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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 non‑specific. - For a specific reference, subsequent revisions do not apply. -...
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3 Definitions of terms, symbols and abbreviations
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3.1 Terms
For the purposes of the present document, the terms given in 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 TR 21.905 [1]. example: text used to clarify abstract rules by applying them literally.
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3.2 Symbols
For the purposes of the present document, the following symbols apply: <symbol> <Explanation>
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3.3 Abbreviations
For the purposes of the present document, the abbreviations given in 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 TR 21.905 [1]. <ABBREVIATION> <Expansion>
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4 Security Assumptions
The AIMLE security should use authorization aspects specified in TS 33.434 [2] as baseline. The AIMLE server is deployed as a SEAL server, hence SEAL architecture is enhanced to incorporate the AIMLE service as shown below, where Figure 4-1 illustrates the service-based representation including AIMLE server as part of...
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5 Key Issues
Editor’s Note: This clause contains all the key issues identified during the study.
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5.1 Key Issue #1: Authorization for AIMLE Service Security for AIML members
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5.1.1 Key issue details
3GPP TS 23.482 [3] introduces support for AIMLE services, enabling AI/ML operations through interactions between the AIMLE client and AIMLE server(s) over the AIML-UU reference point, and between the VAL servers and AIMLE servers over AIML-S respectively. These services involve distributed AI/ML operations across mul...
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5.1.2 Security threats
Unauthorized AIMLE members (e.g., FL members) participating in AIMLE services may gain access to data exchanged between AIMLE clients and servers. Lack of robust authorization allows unreliable or unauthorized AIMLE members (e.g., FL members) to degrade the quality, efficiency, or availability of AIMLE operations.
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5.1.3 Potential security requirements
The 3GPP system shall support authorization mechanisms for AIML members (e.g., FL members) utilising AIMLE services for various AIMLE procedures.
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5.2 Key Issue #2: Secure AIMLE ML Model Access
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5.2.1 Key Issue details
TS 23.482 [3] describes AIMLE services which supports ML Model retrieval, ML model training, ML model management (model information storage and discovery) ML model update, and ML model selection aspects. AIMLE Services uses SEAL as the fundamental architecture and the authorization aspects of SEAL Security in TS 33.434...
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5.2.2 Security threats
Unauthorized AIMLE client(s)/ VAL server using AIMLE services may gain access to ML model data leading to leakage of model. Lack of robust authorization allows unauthorized AIMLE client(s) or VAL servers to degrade the quality, efficiency, or availability of AIMLE operations.
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5.2.3 Potential security requirements
The 3GPP system shall support authorization to secure AIMLE service-based ML Model operations such as retrieval, training, update, selection, and management (i.e., ML model information storage and discovery). 5.X Key Issue #X: <Key Issue Name> 5.X.1 Key Issue details 5.X.2 Security threats 5.X.3 Potential securit...
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6 Solutions
Editor’s Note: This clause contains the proposed solutions addressing the identified key issues.
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6.1 Solution #1: Authorization for AIMLE Services
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6.1.1 Introduction
This solution address KI#1.
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6.1.2 Solution details
AIMLE authorization related to AIML Services can reuse the authorization procedure specified in TS 33.434 [2] clause 5.2.2 (SEAL service authorization) and clause B.3.3 (SEAL service authorization) as the baseline where, SIM-S or AIMLE Server (with SIM capabilities) acts as an authorization server and issues access tok...
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6.1.3 Evaluation
The solution uses the SEAL service authorization procedure as baseline with the following impacts: To secure the SEAL based AIMLE Services, this solution provides enhancements to the access token claims (such as scope and audience) to indicate AIMLE procedure and information flow specific information to allow related...
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6.2 Solution #2: Authorization of AIMLE clients acting as FL members for access to AIMLE Service Security
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6.2.1 Introduction
This solution proposes the authorization of AIMLE clients in support of federated learning (FL). It ensures that only authorized clients (FL members) are selected, and that secure token-based verification is performed using authorization server. Tokens include only the minimum required claims such as ML model ID / Appl...
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6.2.2 Solution details
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6.2.2.1 The procedure for AIMLE clients’ authorization
Figure 6.2.2.1-1 Authorization’s procedure of AIMLE clients acting as FL members 1. The VAL server sends a FL member grouping support request to the AIMLE server. The request includes the requestor ID, security credentials, and FL grouping criteria (e.g., grouping method, member selection criteria). The initial requ...
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6.2.3 Evaluation
This solution ensures that only authorized AIMLE clients participate as members in FL process. It introduces a token-based authorization process handled by the AIMLE server with support from SEAL. Editor’s Note: Further evaluation is FFS.
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6.3 Solution #3: Re-using existing mechanisms
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6.3.1 Introduction
This solution addresses key issue #1 (Authorization for AIMLE Service Security for AIML members) and key issue #2 (Secure AIMLE ML Model Access) by re-using existing mechanisms available in SEAL security architecture.
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6.3.2 Solution details
There is no new interface specified for AIMLE services. Editor’s Note: Further analysis and clarification on interfaces are FFS. Thus, security for all the interfaces used in the AIMLE has already been addressed including the authorization aspects. Finer granular authorization such as who can be involved in the FL o...
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6.3.3 Evaluation
TBD
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6.4 Solution #4: Authorization for Secure AIMLE based ML Model Access
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6.4.1 Introduction
This solution address KI#2.
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6.4.2 Solution details
AIMLE authorization related to AIML Services can reuse the authorization procedure specified in TS 33.434 [2] clause 5.2.2 (SEAL service authorization) and clause B.3.3 (SEAL service authorization) as the baseline where, SIM-S or AIMLE Server (with SIM capabilities) acts as an authorization server and issues access tok...
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6.4.3 Evaluation
The solution uses the SEAL service authorization procedure as baseline with the following impacts: To secure the SEAL based AIMLE Services, this solution provides enhancements to the access token claims (such as scope and audience) to indicate AIMLE procedure and information flow specific information to allow related ...
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6.5 Solution #5: FL member authorization for AIMLE services
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6.5.1 Introduction
As specified in TS 23.482[3], the FL members consuming the AIMLE services are AIMLE or VAL server or VAL clients. There are several procedures defined such as ML model retrieval, ML model training, FL member registration, event subscription, AIMLE client registration/discovery/selection/participation so on. As most of...
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6.5.2 Solution details
For any interaction between AIMLE clients and the AIMLE server, the AIMLE client is provided with the access token by the SIM-S as specified in 5.2 of clause 33.434[2]. For any interaction between VAL server and AIMLE server (e.g., model training), the VAL server is provisioned with an access token by out of band mean...
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6.5.3 Evaluation
TBD 6.Y Solution #Y: <Solution Name> 6.Y.1 Introduction Editor’s Note: Each solution should list the key issues being addressed. 6.Y.2 Solution details 6.Y.3 Evaluation Editor’s Note: Each solution should motivate how the security requirements of the key issues being addressed are fulfilled.
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7 Conclusions
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7.1 Key Issue #1: Authorization for AIMLE Service Security for AIML members
Editor’s Note: This clause contains the agreed conclusions for Key Issue #1.
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7.2 Key Issue #2: Secure AIMLE ML Model Access
Editor’s Note: This clause contains the agreed conclusions for Key Issue #2. Annex A: Change history Change history Date Meeting TDoc CR Rev Cat Subject/Comment New version 2025-08 SA3#123 S3-252919 AIMLE Service Security TR Skeleton 0.0.0 2025-09 SA3#123 S3-253004 Included Contrib...
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1 Scope
This document studies potential security and privacy architecture and procedures for 6G mobile networks for improvement of existing services and support of new services, to meet the 6G system requirements and architecture. One goal of this document is to study how to create lean and streamlined standards for 6G, e.g....
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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 non‑specific. - For a specific reference, subsequent revisions do not apply. -...
f0d8ac8ba87e5fa314bc150073c98efb
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3 Definitions of terms, symbols and abbreviations
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3.1 Terms
For the purposes of the present document, the terms given in 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 TR 21.905 [1]. example: text used to clarify abstract rules by applying them literally.
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3.2 Symbols
For the purposes of the present document, the following symbols apply: <symbol> <Explanation>
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3.3 Abbreviations
For the purposes of the present document, the abbreviations given in 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 TR 21.905 [1]. <ABBREVIATION> <Expansion>
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4 Security areas and high level security requirements
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4.1 Security areas
Editor's Note: This clause further clarifies the scope of the study by listing the security areas that SA3 is working on. This document includes the following security areas: 1. Security Architecture deals with aspects such as identifying the different security domains and their characteristics, defining the di...
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4.2 Potential high level security requirements
Editor's Note: This clause will document high-level requirements that guide the study.
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5 Key issues
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5.1 Security area #1: Security architecture
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5.1.1 Introduction
This security area addresses the security principles, features, and requirements inherent to the security and trust architecture of 6G systems. This will lay the foundation for all the procedures and the mechanisms necessary to protect the communication and facilitate trust establishment between the UE and the network ...
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5.1.3 Key issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.1.3.y Key issue #1.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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5.2 Security area #2: RAN security
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5.2.1 Introduction
Purpose is to study potential attack vectors, vulnerabilities, security and privacy risks, impact and mitigations. This includes the following aspects: Editor’s Note: To be aligned with TR 38.914 [1] and TR 38.760-2 [2] as 6G RAN study progresses in RAN WGs. - Radio protocol stack, architecture and procedures Edito...
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5.2.2 Security assumptions
Editor's Note: This clause will document security assumptions related to each security area.
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5.2.3 Key issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.2.3.y Key issue #2.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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5.3 Security area #3: UE to Core Network Security
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5.3.1 Introduction
This security area studies how to establish and manage secure communication(s) between the UE and the Core Network. This includes the following aspects: Editor's Note: work is to be aligned and in coordination with TR 23.801-01 [4] based on SA2 progress. Editor's Note: Any potential NAS impact due to the use of AEAD...
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5.3.2 Security assumptions
Editor's Note: This clause will document security assumptions related to each security area.
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5.3.3 Key issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.3.3.y Key issue #1.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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5.4 Security area #4: Security for Core Network, Interconnect and Roaming
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5.4.1 Introduction
Study potential aspects to secure core network communication for the different communication modes within a network and between networks to mitigate threats identified. This is based on the system architecture requirements developed in TR 23.801-1 [6]. Editor's note: This study will be based on agreements documented ...
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5.4.2 Security assumptions
Editor's Note: This clause will document security assumptions related to each security area.
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5.4.3 Key issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.4.3.y Key issue #1.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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5.5 Security area #5: Subscription Authentication and Authorization
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5.5.1 Introduction
This security area includes the following security aspects related to authentication and authorization between the UE and the 6GS regardless of access type (i.e., 3GPP access and/or non-3GPP access): Editor’s Note: Whether trusted or untrusted non-3GPP access, or both are in scope is FFS. -Authentication, key agreem...
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5.5.2 Security assumptions
Editor's Note: This clause will document security assumptions related to each security area.
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5.5.3 Key issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.5.3.y Key issue #3.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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5.6 Security area #6: security and privacy aspects of network exposure
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5.6.1 Introduction
This security area covers the following aspects. - The security and privacy aspects of the exposure mechanism(s) defined in TR 23.801-01 [4]. Editor’s Note: Other aspects are FFS.
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5.6.2 Security assumptions
Editor's Note: This clause will document security assumptions related to each security area.
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5.6.3 Key issues
Editor’s note: This clause will contain the key issues that need to be addressed by SA3 on each security area. The exact contents are FFS. 5.6.3.y Key issue #1.y: <key issue name> Editor's Note: Key issues within the security area are not in any particular order but they are added incrementally (y = 1, 2, 3…) when n...
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6 Solutions
6.x Solutions to Security Area #x <security area name> 6.x.y Solutions to Key Issue #x.y <key issue name> 6.x.y.z Solution #x.y.z: <solution name> Editor's Note: Solutions are added incrementally (z = 1, 2, 3…) when new solution is identified. 'x' refers to the security area, y to the key issue. If a solution addre...
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7 Conclusions
Editor’s note: This clause will contain the overall conclusions made by SA3. The structure of this clause is FFS Annex A Attacker Model A.1 General Editor's Note: This clause includes an introduction to the attacker model. A.2 Architecture overview Editor's Note: This will need to be updated as work progress...
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1 Scope
The present document provides the description and investigation of new AI/ML based use cases, i.e., multi-hop UE trajectory, AI/ML based intra-CU LTM, and other handover enhancements.
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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 non‑specific. - For a specific reference, subsequent revisions do not apply. -...
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3 Definitions of terms, symbols and abbreviations
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3.1 Terms
For the purposes of the present document, the terms given in 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 TR 21.905 [1]. <Void>
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3.2 Symbols
For the purposes of the present document, the following symbols apply: <Void>
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3.3 Abbreviations
For the purposes of the present document, the abbreviations given in 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 TR 21.905 [1]. <Void>
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4 Use cases and Solutions
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4.1 Multi-hop UE trajectory
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4.1.1 Use case description
Editor’s Note: Capture the description of use case In Rel-18, the cell-based UE trajectory prediction is limited to the first-hop target NG-RAN node. Multi-hop predicted UE trajectory across gNBs consists of a list of cells belonging to gNBs where the UE is expected to connect and these cells are listed in chronologi...
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4.1.2 Solutions and standard impacts
Editor’s Note: Capture the solutions for the use case, including potential standard impacts on existing Nodes, functions, and interfaces
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4.1.2.1 Locations for AI/ML Model Training and AI/ML Model Inference
The following solutions are considered for supporting multi-hop UE trajectory: - AI/ML Model Training is located in the OAM and AI/ML Model Inference is located in the gNB. - AI/ML Model Training and AI/ML Model Inference are both located in the gNB. In case of CU-DU split architecture, the following solutions are p...
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4.1.2.2 Input data of multi-hop UE trajectory
To predict the multi-hop UE trajectory, a gNB may need the following information as input data: From the UE: - UE measurement report related to serving cell and neighbouring cells, e.g., RSRP, RSRQ, SINR - UE Mobility History Information From the neighbouring RAN nodes: - UE Mobility History Information From the ...
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4.1.2.3 Output data of multi-hop UE trajectory
The following information can be generated as output: - Multi-hop predicted UE trajectory, including a list of cells that UE is expected to connect to in chronological order and the associated expected time UE stays in the cell
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4.1.2.4 Feedback of multi-hop UE trajectory
To optimize the performance of multi-hop UE trajectory prediction, the following feedback can be considered to be collected from gNBs: - Measured UE Trajectory collected at each individual gNB
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4.1.2.5 Standard impact
Multi-hop UE trajectory prediction is transferred to the target NG-RAN nodes over the Xn interface via the Handover Preparation procedure. For the subsequent handovers, multi-hop predicted UE trajectory includes a list of cells where the UE is expected to connect to in chronological order. For the subsequent handovers...
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4.2 AI/ML assisted Intra-CU LTM
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4.2.1 Use case description
Editor’s Note: Capture the description of use case L1/L2 Triggered Mobility (LTM) is specified in TS 38.300 [2].  Intra-CU LTM is specified in TS38.401[3]. AI/ML can be used to optimise Intra-CU LTM procedures, e.g., to enhance Network and UE performance, optimize resource allocation and reduce mobility failures. A...
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4.2.2 Solutions and standard impacts
Editor’s Note: Capture the solutions for the use case, including potential standard impacts on existing Nodes, functions, and interfaces
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4.2.2.1 Locations for AI/ML Model Training and AI/ML Model Inference
For CU-DU split architecture, the following solutions are possible: - AI/ML Model Training is located in the OAM and AI/ML Model Inference is located in the gNB-CU. - AI/ML Model Training and Model Inference are both located in the gNB-CU.
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4.2.2.2 Input data of AI/ML based Intra-CU LTM
Editor’s Note: To be updated For AI/ML optimization of intra-CU LTM the following information can be considered as input data: - L3 measurement results - UE mobility history - Measured/Predicted radio resource status per cell/SSB area - Measured/Predicted cell-based UE trajectory - Historical UE’s candidate LTM ...
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4.2.2.3 Output data of AI/ML based Intra-CU LTM
For AI/ML optimization of Intra-CU LTM the following information can be considered as output data: - Candidate cell and beam for LTM HO Preparation- Target cell and beam selection for cell switch command - Cell(s) and beam(s) for early synchronization - Predicted TA value(s) for early UL synchronization
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4.2.2.4 Feedback of AI/ML based Intra-CU LTM
The following data is required as feedback data for intra-CU LTM: - LTM target cell - Measured TA value
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4.3 Other handover enhancements
Editor’s Note: Identify other handover enhancements via AI/ML, e.g., inter-CU LTM
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4.3.1 AI/ML assisted inter-CU LTM
Inter-CU LTM is specified in TS 38.300 [2]. AI/ML may be used to optimise inter-CU LTM procedures, e.g., for candidate cell selection. Editor’s Note: If applicable, use agreements for Intra-CU LTM as baseline for inter-CU LTM.
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5 Conclusion
Annex A: Change history Change history Date Meeting TDoc CR Rev Cat Subject/Comment New version 2025-10 RAN3#129-bis R3-256546 Skeleton for TR38.745 v0.0.0 0.0.0 2025-10 RAN3#129-bis R3-257331 TR 38.745 for Study on AI/ML for NG-RAN Phase 3 0.0.1 2025-10 RAN3#129-bis R3-257331 ...