e S1-260045
12-16 January 2026, electronic meeting (revision of S1-254322)
Source: Nokia (Moderator)
pCR Title: Consolidation of KPI requirements on Massive Communication
Draft Spec: 3GPP TR 22.870
Agenda item: 1.4
Document for: Information
Contact: feifei.lou@nokia.com
Abstract: Update the consolidation of massive communication KPI table based on TR 22.870 v1.0.1.
1. Introduction
S1-254322 already updated the table based on the approved S1-254423, but was not treated in SA1#112. This contribution aligned with the editorial changes in v1.0.1 of the TR.
2. Summary of Changes
The following editorial changes are applied.
Update reference number in NOTE 2.
Change “down link”, “up link” to “downlink”, “uplink”.
3. Conclusions
4. Proposal
This contribution only provides the editorial changes and is for information.
* * * First Change * * * *
All new texts
Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]] Massive Communication
Table Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]]-1: KPI for Massive Communication
Profile
Bit rate downlink (Mbit/s)
Bit rate uplink (Mbit/s)
End-to-end latency: maximum (ms) (NOTE 4)
Payload size
# of UEs
Connection (UEs/km2)
Communication service availability: target value (NOTE 4)
Transfer Interval (sec) (NOTE 5)
Smart Grid (monitor and control)
Peak 5
Peak5
100 (NOTE 2)
0.5Kbyte
(NOTE 1)
10 000
> 99.99
60
Smart Grid (software download)
Peak [5]
-
Up to 20MB (NOTE 3)
10 000
> 99.99
-
NOTE 1: Typical message sizes are 500-byte payload meter readings and control.
NOTE 2: Reference for requirement of 100ms EPRI report “Falling Conductor Protection” [[[SUGGESTION_START]]416[[SUGGESTION_END]]].
NOTE 3: up to 20MB for Firmware upgrade.
NOTE 4: As defined in TS 22.261 [14]
NOTE 5: As defined in TS 22.104 [64]
* * * End of Changes * * * *
|
S1-260045.zip
|
2026-01-21T09:30:08.733526
|
S1-260046
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
|
Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG SA WG 1 Meeting #112 Ad Hoc - e S1-260046
12-16 January 2026, electronic meeting (revision of S1-254513)
Source: Nokia (Moderator)
pCR Title: Consolidation of KPI requirements on Industry and Verticals
Draft Spec: 3GPP TR 22.870
Agenda item: 1.4
Document for: Information
Contact: feifei.lou@nokia.com
Abstract: Update the consolidated KPI tables for Industry and Verticals.
1. Introduction
The contribution provides an update to the consolidation of the KPI tables for industry and verticals based on v1.0.1 of the TR. Table 11.29.6-1 is considered with EN and not consolidated.
2. Summary of Changes
Consolidate KPIs from clause 11.25, 11.28, 11.29 into performance requirements for services with high reliability and/or low latency
The consolidated performance requirements for satellite access for UAM and Airplane is removed (moved to the consolidated performance requirements for Ubiquitous Connectivity)
Add the two notes from clause 11.24
Remove some brackets from KPIs as in clause 11.26
Some editorial changes
3. Conclusions
4. Proposal
This contribution is only for information.
* * * First Change * * * *
All new texts
Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]] Consolidated Performance requirements for Further Use Cases on Industry and Verticals
Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].1 UAV, UAM and Airplane Consolidated Performance Requirements
Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].1.1 Consolidated Performance requirements for Communication services for UAM
Table Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].1[[SUGGESTION_START]].1[[SUGGESTION_END]]-1: Consolidated Communication Performance requirements for UAM
Scenario
(note 7)
User Experienced Data rate (DL)
End to end Latency
Typical transmission interval
Reliability
(note 5)
Service area
[11.2] Sensing result to a UAM aircraft
(note 1)
–
[20] ms
[20] ms
[99.9] %
–
[11.2] 8K video live broadcast
[100 Mbps]
Traffic from the UAM aircraft
(note 2)
[200] ms
(note 2)
–
[95] %
Urban, scenic area
[600 kbps]
Traffic towards the UAM aircraft
(note 2)
[20] ms
(note 2)
–
[95] %
[11.2] Video streaming
[4 Mbps] for 720p video
[9 Mbps] for 1080p video
Traffic from the UAM aircraft
(note 2)
[100] ms
(note 2)
–
[95] %
Urban, rural area
[100 Mbps] for 8K video
Traffic from the UAM aircraft
(note 2)
[100] ms
(note 2)
–
[95] %
[11.2] Remote controller through HD video
[>=25 Mbps]
Traffic from the UAM aircraft
(note 2)
[100] ms
(note 2)
–
[99] %
Urban, rural area
[300 kbps]
Traffic towards the UAM aircraft
(note 2)
[20] ms
(note 2)
–
[99] %
[11.2] Video conferencing or video chat
[25 Mbps]
Traffic from passengers onboard UAM aircraft
(note 2) (note 3)
[100] ms
(note 2)
–
[99] %
Urban, rural area
[25 Mbps]
Traffic towards passengers onboard UAM aircraft
(note 2) (note 3)
[100] ms
(note 2)
–
[99] %
[11.2] Immersive multimedia service (e.g., cloud gaming)
[500 kbps]
Traffic from passengers onboard UAM aircraft
(note 3)
[50] ms
(note 4)
–
[99] %
Urban, rural area
[100~500 Mbps]
Traffic towards passengers onboard UAM aircraft
(note 3) (note 6)
[50] ms
(note 4)
–
[99] %
NOTE 1: Typically, the message size for one object detected and/or tracked via sensing is 1 kbyte [37]. It is assumed that around 25 objects (25 kbyte) per frame (20 ms) are sensed surrounding an aircraft. The reliability requirement for UAM aircrafts is about 99.9 %.
NOTE 2: These values are aligned with the KPIs for services provided to the UAV applications in TS 22.125 [35], Table 7.1-1.
NOTE 3: The value is per passenger; and it is assumed that up to 4 passengers per UAM aircraft use communication services simultaneously.
NOTE 4: According to TR 26.928 [50], typically a 50ms latency is a required for cloud gaming use case.
NOTE 5: According to [306], the reliability of real-time services is set to 99%, and that of video streaming is set to 95%.
NOTE 6: Data rate is calculated assuming typical parameters (e.g. resolution, refresh rate and compression rate). Some codecs may further drop the bitrate requirement.
NOTE 7: UAM in terms of altitude up to 1000m[[SUGGESTION_START]].[[SUGGESTION_END]]
Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].1.[[SUGGESTION_START]]2[[SUGGESTION_END]] Consolidated Performance requirements for huge data transfer for Airplane
Table Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].1[[SUGGESTION_START]].2[[SUGGESTION_END]]-[[SUGGESTION_START]]1[[SUGGESTION_END]]: Consolidated performance requirements for huge data transfer for Airplane
Scenario
Experienced data rate (DL)
(note)
Experienced data rate (UL)
(note)
Area traffic capacity
(DL)
Area traffic capacity
(UL)
Overall user density
UE speed
UE type
[11.6] Airplanes connectivity in airport vicinity (within 20 km airport radius, up to 1500 m)
[11 Gbps]/ plane
[22 Gbps]/ plane
TBD
TBD
TBD
Up to [350] km/h
Airplane mounted
NOTE: These KPIs are related to massive data transfer including operational data when the aircraft is on the ground or near the ground. The need is to send 10 TByte from aircraft to the ground (aircraft 1 hour in 20 km radius → 22 Gbit/s) [125].
Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].2 Digital twin Consolidated Performance requirements
Table Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].2-1: Consolidated performance requirements for Di[[SUGGESTION_START]]g[[SUGGESTION_END]]i[[SUGGESTION_START]]t[[SUGGESTION_END]]al twin
Scenario
Max allowed end-to-end latency
Service bit rate: user-experienced data rate
Reliability
Service Area
Location accuracy
Connection density
[devices / m2]
UE Speed
Remarks
[11.4] Real-Time Digital Twin
(note 1)
[1-10] ms
(note 2)
[<100] Mbps (UL) (note 3)
99.999%
(note 4)
[up to 1km2]
(note 5)
[≤ 10] cm
[1-10] devices/m2
Up to vehicular speed
Communication between physical system and the network (note 6)
NOTE 1: Wireless-sensing capability is expected to contribute to enrich the Digital Twin model, with KPIs related to e.g. location accuracy, resolution, range and latency
NOTE 2: Very low latency for the Real-time aspect, both in UL (to report up-to-date data) and DL (to send timely controls)
NOTE 3: It is expected uplink video will be most demanding.
NOTE 4: The minimum requirement as defined in [[[SUGGESTION_START]]4[[SUGGESTION_END]][[SUGGESTION_START]]15[[SUGGESTION_END]]].
NOTE 5: Service coverage both outdoor & indoor (e.g. industrial plant, city area) [31]
NOTE 6: [[SUGGESTION_START]]P[[SUGGESTION_END]]erformance requirements related to the communication between the DT and the end-user device accessing the DT (including network to UE communication) are not included in this table and may depend on the actual application (e.g. web, immersive/XR etc[[SUGGESTION_START]].[[SUGGESTION_END]]) used to access the DT
Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].3 Network managed Localized Communication Consolidated Performance requirements
Table Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].3-1: Consolidated performance requirements for Network managed localized communication
Scenario
Service bit rate: user-experienced data rate
End-to-end latency
[11.20] Direct device communication in a local network - Factory
[≤ 1.5] Gbps
(note 1)
[5] ms
(note 1)
[11.20] Direct device communication in a local network - Vehicle
[0,1 to [1]] Gbps
(note 2)
[5] ms
(note 2)
NOTE 1: Refer to the KPI requirement in TS22.261 [14] Table 7.10.2-1.
NOTE 2: Refer to the performance of Gaming or Interactive Data Exchanging in TS22.261 [14] Table 7.6.1-1
Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].[[SUGGESTION_START]]4[[SUGGESTION_END]] Robotics Consolidated Performance requirements
Table Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].[[SUGGESTION_START]]4[[SUGGESTION_END]]-1: Consolidated performance requirements for Robotics services
Scenario
Max allowed end-to-end latency
Application data rate
Area traffic capacity
Reliability
Message size
Service area
Transfer interval
Connection density
UE speed
Remarks
[11.3] Cooperating Mobile Robots – 6G Network Services
[1 to 10] ms
[<10] Mbps
[<250] Mbps
(note 2)
–
[99.999 to 99.99999] %
(note 4)
(note 3)
[20 m² (indoor) up to
1 km2 (outdoor)]
(note 5)
[≤ 10] ms
[≤ 0.5] devices/ m²
(note 1)
[< 20] km/h
Robot to Network for services such as digital twin applications, AI/ML compute servers.
[11.9] AMRs performing tasks with low acceleration-sensitivity
(note 11)
[100] ms
(note 6)
(note 8)
–
DL: TBD
UL: -
[99.9999] %
(note 8)
–
>0.4 km
(note 9)
(note 10)
–
–
Up to [10] m/s
(note 7)
UE Type: AMR
[11.9] AMRs performing tasks with relatively high acceleration-sensitivity
(note 11)
[10] ms
(note 6)
(note 8)
–
DL: TBD
UL: -
[99.9999] %
(note 8)
–
>0.6 km
(note 9)
(note 10)
–
–
Up to [10] m/s
(note 7)
UE Type: AMR
NOTE 1: Local concentration of mobile robots, for instance, cooperative carrying assuming 8 robots on a space of 4 m x 4 m carrying an object.
NOTE 2: The higher data rate applies when e.g. immersive XR, AI/ML traffic, digital twin data, is exchanged. Otherwise, the lower data rate applies. It is expected uplink video will be most demanding.
NOTE 3: Any common message size to be expected. Message size depends on type of exchanged data, for instance, sensor data, control data, video streams, immersive XR, AI/ML traffic, digital twin data, etc.
NOTE 4: The control of real-time industrial processes requires very high service reliability. Can be lower for non-real-time digital twin or in other contexts (e.g. training, engineering, planning).
NOTE 5: Service area depends on scenario with cooperating mobile robots and on size of location/collaboration area for the scenario. Examples for service areas are the working area of collaborative carrying mobile robots (see 11.3.3.1), a factory floor or a workshop, a construction site deploying autonomous robots that work in unison to construct a building (see 11.3.3.2), or a farmer’s field in autonomous farming with self-driving, smart machines that work collaboratively in agricultural operations.
NOTE 6: The latency considered for up to two hops only. The latency is measured in the time interval between the time that collaborative awareness data is ready to be sent and the time that the data is received so that the information can be delivered to a nearby UE to prepare for necessary action to take.
NOTE 7: Relative speed between a pair of UEs (AMRs) is considered.
NOTE 8: The latency and reliability may vary depending on the type of application and the role of AMR performing different sub-tasks.
NOTE 9: It is assumed that AMRs with heavy payloads are operating passively (e.g. at a very low acceleration level) such that the time it takes for them to make a complete stop is long (e.g. more than 20 seconds).
NOTE 10: This means the minimum required distance when the UEs need to start communication in order to provide minimum ample time for UEs (AMRs) to prepare.
NOTE 11: This includes some scenarios where there are different AMRs using different NPNs.
Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].[[SUGGESTION_START]]5[[SUGGESTION_END]] Other industry and vertical Consolidated Performance requirements
Table Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].[[SUGGESTION_START]]5[[SUGGESTION_END]]-1: Consolidated performance requirements for services with high reliability and[[SUGGESTION_START]]/or[[SUGGESTION_END]] low latency
Scenario
Communication service availability: target value
Communication service reliability: mean time between failures
End-to-end latency
Experienced Data rate
[[SUGGESTION_START]]Peak Data rate[[SUGGESTION_END]]
Message size
Transfer interval: target value
UE speed
Number of UE
Service area
[[SUGGESTION_START]]Others[[SUGGESTION_END]]
[11.24] Direct Transfer Trip
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[> 99.999] %
–
[< 5-10] ms
DL: [0.05 – 5][[SUGGESTION_START]] Mbps[[SUGGESTION_END]]
UL: [0.05 – 5][[SUGGESTION_START]] Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[1500] [[SUGGESTION_START]]b[[SUGGESTION_END]]yte[[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[100] ms
–
[2[[SUGGESTION_START]]-[[SUGGESTION_END]]10] devices
–
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[11.26] Decentralized Local Grid Power Contract
99.999 %
–
[1] ms
–
[[SUGGESTION_START]]–[[SUGGESTION_END]]
< 100 [[SUGGESTION_START]]b[[SUGGESTION_END]]yte[[SUGGESTION_START]]s[[SUGGESTION_END]]
(note [[SUGGESTION_START]]3[[SUGGESTION_END]])
50Hz: 20 ms
60Hz: 17 ms
[[SUGGESTION_START]]–[[SUGGESTION_END]]
< 200 devices
(note [[SUGGESTION_START]]4[[SUGGESTION_END]])
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[11.8] AR guided task
(note [[SUGGESTION_START]]5[[SUGGESTION_END]])
–
Up to [99.999] %
[1[[SUGGESTION_START]]-[[SUGGESTION_END]]5] ms
(note [[SUGGESTION_START]]6[[SUGGESTION_END]])
DL: [240 – 500][[SUGGESTION_START]] Mbps[[SUGGESTION_END]]
UL: [50][[SUGGESTION_START]] Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
–
–
Pedestrian
[20000] devices/km2
(note [[SUGGESTION_START]]7[[SUGGESTION_END]])
Factory
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]][11.25][[SUGGESTION_END]][[SUGGESTION_START]] Monitoring Environment / Telemetry[[SUGGESTION_END]]
[[SUGGESTION_START]][99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][100-500] ms[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [0.04][[SUGGESTION_END]][[SUGGESTION_START]] Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]][200-400][[SUGGESTION_END]] [[SUGGESTION_START]]b[[SUGGESTION_END]][[SUGGESTION_START]]ytes[[SUGGESTION_END]]
[[SUGGESTION_START]][1-15] m[[SUGGESTION_END]][[SUGGESTION_START]]in[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]8[[SUGGESTION_END]][[SUGGESTION_START]])[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]][10-15] devices per km of transmission line[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]Battery life: [[SUGGESTION_END]][[SUGGESTION_START]][10-20] years[[SUGGESTION_END]]
[[SUGGESTION_START]][11.25][[SUGGESTION_END]][[SUGGESTION_START]] Monitoring vibration[[SUGGESTION_END]]
[[SUGGESTION_START]][99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][100-500] ms[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [0.8] Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]][2-10] kbytes[[SUGGESTION_END]]
[[SUGGESTION_START]][1[[SUGGESTION_END]][[SUGGESTION_START]]5[[SUGGESTION_END]][[SUGGESTION_START]]-[[SUGGESTION_END]][[SUGGESTION_START]]60[[SUGGESTION_END]][[SUGGESTION_START]]] min[[SUGGESTION_END]]
[[SUGGESTION_START]](note 8)[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]][5-15] devices per km of transmission line[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]Battery life: [[SUGGESTION_END]][[SUGGESTION_START]][10-20] years[[SUGGESTION_END]]
[[SUGGESTION_START]][11.25] [[SUGGESTION_END]][[SUGGESTION_START]]Monitoring Arching/partial voltage leak[[SUGGESTION_END]]
[[SUGGESTION_START]][99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][5-500] ms[[SUGGESTION_END]]
[[SUGGESTION_START]](note 10)[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [24] Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]][300] kbytes[[SUGGESTION_END]]
[[SUGGESTION_START]][1-5] min[[SUGGESTION_END]]
[[SUGGESTION_START]](note 9)[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]][10-15] devices per km of transmission line[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]Battery life: [[SUGGESTION_END]][[SUGGESTION_START]][10-20] years[[SUGGESTION_END]]
[[SUGGESTION_START]][11.25] [[SUGGESTION_END]][[SUGGESTION_START]]Monitoring Grid assets & Photos on events[[SUGGESTION_END]]
[[SUGGESTION_START]][99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][100-500] ms[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [24] Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]][150-300] kbytes[[SUGGESTION_END]]
[[SUGGESTION_START]][500 ms-5[[SUGGESTION_END]][[SUGGESTION_START]] min[[SUGGESTION_END]][[SUGGESTION_START]]][[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]][10-15] devices per km of transmission line[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]Battery life: [[SUGGESTION_END]][[SUGGESTION_START]][10-20] years[[SUGGESTION_END]]
[[SUGGESTION_START]][11.25] [[SUGGESTION_END]][[SUGGESTION_START]]Monitoring Grid assets & videos on events[[SUGGESTION_END]]
[[SUGGESTION_START]][99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][99.999] %[[SUGGESTION_END]]
[[SUGGESTION_START]][100-500] ms[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [200] Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]][300-2000] kbytes[[SUGGESTION_END]]
[[SUGGESTION_START]][500 ms-5 min][[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]][10-15] devices per km of transmission line[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]Battery life: [[SUGGESTION_END]][[SUGGESTION_START]][10-20] years[[SUGGESTION_END]]
[[SUGGESTION_START]][11.28] [[SUGGESTION_END]][[SUGGESTION_START]]Medical ultrasound monitoring for patients at home (PLMN)[[SUGGESTION_END]]
[[SUGGESTION_START]]> 99.99 [[SUGGESTION_END]][[SUGGESTION_START]]%[[SUGGESTION_END]]
[[SUGGESTION_START]]> 1 month[[SUGGESTION_END]]
[[SUGGESTION_START]]< 100 s[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: [[SUGGESTION_END]][[SUGGESTION_START]]< 86 Mb[[SUGGESTION_END]][[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]](note [[SUGGESTION_END]][[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]]2)[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]stationary[[SUGGESTION_END]]
[[SUGGESTION_START]][[[SUGGESTION_END]][[SUGGESTION_START]]10[[SUGGESTION_END]][[SUGGESTION_START]]-100] devices[[SUGGESTION_END]][[SUGGESTION_START]]/km2[[SUGGESTION_END]]
[[SUGGESTION_START]]Urban and rural areas and indoor. (note [[SUGGESTION_END]][[SUGGESTION_START]]1[[SUGGESTION_END]][[SUGGESTION_START]]1)[[SUGGESTION_END]]
[[SUGGESTION_START]]Energy-efficient transmissions using a device powered with a 3.3V battery of capacity < 1000 mAh that can last at least several hours without recharging[[SUGGESTION_END]]
[[SUGGESTION_START]][11.28] [[SUGGESTION_END]][[SUGGESTION_START]]Medical ultrasound monitoring[[SUGGESTION_END]]
[[SUGGESTION_START]]for patients on the move[[SUGGESTION_END]]
[[SUGGESTION_START]](PLMN)[[SUGGESTION_END]]
[[SUGGESTION_START]]> 99.99[[SUGGESTION_END]][[SUGGESTION_START]] %[[SUGGESTION_END]]
[[SUGGESTION_START]]> 1 month[[SUGGESTION_END]]
[[SUGGESTION_START]]< 100 s[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: < 86 Mbps[[SUGGESTION_END]]
[[SUGGESTION_START]](note 12)[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]< 250 km/h[[SUGGESTION_END]]
[[SUGGESTION_START]][10-100] devices/km2[[SUGGESTION_END]]
[[SUGGESTION_START]]Urban and rural wide[[SUGGESTION_END]]
[[SUGGESTION_START]]Energy-efficient transmissions using a device powered with a 3.3V battery of capacity < 1000 mAh that can last at least several hours without recharging[[SUGGESTION_END]]
[[SUGGESTION_START]][11.29] [[SUGGESTION_END]][[SUGGESTION_START]]Distributed Energy Resources[[SUGGESTION_END]]
[[SUGGESTION_START]]99.999 9 %[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]3 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]5.4 Mb[[SUGGESTION_END]][[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]140 k[[SUGGESTION_END]][[SUGGESTION_START]]b[[SUGGESTION_END]][[SUGGESTION_START]]yte[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]≤ 1 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]Stationary[[SUGGESTION_END]]
[[SUGGESTION_START]]≤ 200 devices[[SUGGESTION_END]]
[[SUGGESTION_START]]20 km x 30 km[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]][11.29] [[SUGGESTION_END]][[SUGGESTION_START]]Electrical Distribution – Distributed automated switching for isolation and service restoration.[[SUGGESTION_END]]
[[SUGGESTION_START]]99.999 9 %[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]< 5 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]1.5 Mb[[SUGGESTION_END]][[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]1500 [[SUGGESTION_END]][[SUGGESTION_START]]b[[SUGGESTION_END]][[SUGGESTION_START]]yte[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]≥ 1 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]Stationary[[SUGGESTION_END]]
[[SUGGESTION_START]]20 devices[[SUGGESTION_END]]
[[SUGGESTION_START]]20 km x 30 km[[SUGGESTION_END]]
[[SUGGESTION_START]]Survival time: 5 ms[[SUGGESTION_END]]
[[SUGGESTION_START]][11.29] [[SUGGESTION_END]][[SUGGESTION_START]]Distributed Voltage Control[[SUGGESTION_END]]
[[SUGGESTION_START]]99.999 %[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]100 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]~100 [[SUGGESTION_END]][[SUGGESTION_START]]b[[SUGGESTION_END]][[SUGGESTION_START]]yte[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]200 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]stationary[[SUGGESTION_END]]
[[SUGGESTION_START]]400 devices[[SUGGESTION_END]]
[[SUGGESTION_START]]20 km x 30 km[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]][11.29] [[SUGGESTION_END]][[SUGGESTION_START]]Distributed energy storage – monitoring[[SUGGESTION_END]]
[[SUGGESTION_START]]> 99.9 %[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]< 10 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: 16 Mb[[SUGGESTION_END]][[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]s
DL: >100 kb[[SUGGESTION_END]][[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]800 k[[SUGGESTION_END]][[SUGGESTION_START]]b[[SUGGESTION_END]][[SUGGESTION_START]]yte[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]10 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]>10 dev[[SUGGESTION_END]][[SUGGESTION_START]]ices[[SUGGESTION_END]][[SUGGESTION_START]]/km2[[SUGGESTION_END]]
[[SUGGESTION_START]]20 km x 30 km[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]][11.29] [[SUGGESTION_END]][[SUGGESTION_START]]Distributed energy storage – data collection[[SUGGESTION_END]]
[[SUGGESTION_START]]> 99.9 %[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: <10 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: 1 s[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: 10.4 Mb[[SUGGESTION_END]][[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]DL: >100 kb[[SUGGESTION_END]][[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]UL: 1.3 M[[SUGGESTION_END]][[SUGGESTION_START]]b[[SUGGESTION_END]][[SUGGESTION_START]]yte[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]DL >100 kbyte[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
[[SUGGESTION_START]]1 s[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]>10[[SUGGESTION_END]][[SUGGESTION_START]] devices[[SUGGESTION_END]][[SUGGESTION_START]]/km2[[SUGGESTION_END]]
[[SUGGESTION_START]]20 km x 30 km[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]][11.29] [[SUGGESTION_END]][[SUGGESTION_START]]Ensuring uninterrupted communication service availability during emergencies.[[SUGGESTION_END]]
[[SUGGESTION_START]]99.999 9 %[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]100 ms[[SUGGESTION_END]]
[[SUGGESTION_START]]< 1 kb[[SUGGESTION_END]][[SUGGESTION_START]]p[[SUGGESTION_END]][[SUGGESTION_START]]s per distributed energy resource[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]Stationary[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]20 km x 30 km[[SUGGESTION_END]]
[[SUGGESTION_START]]–[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 1: DTT uses periodic and aperiodic communication service supporting messages for fault location, isolation, and service restoration.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 2: Typical GOOSE message sizes are 500-byte in IEC 61850 [356] messages plus any overhead due to IP tunnelling, security etc[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]][[SUGGESTION_START]] make 1500 kBytes approximately. DTT messages are always passed between 2 protection devices (UE).[[SUGGESTION_END]]
NOTE [[SUGGESTION_START]]3[[SUGGESTION_END]]: The typical message size for instantaneous electric consumption information time stamped.
NOTE [[SUGGESTION_START]]4[[SUGGESTION_END]]: This is maximum users in a contract in France today.
NOTE [[SUGGESTION_START]]5[[SUGGESTION_END]]: All the values in this table are targeted values and not strict requirements. The DL data rate is assumed 4K video flow + control information + audio data with data compression ratio of 1:25 to 1:50; and UL data rate is assumed video flow 25 Mbps [24] + high resolution depth sensor data 25 Mbps + audio data.
NOTE [[SUGGESTION_START]]6[[SUGGESTION_END]]: It is for downlink or uplink one way delay.
NOTE [[SUGGESTION_START]]7[[SUGGESTION_END]]: Not all UEs are consuming the same 3GPP services at the same time.
[[SUGGESTION_START]]NOTE 8: Immediate transmission when a threshold is crossed[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 9: Monitoring frequency varies depending on what is monitored and during which period of the year the voltage is monitored, Winter months are less frequent than summer months[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 10: 5–15 ms is required only when the operator chooses to use the network in a rapid protection loop[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 11: “deep indoor” term is meant to be places like e.g. elevators, building’s basement, underground parking lot[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 12: The ultrasound cardiac-assessment patch must be able to transmit single-plane, B-mode (i.e. grey-scale, 2-dimensional) moving images to the edge. A resolution of 600 x 600 pixels with 8-bit pixel depth and a 30 fps framerate is expected. As the possibilities for image compression are limited (e.g. considering the quality requirements for medical imagery), this leads to uplink data generation at rates of up to 86 Mbps. One or a few assessments will be made per day and each assessment involves recording 1 minute of data. Transmission may be spread out over time to some extent (e.g. 20 Mbps for 5 minutes).[[SUGGESTION_END]]
Table Y.2.[[SUGGESTION_START]]x[[SUGGESTION_END]].[[SUGGESTION_START]]5[[SUGGESTION_END]]-2: Consolidated performance requirements for Positioning for Further Use Cases on Industry and Verticals
Scenario
Positioning Accuracy
(95 % confidence level)
Positioning service availability
Positioning service latency
Number of UE
UE speed
UE type
Service area
Horizontal Accuracy
Vertical Accuracy
[11.8] AR remote guided task – outdoor
[1-3] m
(note 5)
[3-5] m
(note 5)
[99.9] %
(note 6)
[15] ms
(note 7)
TBD
(note 8)
–
–
TBD
(note 9)
[11.8] AR remote guided task – indoor factory
[0,2-1] m
(note 5)
[1-3] m
(note 5)
[99.9] %
(note 6)
[15] ms
(note 7)
≥200
(note 8)
–
–
200,000m2
(note 9)
[11.3] Cooperating Mobile Robots – Robot localization
[1] m
(note 2)
–
–
–
–
[< 20] km/h
–
–
[11.3] Human presence detection for mobile Robots
[40] cm
(note 3)
–
–
–
–
Pedestrian
–
–
[11.3] Cooperating Mobile Robots
[<10] cm
(note 1)
–
–
–
–
slow (note 4)
–
–
[11.4] Real-Time Digital Twin
[≤ 10] cm
–
–
[1-10] ms
(note 14)
[1-10] devices/m2
Up to vehicular speed
–
[up to 1] km2
(note 13)
[11.16] Critical Infrastructure Monitoring – Outdoor
[10] cm
(note 10)
[1] m
[99] %
[100] ms
(note 11)
–
stationary
Infrastructure mounted
–
[11.17] Machine Guidance, Machine Control – Outdoor
[2-10] cm
(note 12)
[10] cm
[99] %
[30] s
–
[5] km/h
Machinery mounted
–
[[SUGGESTION_START]]NOTE 1:[[SUGGESTION_END]] Collaborative tasks such as handing over material and objects or robot navigation in close distance to other objects require more accurate positioning.
NOTE 2: Tasks like robot localization can work with less accurate positioning.
NOTE 3: Identification of human presence in areas where people are around: [40] [[SUGGESTION_START]]cm [[SUGGESTION_END]]resolution within an area defined by [1] [[SUGGESTION_START]]m [[SUGGESTION_END]]range around the robot [112][[SUGGESTION_START]].[[SUGGESTION_END]]
NOTE 4: Velocity of robots is usually slow and adapted to the localization performance in such situations.
NOTE 5: The positioning accuracies are based on the UEs on boarded on the AR devices and factory equipment which are used to operate AR remote guided tasks. They are almost same as the values in level 5 in TS 22.261 Table 7.3.2.2-1.
NOTE 6: The availability reuses the values in level 4 in TS 22.261 Table 7.3.2.2-1.
NOTE 7: It reuses the values in level 4 in TS 22.261 Table 7.3.2.2-1.
NOTE 8: The number of positioning UE is the number of UEs which the 6G system is asked to position simultaneously in the positioning service area with the required positioning performance[[SUGGESTION_START]].[[SUGGESTION_END]]
NOTE 9: The 6G positioning service area is based on the typical single industry factory size.
NOTE 10: Assuming the UEs are installed at each story of a building with story height of 4m.
NOTE 11: Assuming the frequency of an earthquake is 10 Hz.
NOTE 12: Assuming the similar performance as RTK-GNSS [231].
NOTE 13: Service coverage both outdoor & indoor (e.g. industrial plant, city area) [31][[SUGGESTION_START]].[[SUGGESTION_END]]
NOTE 14: Very low latency for the Real-time aspect, both in UL (to report up-to-date data) and DL (to send timely controls)[[SUGGESTION_START]].[[SUGGESTION_END]]
* * * End of changes * * * *
|
S1-260046.zip
|
2026-01-21T09:30:34.613987
|
S1-260067
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
|
Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG SA WG 1 Meeting #112 Ad Hoc - e S1-260067
12-16 January 2026, electronic meeting (revision of S1-26xxxx)
Source: CATT
pCR Title: Update the table titles of 14.1.11 ubiquitous connectivity
Draft Spec: 3GPP TR22.870 v1.0.1
Agenda item: 1.4
Document for: Approval
Contact: Qing Wan <wanqing1@cictmobile.com>
Abstract: The contribution intends to update the table titles of clause 14.1.11 for the consolidation of Ubiquitous Connectivity.
1. Introduction
The tablel titles of clause 14.1.11 are left for furthre update regarding the discussion in SA1#112. This contribution intends to clarify how to group the consolidated requirements and entitle the tables correspondingly.
2. Reason for Change
The consolidation for ubiquitous connectivity has got good progress in SA1#112 and the temporary results have been captured in S1-254410. Besides, there are additional 6 PRs to be considered for the consolidation as below.
PR8.17.6-1 and PR8.17.6.2 for satellite backhaul.
PR8.19.6-1 with EN for HAPS communication
PR 8.20-1 and PR 8.20-2 for communication between cooperating UEs.
PR 8.21.6-1 for satellite access
From the service and focus perspective, all the CPRs under discussion and PRs can be categorized into 3 groups:
Group1- the requirements relevant to the communication capability with satellite access and satellite backhaul;
Group2- the requirements relevant to determing position, velocity and time based on satellite-based positioning capability;
Group3- the requirements relevant to other NTN such as HAPS, other non-communication capabilities of satellite, etc, not belonging to either Group1 or Group2.
Thus, Table 14.1.11-1 can be entitled as Satellite-based communication. Table 14.1.11-2 can be entitled as Satellite-based positioning. Table 14.1.11-3 can be entitled as Other aspects.
3. Conclusions
<Conclusion part (optional)>
4. Proposal
It is proposed to agree the following changes to 3GPP TR22.870 v1.0.1.
* * * First Change * * * *
14.1.11 Ubiquitous Connectivity
[[SUGGESTION_START]]Editor’s Note: Table[[SUGGESTION_END]][[SUGGESTION_START]]14.1.11-[[SUGGESTION_END]][[SUGGESTION_START]]1 [[SUGGESTION_END]][[SUGGESTION_START]]will [[SUGGESTION_END]][[SUGGESTION_START]]include CPRs about [[SUGGESTION_END]][[SUGGESTION_START]]the communication with [[SUGGESTION_END]][[SUGGESTION_START]]satellite access and[[SUGGESTION_END]][[SUGGESTION_START]]/or[[SUGGESTION_END]][[SUGGESTION_START]] satellite backhaul; Table[[SUGGESTION_END]][[SUGGESTION_START]]14.1.11-[[SUGGESTION_END]][[SUGGESTION_START]]2 [[SUGGESTION_END]][[SUGGESTION_START]]will [[SUGGESTION_END]][[SUGGESTION_START]]include CPRs about determining position, velocity [[SUGGESTION_END]][[SUGGESTION_START]]and/or[[SUGGESTION_END]][[SUGGESTION_START]] time[[SUGGESTION_END]] [[SUGGESTION_START]]based on[[SUGGESTION_END]] [[SUGGESTION_START]]satellite-based positioning[[SUGGESTION_END]] [[SUGGESTION_START]]capability[[SUGGESTION_END]][[SUGGESTION_START]]; Table[[SUGGESTION_END]][[SUGGESTION_START]]14.1[[SUGGESTION_END]][[SUGGESTION_START]].11-3 [[SUGGESTION_END]][[SUGGESTION_START]]will [[SUGGESTION_END]][[SUGGESTION_START]]include CPRs relevant to [[SUGGESTION_END]][[SUGGESTION_START]]other NTN such as HAPS[[SUGGESTION_END]][[SUGGESTION_START]], or[[SUGGESTION_END]][[SUGGESTION_START]] other non-communicatio[[SUGGESTION_END]][[SUGGESTION_START]]n [[SUGGESTION_END]][[SUGGESTION_START]]capabilities[[SUGGESTION_END]] [[SUGGESTION_START]]of[[SUGGESTION_END]][[SUGGESTION_START]] satellite[[SUGGESTION_END]][[SUGGESTION_START]], etc[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
Table 14.1.11-1: Satellite-based communication
CPR #
Consolidated Potential Requirement
Original PR #
Comment
14.1.11-1-1
Table 14.1.11-2: Satellite-based positioning
CPR #
Consolidated Potential Requirement
Original PR #
Comment
14.1.11-2-1
Table 14.1.11-3: Other[[SUGGESTION_START]] aspect[[SUGGESTION_END]][[SUGGESTION_START]]s[[SUGGESTION_END]]
CPR #
Consolidated Potential Requirement
Original PR #
Comment
14.1.11-3-1
* * * End of Change * * * *
|
S1-260067.zip
|
2026-01-21T09:31:09.286240
|
S1-260073
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
|
Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG-SA WG1 Meeting #112-Ad Hoc-e S1-260073
12-16 January 2026, Online (revision of xxxx)
Source: 6G Study Rapporteurs
pCR Title: Pseudo-CR on updating definition of SHE (Service Hosting Environment) and computing section skeleton
Draft Spec: 3GPP TR 22.870 v1.0.1
Agenda item: 1.4
Document for: Approval
Contact: Xiaonan Shi (shixiaonan@chinamobile.com) and Jean Trakinat (jean.trakinat1@t-mobile.com)
Abstract: This paper proposes to update the SHE definition and computing skeleton in TR.
1. Introduction
This paper proposes two changes.
The first change is to change the definition of Service Hosting Environment.
And the second change is to change the current CPR skeleton that to adding the new section to put all the computing related requirements into this new section.
2. Reason for Change
The current definition of Service Hosting Environment (SHE) was agreed in several meetings ago. The details can be seen below:
Service Hosting Environment: the environment, located inside of 6G network and fully controlled by the operator, where Hosted Services are offered from.
But in this definition, the SHE still includes the RAN as the potential computing service provider. But from the operator point of view, that RAN as computing service provider may have some drawbacks:
Firstly, if computing resources are deployed in the RAN, the size and wide distribution of RAN nodes may result in a corresponding deployment of computing resources at each RAN node, which brings the challenges to equipment operation and maintenance, power supply, security, etc.
Secondly, generally the deployment locations of base stations are relatively close to users and these locations may not have be able to provide a suitable environmental condition for data centers, making it difficult to deploy computing resources on a large scale. Large-scale data center deployments with their high requirements for the stability also reaise a larger energy requirement for these sites..
Lastly, the computing resources deployed in the core network facilitate data aggregation and analysis, and can better support applications such as AI training and big data analysis that require global data. Using the computing resources on the RAN side would increase data traffic, making it difficult to achieve data collaboration and aggregation.
3. Conclusions
SA1 needs to conclude on the computing aspects in this study. In the current TR, the requirements for computing are distributed across multiple chapters such as AI, immersive experiences, and sensing. The demand for computing has permeated into various aspects of the 6G business scenarios. We need to separately present the computing section as a separate chapter to discuss the common computing requirements for each scenario, so that in the first version of 6G, computing services can be regarded as one of the important features of 6G and be standardized.
Propose to update the deinition of Service Hosting Environment and change the skeleton of CPR.
4. Proposal
It is proposed to agree the following changes to 3GPP TR 22.870 v1.0.1.
* * * First Change * * * *
3 Definitions of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the terms given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
6G AI Service: a service provided by the 6th Generation (6G) network where AI functionalities (e.g. AI model training, AI model management or AI inference) are made available to a subscriber/user or an authorized application on the User Equipment (UE) or on an application server (AS).
6G Computing Service: a service provided by 6G network utilizing computing resources in Service Hosting Environment, which can be used by a subscriber (via UE)/3rd party.
NOTE 1: The computing resources can refer to hardware and/or software that provides the required processing, storage capability etc. to perform computational tasks (e.g. XR rendering).
6G System Data: the data that is controlled by the 6G system and can be generated or collected by the 6G system.
NOTE 2: 6G system data is different from traditional user traffic data which is application level data being transmitted through the 3GPP system for user related services.
6G Wireless sensing: 6G system feature providing capabilities to get information about characteristics of the environment and/or objects within the environment (e.g. shape, size, orientation, speed, location, distances or relative motion between objects, etc.) using radio frequency signals.
NOTE 3: The 6G Wireless sensing service can use data acquired with either NR-based radio signals, non-3GPP radio signals, or a combination.
AI Agent: an automated intelligent entity that achieves a specific goal (autonomously or not) on behalf of another entity, by e.g. interacting with its environment, acquiring contextual information, reasoning, self-learning, decision-making, executing tasks (independently or in collaboration with other AI Agents)
Cooperating UEs: a group of UEs that have associated subscriptions allowing them to cooperate when they are in proximity of each other, in coordination with the network, e.g. to improve energy efficiency and user experience.
Digital Twin: a real time representation of physical assets in a digital world.
NOTE 4: This definition was taken from ITU-T Recommendation Y.3090 [113].
Energy Supply: the delivery of electricity to a physical location. This is typically realized by placing two or more wires coming from a DSO at a geographical location and connecting those wires to a metering device.
NOTE 5: This definition was taken from TS 28.318 [232].
Indirect device connection: the connection between two UEs with a relay UE in the middle.
NOTE 6: Direct device connection and (in)direct network connection are defined in TS 22.261 [14].
Intelligent assistance service: a 3GPP service to help subscribers and third-party applications perform their tasks or services, e.g. using an AI Agent.
NOTE 7: For example, intelligent assistance service in the context of autonomous driving can be to support collision avoidance, parking assistance, emergency trajectory alignment, automated intersection-crossing, etc.
Intelligent Communication Assistant: the virtual intelligent communication assistant locates in operator network and interacts with the users through voice, video, text, gestures or other modalities. The assistant can be customized for each particular user by accessing user data and network data which are stored or collected in the network, with user’s consent. It can provide various communication services and support individual users based on user’s intention and requirement utilizing AI capability. One subscriber can have one or more Intelligent Communication Assistants.
Intent: expectations including requirements, goals and constraints without specifying how to achieve them. [147]
NOTE 8: Intent enables interaction between users/subscribers/3rd party and 6G system when utilizing 3GPP services, and for facilitating Operations, Administration, and Management (OAM) of 6G network.
Maximum slice energy credit limit: a policy establishing an upper bound on the aggregate quantity of energy consumption by the 6G system to provide services for a specific slice, e.g. in kilowatt hours.
Network Digital Twin: virtual replica of (part of) a mobile network to emulate (or simulate) the behaviour of the actual network.
Editor’s Note: it is FFS to update this definition.
Network Federation: refers to the interoperability of two or more 6G networks, enabling them to share resources and services, to achieve shared objectives. Federated 6G networks maintain their autonomy but coordinate to share resources, or services, ensuring mutual benefits without compromising individual operational control or data privacy.
NOTE 9: Network federation is currently defined in TS 28.538 [257], TS 23.558 [52] and allows Mobile Network Operators (MNOs) to share edge computing resources.
non-3GPP sensing station: a device capable of emitting and/or receiving non-3GPP radio signals specified in IEEE 802.1bf [201] that can result in acquisition of non-3GPP sensing data.
NOTE 10: The non-3GPP sensing station is owned, operated and deployed by the network operator or its business partner, including scenarios in which the equipment is owned and operated by the customer of the network operator.
Personal Data: any information relating to a user or subscriber that can be used to, either directly or indirectly, identify that user or subscriber, or to distinguish that user or subscriber from others.
Satellite access: direct connectivity between the UE and the satellite.
Satellite Constellation: a set of satellites working together as a system or network. A satellite constellation can be composed of satellites in the same orbit types or different orbits (GSO, NGSO) with different characteristics.
Sensing target density: total number of objects to be sensed per geographic area. It is a measure of how many objects the 3GPP system can detect, identify and/or track within a target sensing area.
Service Hosting Environment: the environment, located inside of 6G network [[SUGGESTION_START]](excluding RAN) [[SUGGESTION_END]]and fully controlled by the operator, where Hosted Services are offered from.
Serving satellite: a satellite providing the satellite access to a UE. In the case of NGSO (Non-Geostationary Satellite Orbit), the serving satellite is always changing due to the nature of the constellation.
* * * Second Change, all new text * * * *
[[SUGGESTION_START]]14.1.9 Computing [[SUGGESTION_END]]
[[SUGGESTION_START]]Editor’s Note: This includes (but not limited to) PRs from clause 6, 9, 11, W. [[SUGGESTION_END]] [[SUGGESTION_START]]Description under this section refers to services or features provided by 6G network utilizing computing resources in Service Hosting Environment, which can be used by a subscriber (via UE)/3rd party.[[SUGGESTION_END]]
[[SUGGESTION_START]]Table 14.1.9-1 – General Computing requirements[[SUGGESTION_END]]
[[SUGGESTION_START]]CPR #[[SUGGESTION_END]]
[[SUGGESTION_START]]Consolidated Potential Requirement[[SUGGESTION_END]]
[[SUGGESTION_START]]Original PR #[[SUGGESTION_END]]
[[SUGGESTION_START]]Comment[[SUGGESTION_END]]
[[SUGGESTION_START]]14.1.9-1-1[[SUGGESTION_END]]
[[SUGGESTION_START]]Table 14.1.9-2 – Computing aspects related to AI[[SUGGESTION_END]]
[[SUGGESTION_START]]CPR #[[SUGGESTION_END]]
[[SUGGESTION_START]]Consolidated Potential Requirement[[SUGGESTION_END]]
[[SUGGESTION_START]]Original PR #[[SUGGESTION_END]]
[[SUGGESTION_START]]Comment[[SUGGESTION_END]]
[[SUGGESTION_START]]14.1.9-[[SUGGESTION_END]][[SUGGESTION_START]]2[[SUGGESTION_END]][[SUGGESTION_START]]-1[[SUGGESTION_END]]
[[SUGGESTION_START]]Table 14.1.9-3 – Computing aspects related to Immersive communication[[SUGGESTION_END]]
[[SUGGESTION_START]]CPR #[[SUGGESTION_END]]
[[SUGGESTION_START]]Consolidated Potential Requirement[[SUGGESTION_END]]
[[SUGGESTION_START]]Original PR #[[SUGGESTION_END]]
[[SUGGESTION_START]]Comment[[SUGGESTION_END]]
[[SUGGESTION_START]]14.1.9-[[SUGGESTION_END]][[SUGGESTION_START]]3[[SUGGESTION_END]][[SUGGESTION_START]]-1[[SUGGESTION_END]]
[[SUGGESTION_START]]Table 14.1.9-4 – Computing aspects related to Industry&Vetical[[SUGGESTION_END]]
[[SUGGESTION_START]]CPR #[[SUGGESTION_END]]
[[SUGGESTION_START]]Consolidated Potential Requirement[[SUGGESTION_END]]
[[SUGGESTION_START]]Original PR #[[SUGGESTION_END]]
[[SUGGESTION_START]]Comment[[SUGGESTION_END]]
[[SUGGESTION_START]]14.1.9-[[SUGGESTION_END]][[SUGGESTION_START]]4[[SUGGESTION_END]][[SUGGESTION_START]]-1[[SUGGESTION_END]]
* * * End of Change * * * *
|
S1-260073.zip
|
2026-01-21T09:31:48.229495
|
S1-260076
|
SA1
|
TSGS1_112-Ad_Hoc-e
|
pCR
|
available
|
Consolidation of service and performance requirements to FS_6G-REQ [SP-241391]
|
3GPP TSG SA WG 1 Meeting #112 Ad Hoc - e S1-260076
12-16 January 2026, electronic meeting (revision of S1-26xxxx)
Source: Samsung
pCR Title: 22.870 pCR on Subscriber Permission
Draft Spec: TR 22.870
Agenda item: 1.4 (Consolidation of performance requirement to FS_6G-REQ)
Document for: Approval
Contact: Erik Guttman
Abstract: The term 'subscriber permission' will be used in TR 22.870 but has not yet been defined. This pCR proposes a definition for this term.
1. Introduction
During the course of the FS_6G_REQ study, user consent requirements for 6G have been discussed. It is clear that the term 'user consent' has been used for different purposes and this complicated the discusion.
- Default OFF
Some services are 'not for the benefit of the subscriber' and are only turned on with explicit consent, e.g. MDT.
- Discretionary Service Use unless there is a Regulatory Requirement
Some services are only appropriate in certain contexts, some of which are regulated, some not, e.g. LCS. For emergency calls, location services are mandatory. For other purposes they are only allowed at the discretion of the user.
- Regulatory Requirement for Privacy
Some services could expose personal data (location, sensor data, usage data, etc.) There are regulatory requirements such as GDPR in the EU that require a consent before data can be collected, processed, divulged, etc. Also, there are requirements how this consent can be revised over time.
- Discretionary Service Use
Many services and functionality is desired, but only in certain contexts. For example, IMS call handling includes subscription parameters for call redirection. These parameters must be dynamically changed in order to ensure proper handling (e.g. when a single UE terminates more than one MSISDN, or some calls should be forwarded, etc.) The point is: some services only make sense to use in certain contexts (depending on where the UE is [at work, at home], at different times [vacation, working hours, etc.].)
The term 'user consent' conflated all of these, depending on where the term was used in SA1 specifications. To resolve this ambiguity, we support use of the term 'subscriber preference' for all aspects that are not 'subject to regulation.' Still, in order to accomodate the 'Discretionary' aspect outlined above, we propose to add additional clarification
2. Reason for Change
This pCR adds clarification what is meant by subscriber permission with respect to service requirements. This is needed since many groups in 3GPP currently debate the terminology and meaning of 'user consent requirements.'
In TR 21.905, 3GPP Terminology, Subscriber is defined as:
Subscriber: A Subscriber is an entity (associated with one or more users) that is engaged in a Subscription with a service provider. The subscriber is allowed to subscribe and unsubscribe services, to register a user or a list of users authorised to enjoy these services, and also to set the limits relative to the use that associated users make of these services.
The highlighted text expresses that subscriptions are dynamic and the use of services can be conditional, e.g. on where and when the service is to be used. Though the term 'subscriber preference' has not been defined, there are examples of use of 'subscriber preference' in 3GPP specifications. For example, TS 22.142 (SMS value added services):
The VAS-SMS shall be able to support a request from an application to query/change the subscriber’s preferences for a certain service, for example:
i) To add or delete or modify a subscriber’s filtering conditions by which VAS-SMS can refuse some of the subscriber’s incoming messages.
ii) To modify a subscriber’s signature that will be appended to an SM sent from the subscriber.
iii) To modify a subscriber’s forwarding address that substitutes for the subscriber’s original receiving address.
The 'Generic User Profile (GUP)' specified in TS 22.240 states:
5.1 Subscriber Requirements
For a subscriber’s services, that support and are supported by GUP:
- The subscriber shall be able to customise her subscribed services and interrogate customisation settings, subject to limitations by the Home operator and/or value added service provider. The user interface for customisation/interrogation is service specific and out of scope of this specification.
5.1.1 User Requirements
For a user’s services, that support and are supported by GUP:
- The user shall be able to customise the services , that have been subscribed to her by the subscriber and interrogate customisation settings, subject to limitations by the Home operator and/or value added service provider and/or subscriber. The user interface for customisation/interrogation is service specific and out of scope of this specification.
GUP was considered as a way to support complex services offered by 3GPP operators that were envisioned in the 3G period, such as internet applications offered over IMS, SMS value added services, multicast services, custom ring tones. In 4G and 5G, the focus was on support of data services and decreasingly on services that required subscriber or user exposed preferences. An important counter example was WebRTC, where specific configuration and consent was required to connect all the pieces. See TS 22.XXX.
The considerations that led to GUP are relevant again in 6G, as many of the use cases and requirements in TR 22.870 consider capabilities that can be considered unacceptable - not as a regulatory consideration - in certain contexts (e.g. Sensing at the work place), or unacceptable drain on the battery life of the terminal (e.g. AI training or inference processing), or unacceptable exposure of confidential information (e.g. gathering, processing and exposing UE data collected from IoT devices deployed for sensitive purposes by the subscriber.)
It is not proposed here to revive GUP or create a normative citation to it in the 6G specification. This would require further discussion. The proposal at this point is to capture the essential concept from GUP in a newly added definition, to clarify the purpose of subscriber preference as it is used in TR 22.870.
3. Conclusions
<Conclusion part (optional)>
4. Proposal
It is proposed to agree the following changes to 3GPP TS / TR <TS/TR number and version>.
* * * First Change * * * *
3.1 Terms
For the purposes of the present document, the terms given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
6G AI Service: a service provided by the 6th Generation (6G) network where AI functionalities (e.g. AI model training, AI model management or AI inference) are made available to a subscriber/user or an authorized application on the User Equipment (UE) or on an application server (AS).
6G Computing Service: a service provided by 6G network utilizing computing resources in Service Hosting Environment, which can be used by a subscriber (via UE)/3rd party.
NOTE 1: The computing resources can refer to hardware and/or software that provides the required processing, storage capability etc. to perform computational tasks (e.g. XR rendering).
6G System Data: the data that is controlled by the 6G system and can be generated or collected by the 6G system.
NOTE 2: 6G system data is different from traditional user traffic data which is application level data being transmitted through the 3GPP system for user related services.
6G Wireless sensing: 6G system feature providing capabilities to get information about characteristics of the environment and/or objects within the environment (e.g. shape, size, orientation, speed, location, distances or relative motion between objects, etc.) using radio frequency signals.
NOTE 3: The 6G Wireless sensing service can use data acquired with either NR-based radio signals, non-3GPP radio signals, or a combination.
AI Agent: an automated intelligent entity that achieves a specific goal (autonomously or not) on behalf of another entity, by e.g. interacting with its environment, acquiring contextual information, reasoning, self-learning, decision-making, executing tasks (independently or in collaboration with other AI Agents)
Cooperating UEs: a group of UEs that have associated subscriptions allowing them to cooperate when they are in proximity of each other, in coordination with the network, e.g. to improve energy efficiency and user experience.
Digital Twin: a real time representation of physical assets in a digital world.
NOTE 4: This definition was taken from ITU-T Recommendation Y.3090 [113].
Energy Supply: the delivery of electricity to a physical location. This is typically realized by placing two or more wires coming from a DSO at a geographical location and connecting those wires to a metering device.
NOTE 5: This definition was taken from TS 28.318 [232].
Indirect device connection: the connection between two UEs with a relay UE in the middle.
NOTE 6: Direct device connection and (in)direct network connection are defined in TS 22.261 [14].
Intelligent assistance service: a 3GPP service to help subscribers and third-party applications perform their tasks or services, e.g. using an AI Agent.
NOTE 7: For example, intelligent assistance service in the context of autonomous driving can be to support collision avoidance, parking assistance, emergency trajectory alignment, automated intersection-crossing, etc.
Intelligent Communication Assistant: the virtual intelligent communication assistant locates in operator network and interacts with the users through voice, video, text, gestures or other modalities. The assistant can be customized for each particular user by accessing user data and network data which are stored or collected in the network, with user’s consent. It can provide various communication services and support individual users based on user’s intention and requirement utilizing AI capability. One subscriber can have one or more Intelligent Communication Assistants.
Intent: expectations including requirements, goals and constraints without specifying how to achieve them. [147]
NOTE 8: Intent enables interaction between users/subscribers/3rd party and 6G system when utilizing 3GPP services, and for facilitating Operations, Administration, and Management (OAM) of 6G network.
Maximum slice energy credit limit: a policy establishing an upper bound on the aggregate quantity of energy consumption by the 6G system to provide services for a specific slice, e.g. in kilowatt hours.
Network Digital Twin: virtual replica of (part of) a mobile network to emulate (or simulate) the behaviour of the actual network.
Editor’s Note: it is FFS to update this definition.
Network Federation: refers to the interoperability of two or more 6G networks, enabling them to share resources and services, to achieve shared objectives. Federated 6G networks maintain their autonomy but coordinate to share resources, or services, ensuring mutual benefits without compromising individual operational control or data privacy.
NOTE 9: Network federation is currently defined in TS 28.538 [257], TS 23.558 [52] and allows Mobile Network Operators (MNOs) to share edge computing resources.
non-3GPP sensing station: a device capable of emitting and/or receiving non-3GPP radio signals specified in IEEE 802.1bf [201] that can result in acquisition of non-3GPP sensing data.
NOTE 10: The non-3GPP sensing station is owned, operated and deployed by the network operator or its business partner, including scenarios in which the equipment is owned and operated by the customer of the network operator.
Personal Data: any information relating to a user or subscriber that can be used to, either directly or indirectly, identify that user or subscriber, or to distinguish that user or subscriber from others.
Satellite access: direct connectivity between the UE and the satellite.
Satellite Constellation: a set of satellites working together as a system or network. A satellite constellation can be composed of satellites in the same orbit types or different orbits (GSO, NGSO) with different characteristics.
Sensing target density: total number of objects to be sensed per geographic area. It is a measure of how many objects the 3GPP system can detect, identify and/or track within a target sensing area.
Service Hosting Environment: the environment, located inside of 6G network and fully controlled by the operator, where Hosted Services are offered from.
Serving satellite: a satellite providing the satellite access to a UE. In the case of NGSO (Non-Geostationary Satellite Orbit), the serving satellite is always changing due to the nature of the constellation.
[[SUGGESTION_START]]s[[SUGGESTION_END]][[SUGGESTION_START]]ubscriber [[SUGGESTION_END]][[SUGGESTION_START]]permission[[SUGGESTION_END]][[SUGGESTION_START]]: [[SUGGESTION_END]][[SUGGESTION_START]]Authorisations and limits resulting from actions by a subscriber to subscribe to and unsubscribe from services provided to a user or a list of users.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 11[[SUGGESTION_END]][[SUGGESTION_START]]: [[SUGGESTION_END]][[SUGGESTION_START]]Subscriber permissions can change over time and can depend on service-specific preferences or contextual conditions. Subscriber permissions are not limited to binary enable or disable states, e.g., they can define restrictions on when or where a service can be provided.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE [[SUGGESTION_END]][[SUGGESTION_START]]12: [[SUGGESTION_END]][[SUGGESTION_START]]Subscriber permissions can apply to the use, processing, or exposure of subscriber's data associated with the subscribed services. The subscriber determines which users, or set of users, have access to their data.[[SUGGESTION_END]]
[[SUGGESTION_START]]NOTE 13: [[SUGGESTION_END]][[SUGGESTION_START]]Subscriber permissions can apply to services that do not provide direct user benefits (e.g., mechanisms to provide [[SUGGESTION_END]][[SUGGESTION_START]]non-essential [[SUGGESTION_END]][[SUGGESTION_START]]operational information to the network). [[SUGGESTION_END]][[SUGGESTION_START]]A[[SUGGESTION_END]][[SUGGESTION_START]] subscriber [[SUGGESTION_END]][[SUGGESTION_START]]can decline[[SUGGESTION_END]][[SUGGESTION_START]] to provide subscriber permission, so that [[SUGGESTION_END]][[SUGGESTION_START]]terminal equipment resources (e.g., battery, computational resources) [[SUGGESTION_END]][[SUGGESTION_START]]are not used [[SUGGESTION_END]][[SUGGESTION_START]]for such services[[SUGGESTION_END]][[SUGGESTION_START]].[[SUGGESTION_END]]
* * * Next Change * * * *
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S1-260076.zip
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2026-01-21T09:32:13.484454
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