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90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.6.3.2 Near-RT Massive MIMO Beam-based Mobility Robustness Optimization | The context of the Massive MIMO Beam-based Mobility Robustness Optimization is captured in table 4.6.3.2-1. Table 4.6.3.2-1: Beam-based Mobility Robustness Optimization Use Case Stage Evolution / Specification <<Uses>> Related use Goal Enable flexible optimization of the Beam-based Mobility Robustness Optimization by means of configuration parameter change with operator-defined objectives, and allow for AI/ML-based solutions. Actors and Roles Near-RT RIC acting as bMRO function, data collection function, and AI/ML model training function. E2 Nodes acting as configuration enforcement function. Assumptions E2 connectivity is established between Near-RT RIC and E2 Nodes. O1 connectivity is established between Near-RT RIC and SMO. Network is operational. Pre conditions Active Grid-of-Beams beam pattern is defined. Begins when Operator specified trigger condition is set or event is detected or periodically. Step 1 (M) Near-RT RIC collects necessary data from E2 Nodes and related GoB Beam Pattern Information and trains the AI/ML model with the collected data. Step 2 (M) Trained AI/ML models are executed in Near-RT RIC and infer for configuration parameter optimization based on the operator target. Step 3 (M) Continuously or upon trigger (e.g. change in the mMIMO Beam Pattern configuration, manual trigger etc.), Near-RT RIC configures optimized parameters in E2 Nodes (e.g. bCIO-s). Step 4 (M) Near-RT RIC monitors the network performance. If the algorithm performance is unsatisfactory in terms of predefined objective/requirement, Near-RT RIC initiates fallback mechanism to restore previous/default configuration. It can also gather necessary information and data to retrain and update the AI/ML model or trigger the optimization. Ends when Operator specified trigger condition or event is satisfied. Exceptions None identified. Post Conditions The E2 Nodes operate using the newly deployed parameters. The flow diagram of the Massive MIMO Beam-based Mobility Robustness Optimization is given in figure 4.6.3.2-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 40 ETSI ETSI TS 104 036 V12.0.0 (2025-04) 41 NOTE: On , ; one of the necessary inputs for training and inference of the bMRO function is the (current) GoB Beam Pattern (or alternative beam pattern) that is determined externally (in the SMO, in the Non-RT RIC, in the E2 Nodes, or in the Near-RT RIC by another function). The relevant GoB Beam Pattern Information shall be made available to the Near- RT RIC bMRO function both for training and inference. Depending on implementation, this can be achieved by transmission from the SMO (over O1), or by transmission from the E2 Nodes (over E2), or by combined transmission from the SMO and the E2 Nodes (or by communication between two Near-RT RIC functions). Moreover, depending how the relevant GoB Beam Pattern Information is defined, the necessary information can be even transmitted separately and asynchronously (e.g. SMO transmits a list of GoB Beam Patterns for the next, longer time period, while the E2 Nodes transmit the exact times of Beam Pattern change and indicate the ID of the Beam Pattern in the list). Figure 4.6.3.2-1: Massive MIMO Beam-based Mobility Robustness Optimization flow |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.6.4 Required data | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.6.4.1 Non-RT Massive MIMO GoB Beam Forming optimization | There are different types of data that are required from different parts of the network, and the following list summarizes with some examples: 1) Environment data: Cell site information (location), inter-site distance, BS system configuration, (e.g. operating frequency, bandwidth, frame structure, transmit power, default beam weight configuration); complete set of Massive-MIMO configurations, i.e. Horizontal beamwidth adjustable range, Vertical beamwidth adjustable range, Azimuth angle adjustable range, Elevation angle adjustable range. 2) From RAN to SMO and/or Near-RT RIC: a) Measurement reports with RSRP/RSRQ/CQI/SINR per beam information for the UEs in cells of interest; the time granularity of data collection shall be configurable and satisfy the requirement of the AI/ML model. b) Network KPIs: e.g. cell downlink/uplink traffic load, RRC connection attempts, average RRC connected UE, maximum RRC connected UE, average active connections (downlink/uplink), DL/UL throughput, DL/UL spectral efficiency, NI (Noise interference); beam resource usage (transmitted power per beam/directions and associated PRB usage), beam based handover and beam failure statistics. 3) From Application to SMO: a) user location related information, e.g. GPS coordinates for the purpose of constructing/training relevant AI/ML models. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.6.4.2 Near-RT Massive MIMO Beam-based Mobility Robustness Optimization | 1) Beam-specific handover related KPMs, as specified in 3GPP TS 28.552 [6], in 3GPP TS 28.622 [8] and in 3GPP TS 28.624 [9] from E2 Nodes, similar to: a) Too Early Handovers. b) Too Late Handovers. c) Attempted Handovers. d) Successful Handovers. e) Failed Handovers. f) The time granularity is an integer multiple of 1 second as specified in 3GPP TS 28.622 [8]. 2) The beam pattern information supplied externally is not supported in this version of the specification. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 42 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.7 Use case 7: RAN Sharing | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.7.0 Introduction | This use case provides the motivation, description, and requirements for RAN sharing use case. The goal of this use case is to enable multiple operators to share the same O-RAN infrastructure, while allowing them to remotely configure and control the shared resources via a remote O1, O2 and E2 interface. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.7.1 Background and goal of the use case | RAN sharing is envisioned as an efficient and sustainable way to reduce the network deployment costs, while increasing network capacity and coverage. Among the different RAN sharing models that have been experimented so far, a special focus is put here on the evaluation of the compatibility of the "Geographical Split" RAN sharing model with the O-RAN architecture. In such a model, a coverage area is split between two or more operators; each operator manages the RAN in a specific area, while sharing its RAN infrastructure and computing resources with its partner operators. Specifically, this use case analyses the Multi Operator RAN (MORAN) sharing scenario, wherein each operator utilizes a separate carrier in order to achieve more freedom and independency on the control of the radio resources. Accordingly, the goal of this use case is to propose a sharing-compliant O-RAN architecture that lets operators to configure the shared network resources independently from configuration and operating strategies of the other sharing operators. Specifically, it is proposed that a Home Operator (Operator A) makes available its O-RAN infrastructure and computing resources to host the virtual RAN functions (VNF) of a second operator (Operator B), allowing it to configure and control such remote VNFs via a remote O1, O2 and E2 interface. Figure 4.7.1-1: MORAN Use-Case in O-RAN The logic architecture of the proposed MORAN use case is shown in figure 4.7.1-1. It is assumed that Operator A owns the site A and shares the PHY Layer (LOW) with Operator B (Shared O-RU). Indeed, multiple PLMN IDs are broadcasted, while each operator operates in a different carrier. Moreover, the computing resources of the site A are shared among multiple VNFs, belonging to Operator A and Operator B, respectively. Each VNF represents a logic implementation of the O-DU and O-CU functionalities and is controlled by each partner operator in an independent manner. While Operator A can directly orchestrate and configure its VNFs, Operator B needs to control its VNFs in a remoted manner. The challenge here is to enable Operator B to configure and control resources in an infrastructure that is owned by another operator. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 43 Accordingly, it is assumed that Operator B can monitor and control the remote radio resources via the RIC node of site B, using an "E2 remote" interface. Note that in the proposed architecture, the RIC nodes are not shared and kept independent at the site A and B respectively. However, it is assumed that Operator B cannot directly orchestrate its VNFs in site A, but it is allowed to communicate the desired initial VNF configuration via an extended O1, O2 interface, hereafter referred to as "O1, O2 + SLA" interface (O1, O2 remote). Note that the O1, O2 nomenclature is used hereafter to refer to both O1 and O2 messages. The "O1, O2 remote" interface is connected to a specific "sharing orchestration application", referred to as "SMO- Sharing APP", that is located at the Service Management & Orchestration Framework of each operator. Specifically the "SMO-Sharing APP" at site A acts as an SLA (Service Level Agreement) monitoring and filter entity: it checks that O1, O2 requests coming from Operator B are in line with a predefined SLA and finally configures the VNF of Operator B, according to the initial O1, O2 request. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.7.2 Entities/resources involved in the use case | 1) SMO-Sharing APP (site A): a) SLA Monitoring: checks that orchestration/management requests sent by Operator B are in line with the SLA. b) Remote provisioning and initial VNF deployment: asks the IMF to instantiate the VNFs for Operator B. c) Remote management operations via "O1, O2 remote": configures the VNF of Operator B via the Orchestrator, according to "O1, O2 remote" requests sent by Operator B. d) Forwards RAN related data, collected from the hosted VNFs, to the SMO-Sharing APP (site B) over the "O1, O2 remote" interface. 2) SMO-Sharing APP (site B): a) Detects the "SMO-Sharing-APP" in site A towards which to forward "O1, O2 remote" requests. b) Sends "O1, O2 remote" commands for initial deployment and configuration of remote VNFs. c) Forwards RAN related data of Operator B, collected in site A, to the Non-RT RIC. 3) IMF (Site A): Creates VNFs for Operator B in site A on initial request of the SMO-Sharing APP (site A). 4) RAN (site A): a) Supports data collection from the hosted VNFs with radio state report over "E2 remote" interface. b) Supports data collection from hosted VNFs with UE KPI report over "O1, O2 remote" interface. 5) Non-RT RIC (site B): a) Configures the initial network policy template, e.g. default scheduling policy, of the remote VNFs. b) Elaborates RAN data collected by "SMO-Sharing APP", e.g. scheduling performance metrics, and sends A1 policy/intentions to the remote virtual O-DU/O-CU (VNF_B) via the Near-RT RIC. 6) Near-RT RIC (site B): a) Monitors and collects E2-related parameters from the remote VNFs. b) Detects the "E2 remote" interface towards the VNFs hosted in site A. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.7.3 Solutions | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.7.3.1 RAN sharing | The context of the RAN Sharing Use Case is captured in table 4.7.3.1-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 44 Table 4.7.3.1-1: RAN Sharing Use Case Use Case Stage Evolution / Specification <<Uses>> Related use Goal Enable two operators to share the same O-RAN infrastructure, while allowing them to remotely configure and control the shared resources via a remote "O1", "O2" and "E2" interface. Actors and Roles Sharing-SMO APP handles remote orchestration operations via "O1, O2 remote" interface. Non-RT RIC (Operator B): updates configuration of VNFs hosted in site A. Near-RT RIC (Op. B): execute remote E2 commands via "E2 remote" interface. RAN (Site A): collects and reports RAN statistics to the RIC of Operator B (RIC_B) for its VNFs hosted in site A. Assumptions All relevant functions and components are instantiated. A1, O1, O2, E2 interface connectivity is established with local SMO, Non-RT RIC and Near-RT RIC, respectively. "O1, O2 remote" and "E2 remote" end-to-end connectivity is established with remote SMO and remote Near-RT RIC, respectively. The remote interfaces have been secured through appropriate end-to-end security mechanisms (security configuration details are out of scope of this use case). Non-RT RIC_B and Near-RT RIC_B are aware of the presence of O-DU_B and O-CU_B in the site A. Near-RT RIC_B is aware of the "E2 remote" interface, to be used to control the remote VNFs hosted in site A. Pre conditions An SLA sharing agreement is established between the home (Operator A) and host operator (Operator B). The SLA defines the amount of physical resources (CPU, memory, etc.), that can be allocated to the host operator and the type of admissible orchestration operations that can be remotely executed by the host operator. Such SLA is translated in appropriate SLA monitoring-check controls to be executed by the SMO-Sharing APP. Begins when Phase 1-2: Host Operator (Operator B) asks to provision and instantiate an O-DU_B and O-CU_B in the site of the Home Operator (Operator A). Phase 3: Host Operator wants to send a new instruction to the shared RAN over the "E2 remote" interface. Step 1 (M) SMO-Sharing APP_B sends a request to SMO-sharing-APP_A for provisioning and deploying a remote virtual O-DU_B and O-CU_B in the site A. Step 2 (M) SMO-Sharing APP_A checks that the request is in line with the predefined SLA and ask the IMF (via the Orchestrator) to instantiate the VNFs for the O-CU_B and O-DU_B. Step 3 (O) IMF creates VNF for Operator B in site A as for the request of the SMO-Sharing APP_A. Step 4 (M) SMO-Sharing APP_B notifies SMO-Sharing APP_A the request to install a default network policy template, e.g. RB scheduling policy, in the remote VNFs. Step 5 (M) SMO-Sharing APP_A checks that Operator B request is in line with the SLA and configures (via the Orchestrator) the O-DU_B/ O-CU_B via an O1 configuration command. Step 6 (M) RAN related data from VNF_B in site A are collected at SMO Collector and forwarded to the SMO-Sharing APP_A, which in turns forwards them to the Non-RT RIC_B, via the SMO-Sharing APP_B. Step 7 Non-RT RIC_B decides to update the default network policy of the remote VNFs, e.g. scheduling policy of O-DU_B/O-CU_B and sends an A1 update policy request to the Near-RT RIC_B. Step 8 Near-RT RIC_B configures the remote O-DU_B/O-CU_B accordingly, over the "E2 remote" interface. Ends when The VNFs of Operator B in site A are instantiated with success and no update-requests are sent by the Host Operator (Operator B). Exceptions None identified. Post Conditions RIC of Operator B monitors relevant radio KPI from the remote O-CU_B and O-DU_B and decides to reconfigure the scheduling policy as for Step 7. The flow diagram of the VNF configuration procedure for VNF_B hosted in site A is given in figure 4.7.3.1-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 45 Figure 4.7.3.1-1: VNF configuration procedure for VNF_B hosted in site A |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.7.4 Required data | Multi-dimensional data are expected to be handled by the SMO-Sharing APP: 1) SLA data needs to be converted in a set of condition steps to be matched for each request of the Host Operator (Operator B). 2) SMO needs to handle O1, O2 messages sent by the Host Operator, converting them in local O1, O2 commands. The RAN of the home operator needs to report to the RIC_B the network state of the served UEs that belong to the host operator. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 46 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.8 Use case 8: QoS Based Resource Optimization | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.8.0 Introduction | This use case provides the background and motivation for the O-RAN architecture to support RAN QoS based resource optimization. Moreover, some high-level description and requirements over Non-RT RIC and A1 interfaces are introduced. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.8.1 Background and goal of the use case | QoS based resource optimization can be used when the network has been configured to provide some kind of preferential QoS for certain users. One such scenario can be related to when the network has been configured to support e2e slices. In this case, the network has functionality that ensures resource isolation between slices as well as functionality to monitor that slice Service Level Specifications (SLS) are fulfilled. In RAN, it is the scheduler that ensures that Physical Resource Block (PRB) resources are isolated between slices in the best possible way and also that the PRB resources are used in an optimal way to best fulfill the SLS for different slices. The desired default RAN behavior for slices is configured over O1. For example, the ratio of physical resources (PRBs) reserved for a slice is configured at slice creation (instantiation) over O1. Also, QoS can be configured to guide the RAN scheduler how to (in real-time) allocate PRB resources to different users to best fulfill the SLS of a specific slice. In the NR NRM this is described by the resource partition attribute. Instantiation of a RAN sub-slice will be prepared by rigorous planning to understand to what extent deployed RAN resources will be able to support RAN sub-slice SLS. Part of this procedure is to configure RAN functionality according to above. With this, a default behavior of RAN is obtained that will be able to fulfill slice SLSs for most situations. However, even through rigorous planning, there will be times and places where the RAN resources are not enough to fulfill SLS given the default configuration. To understand how often (and where) this happens, the performance of a RAN slice will continuously be monitored by SMO. When SMO detects a situation when RAN SLS cannot be fulfilled, Non-RT RIC can use A1 policies to improve the situation. To understand how to utilize A1 policies and how to resolve the situation, the Non-RT RIC will use additional information available in SMO. Take an emergency service as an example of a slice tenant. For this example, it is understood (at slice instantiation) that 50 % of the PRBs in an area can be enough to support the emergency traffic under normal circumstances. Therefore, the ratio of PRBs for the emergency users is configured to 50 % as default behavior for the pre-defined group of users belonging to the emergency slice. Also, QoS is also configured in CN and RAN so that video cameras of emergency users get a minimum bitrate of 500 kbps. Now, suppose a large fire is ongoing and emergency users are on duty. Some of the personnel capture the fire on video on site. The video streams are available to the Emergency Control Command. Because of the high traffic demand in the area from several emergency users (belonging to the same slice), the resources available for the Emergency slice is not enough to support all the traffic. In this situation, the operator has several possibilities to mitigate the situation. Depending on SLAs towards the Emergency slice compared to SLAs for other slices, the operator could reconfigure the amount of PRB reserved to Emergency slice at the expense of other slices. However, there is always a risk that Emergency video quality is not good enough irrespective if all resources are used for Emergency users. It might be that no video shows sufficient resolution due to resource limitations around the emergency site. In this situation, the Emergency Control Command decides, based on the video content, to focus on a selected video stream to improve the resolution. The Emergency Control System gives the information about which users to up- and down-prioritized to the e2e slice assurance function (through e.g. an Edge API) of the mobile network to increase bandwidth for selected video stream(s). Given this additional information, the Non-RT RIC can influence how RAN resources are allocated to different users through a QoS target statement in an A1 policy. By good usage of the A1 policy, the Emergency Control Command can ensure that dynamically defined group of UEs provides the video resolution that is needed. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.8.2 Entities/resources involved in the use case | 1) Non-RT RIC: a) Monitor necessary QoS related metrics from network function and other SMO functions. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 47 b) Send policies to Near-RT RIC to drive QoS based resource optimization at RAN level in terms of expected behavior. 2) Near-RT RIC: a) Support interpretation and execution of A1 policies for QoS based resource optimization. 3) RAN: a) Support network state and UE performance report with required granularity to SMO over O1 interface. b) Support QoS enforcement based on messages from E2, which are expected to influence RRM behavior. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.8.3 Solutions | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.8.3.1 QoS based resource optimization | The context of the QoS based resource optimization is captured in table 4.8.3.1-1. Table 4.8.3.1-1: QoS based resource optimization Use Case Stage Evolution / Specification <<Uses>> Related use Goal Drive QoS based resource optimization in RAN in accordance with defined policies and configuration. Actors and Roles Non-RT RIC: Creates A1 policies. Near-RT RIC: Enforces A1 policies. RAN: policy enforcement. SMO: termination point for O1 interface. Assumptions All relevant functions and components are instantiated and configured according wanted default behavior. A1 interface connectivity is established with Non-RT RIC. O1 interface connectivity is established with SMO. The default configuration will handle most situations. Pre conditions Network is operational with default configuration. SMO has established the data collection and sharing process, and Non-RT RIC has access to this data. Non-RT RIC analyses the data from RAN to understand the current resource consumption. Begins when Non-RT RIC observes that resources are close to congestion in a certain area. Step 1 (O) If needed, Non-RT RIC orders additional RAN observability, SMO configures additional observability over O1. Step 2 Non-RT RIC evaluates RAN resource utilization for all users in a slice in specific area. Step 3 Non-RT RIC asks for additional information from additional SMO functionality, e.g. e2e slice assurance function. Step 4 Non-RT RIC determines dynamic group of users for which QoS target shall be changed. Step 5 Non-RT issues A1 policy/policies with QoS target based on information from other SMO functionality. Ends when Non-RT RIC (through O1 observability) understands that situation of resource constraints within the slice is resolved and the deployed policies are deleted over A1. Exceptions None identified. Post Conditions The flow diagram of the QoS based resource optimization is given in figure 4.8.3.1-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 48 Figure 4.8.3.1-1: Flow diagram, QoS based resource optimization |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.8.4 Required data | For this use case, different kind of observability need to be reported to Non-RT RIC. First Non-RT RIC shall monitor resource consumption in the area. As long as resource consumption is low, the RAN scheduler will be able to give all users in an area the needed resources. When resource consumption in an area increases above a threshold, the risk of that the default configuration of RAN will not be enough to fulfil the requirements. At this point, the Non-RT RIC need to be able to configure more detailed reporting for individual UEs that the Non-RT RIC is interested in. This detailed observability shall provide the Non-RT RIC better insight in performance for specific users and therefore includes observability of e.g. user throughput and delay. With this more detailed observability, the Non-RT RIC can understand when pre-configured priorities are not enough for the scheduler to solve the problem and when additional (Non RAN) information to solve the prioritization is needed. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.9 Use case 9: RAN Slice SLA Assurance | The 3GPP standards architected a sliceable 5G infrastructure which allows creation and management of customized networks to meet specific service requirements that can be demanded by future applications, services and business verticals. Such a flexible architecture needs different requirements to be specified in terms of functionality, performance and group of users which can greatly vary from one service to the other. The 5G standardization efforts have gone into defining specific slices and their Service Level Agreements (SLAs) based on application/service type as specified in 3GPP TS 23.501 [2]. Since network slicing is conceived to be an end-to-end feature that includes the core network, the transport network and the Radio Access Network (RAN), these requirements shall be met at any slice subnet during the life-time of a network slice as specified in 3GPP TS 28.530 [4], especially in RAN side. Exemplary slice performance requirements are specified in terms of throughput, energy efficiency, latency and reliability at a high level in SDOs such as 3GPP TS 22.261 [1] and GSMA NG.116 [17]. These requirements are defined as a reference for SLA/contractual agreements for each slice, which individually need proper handling in NG-RAN. Although network slicing support is started to be defined with 3GPP Release 15, slice assurance mechanisms in RAN needs to be further addressed to achieve deployable network slicing in an open RAN environment. It is necessary to assure the SLAs by dynamically controlling slice configurations based on slice specific performance information. Existing RAN performance measurements as specified in 3GPP TS 28.552 [6] and information model definitions as specified in 3GPP TS 28.541 [5] are not enough to support RAN slice SLA assurance use cases. This use case is intended to clarify necessary mechanisms and parameters for RAN slice SLA assurance. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 49 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.9.1 Background and goal of the use case | In the 5G era, network slicing is a prominent feature which provides end-to-end connectivity and data processing tailored to specific business requirements. These requirements include customizable network capabilities such as the support of very high data rates, traffic densities, service availability and very low latency. According to 5G standardization efforts, the 5G system can support the needs of the business through the specification of several service needs such as data rate, traffic capacity, user density, latency, reliability, and availability. These capabilities are always provided based on a Service Level Agreement (SLA) between the mobile operator and the business customer, which brought up interest for mechanisms to ensure slice SLAs and prevent its possible violations. O-RAN's open interfaces and AI/ML based architecture will enable such challenging mechanisms to be implemented and help pave the way for operators to realize the opportunities of network slicing in an efficient manner. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.9.2 Entities/resources involved in the use case | 1) Non-RT RIC: a) Retrieve RAN slice SLA target from respective entities such as SMO, NSSMF. b) Long term monitoring of RAN slice performance measurements. c) Training of potential ML models that will be deployed in Non-RT RIC for slow loop optimization and/or Near-RT RIC for fast loop optimization. d) Support deployment and update of AI/ML models into Near-RT RIC. e) Send slice control/slice SLA assurance xApps from SMO. f) Create A1 policies based on RAN intent A1 feedback. g) Retrieve UE specific performance reports. h) Send A1 policies and enrichment information to Near-RT RIC to drive slice assurance. i) Send O1 reconfiguration requests to SMO for slow-loop slice assurance. 2) Near-RT RIC: a) Near real-time monitoring of slice specific RAN performance measurements. b) Support deployment and execution of the AI/ML models from Non-RT RIC. c) Receive slice SLA assurance xApps from SMO. d) Send UE specific performance reports to SMO/Non-RT RIC. e) Support interpretation and execution of policies from Non-RT RIC. f) Perform optimized RAN (E2) actions to achieve RAN slice requirements based on O1 configuration, A1 policy, and E2 reports. 3) RAN: a) Support slice assurance actions such as slice-aware resource allocation, prioritization, etc. b) Support slice specific performance measurements through O1. c) Support slice specific performance reports through E2. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 50 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.9.3 Solutions | 4.9.3.1 Creation and deployment of RAN slice SLA assurance models and control apps The context of the creation and deployment of RAN slice SLA assurance models and control apps is captured in table 4.9.3.1-1. Table 4.9.3.1-1: Creation and deployment of RAN slice SLA assurance models and control apps Use Case Stage Evolution / Specification <<Uses>> Related use Goal Training and distribution of the model, or distribution of control apps. Actors and Roles Non-RT RIC, Near-RT RIC, SMO. Assumptions All relevant functions and components are instantiated. A1, O1 interface connectivity is established. Pre conditions Near-RT RIC and Non-RT RIC are instantiated with A1 interface and connectivity has been established between them. O1 interface has been established between SMO and Near-RT RIC. Begins when A RAN slice is activated. Step 1 (M) Non-RT RIC retrieves a RAN slice SLA from SMO (NSSMF). Step 2a Non-RT RIC starts to collect performance measurements (PMs) via O1. Examples of the PMs are CSI, PRB usage, L2 throughput, RAN latency, etc. Applicable PMs are specified in 3GPP TS 28.552 [6]. Step 2 and 3 are mandatory in case of using the AI/ML model Step 2b (O) Non-RT RIC starts to collect enrichment information (EIs) from external applications. Examples of the external applications are public safety application triggering slice priority during an emergency event, or location-based enrichment information, etc. Step 2c Non-RT RIC analyses collected PMs and/or EIs for long term monitoring, such as during the day or over the weekend. Step 3 Non-RT RIC does the model training using the collected data in step 2 and obtains RAN slice SLA assurance models. Step 4a In case of using the AI/ML model, Non-RT RIC deploys the trained model internally for slow loop optimization and/or distributes it to the Near-RT RIC via O2 for fast loop optimization. Step 4a or 4b is Mandatory Step 4b In case of using the control app, the control app is deployed by SMO to Non-RT RIC for slow loop optimization and/or Near-RT RIC via O2 for fast loop optimization. Step 5 (M) Non-RT RIC receives feedback internally or from Near-RT RIC via A1 to update the model or control apps based on it. Ends when A RAN slice is deactivated. Exceptions None identified. Post Conditions None identified. The flow diagram of the creation and deployment of RAN slice SLA assurance models and control apps is given in figure 4.9.3.1-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 51 Figure 4.9.3.1-1: Flow diagram, Creation and deployment of RAN slice SLA assurance models and control apps |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.9.3.2 Slow loop RAN Slice SLA optimization | The context of the slow loop RAN Slice SLA optimization is captured in table 4.9.3.2-1. Table 4.9.3.2-1: Slow loop RAN Slice SLA optimization Use Case Stage Evolution / Specification <<Uses>> Related use Goal Slow loop RAN Slice SLA optimization. Actors and Roles Non-RT RIC, Near-RT RIC, SMO, RAN. Assumptions All relevant functions and components are instantiated. A1, O1, E2 interface connectivity is established. Pre conditions Near-RT RIC and Non-RT RIC are instantiated with A1 interface connectivity being established between them. O1 interfaces are established between SMO and Near-RT RIC, and SMO and RAN nodes. RAN slice SLA assurance models or control apps have been deployed in Non-RT RIC and Near-RT RIC respectively. Begins when A RAN slice is activated. Step 1a Non-RT RIC decides that RAN shall be reconfigured based on long term trends collected via O1 using PMs and/or EIs. Examples of the PMs are layer 2 throughput, PRB usage, CSI, RAN latency. Config update Step 1a or 1b is mandatory ETSI ETSI TS 104 036 V12.0.0 (2025-04) 52 Use Case Stage Evolution / Specification <<Uses>> Related use Step 1b Non-RT RIC decides to create slice specific A1 policies based on RAN slice SLA requirements and/or operator-defined RAN intents, A1 feedback from Near-RT RIC, EI from external app server and O1 based long term trends. The policies include scope identifiers (e.g. S-NSSAI, Flow ID, Cell ID) and/or policy statements (e.g. slice specific KPI targets). Policy update Step 2a The model or control app in Non-RT RIC requests SMO to update slice configuration of Near-RT RIC and/or RAN nodes through O1. Config request Step 2b SMO sends the updated slice configuration to Near-RT RIC and/or RAN nodes via O1. Examples of the slice configuration are the number of allocated PRBs, number of flows, slice priorities. Config delivery Step 2b or 2c is mandatory Step 2c Non-RT RIC sends the updated A1 policies to Near-RT RIC. Policy delivery Step 3a Near-RT RIC and RAN nodes process and execute the updated slice configuration. Config execution Step 3a or 3b is mandatory Step 3b Near-RT RIC receives the updated A1 policy, controls RAN nodes based on the A1 policy and sends the feedback to Non-RT RIC via A1. Policy execution Ends when A RAN slice is deactivated. Exceptions None identified. Post Conditions None identified. The flow diagram of the slow loop RAN Slice SLA optimization is given in figure 4.9.3.2-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 53 Figure 4.9.3.2-1: Flow diagram, Slow loop RAN Slice SLA optimization |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.9.3.3 Fast loop RAN Slice SLA optimization | The context of the fast loop RAN Slice SLA optimization is captured in table 4.9.3.3-1. Table 4.9.3.3-1: Fast loop RAN Slice SLA optimization Use Case Stage Evolution / Specification <<Uses>> Related use Goal Fast loop RAN Slice SLA optimization. Actors and Roles Non-RT RIC, Near-RT RIC, SMO, RAN. Assumptions All relevant functions and components are instantiated. A1, O1, E2 interface connectivity is established. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 54 Use Case Stage Evolution / Specification <<Uses>> Related use Pre conditions Near-RT RIC and Non-RT RIC are instantiated with A1 interface connectivity being established between them. O1 interfaces are established between SMO and Near-RT RIC, and SMO and RAN nodes. RAN slice SLA assurance models or control apps have been deployed in Near-RT RIC. Begins when A RAN slice is activated Step 1 Non-RT RIC decides to generate a policy for Near-RT RIC slice SLA assurance based on RAN slice SLA requirements and/or operator-defined RAN intents, A1 feedback from Near-RT RIC, EI from external app server and O1 based long term trends. Step 2 Near-RT RIC receives slice specific O1 configuration and A1 policies from SMO and Non-RT RIC respectively. The former is static and default, the latter is dynamic, optimized and converted from slice SLA. The policies consist of scope identifiers (e.g. S-NSSAI, Flow ID, Cell ID) and policy statements (e.g. slice specific KPI targets). In case of using EIs, Near-RT RIC also receives the EIs from Non-RT RIC via A1-EI interface. Step 3 Near-RT RIC starts to collect PMs via E2. Examples of the PMs are CSI, PRB usage, L2 throughput, RAN latency, etc. Applicable PMs are specified in 3GPP TS 28.552 [6]. Step 4 The model or control app in Near-RT RIC analyses collected PMs, A1 policies from Non-RT RIC (and optionally EIs from A1-EI interface) to guide RAN nodes via E2 to meet the slice SLA. Step 5 Near-RT RIC sends A1 feedback to Non-RT RIC. Ends when A RAN slice is deactivated. Exceptions None identified. Post Conditions None identified. The flow diagram of the fast loop RAN Slice SLA optimization is given in figure 4.9.3.3-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 55 Figure 4.9.3.3-1: Flow diagram, Fast loop RAN Slice SLA optimization |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.9.4 Required data | The measurement counters and KPIs (as defined by 3GPP and will be extended for O-RAN use cases) shall be appropriately aggregated by cell, QoS type, slice, etc. 1) Per-UE performance statistics such as CSI, RSRP/CQI distribution. 2) Per slice performance statistics such as PDCP throughput, PRB usage. 3) Per UE performance reports such as L2 throughput, RAN latency. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.10 Use case 10: Multi-vendor Slices | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.10.0 Introduction | This use case "Multi-vendor slices" is a case that vO-DU and vO-CU functions composing each slice is provided from different vendor. In this sub clause, concept, motivation and benefits of introducing "Multi-vendor slices" are explained and candidate solutions are studied. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 56 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.10.1 Background and goal of the use case | Proposed use case enables multiple slices with functions provided from multi- vendors, such as slice #1 is composed with DU and CU provided from vendor A and slice #2 is composed with DU and CU provided from vendor B (see figure 4.10.1-1). Figure 4.10.1-1: Multi-vendor Slices To support Multi-vendor slicing, there are many possible configurations to realize this use case; all of which share that one O-RU is connected to one or more O-DUs. For example, one possible configuration might be one where a single cell is shared by two O-DUs, and another possible configuration is where two cells are allocated to two different O-DUs in a Shared O-RU configuration. Under those possible configurations, it is desired to keep frequency efficiency. When providing multiple slices, it is assumed that suitable vO-DU/scheduler and vO-CU treat each slice respectively. A vendor who providing vO-DU and vO-CU function can have a strength of a customized scheduler for a certain service. With accomplishment of multi-vendor circumstances, following benefits can be expected. 1) More flexible and time to market deployment: Operator can maximize options to choose suitable vO-DU/scheduler and vO-CU to offer various slice. For example, some vendor has a strength of a scheduler for eMBB service and the other has a strength of scheduler for URLLC service. Or, vendor A can provide vO-DU/scheduler and vO-CU suitable for URLLC earlier than vendor B, therefore operator can choose vO-DU and vO-CU functions from vendor A to meet their service requirement. Also, when operator will add a new service/slice, new functions from a new vendor can be introduced with less consideration for existing vendor if multi-vendor circumstance was realized. This can lead to expand vendor's business opportunities rapidly. 2) Flexible deployment when sharing RAN equipment among operators: When operators want to share RAN equipment and resources, RAN vendors and their placement of each RAN functions can be different. If multi-vendor circumstance was introduced, then it can relax restrictions among operators to share RAN equipment and resources. This can lead to expand opportunity reaching agreement of RAN sharing among operators. With expansion of RAN sharing, CAPEX and OPEX by operator will be optimized and additional investment can be done more. 3) Reducing supply chain risk: If existing vendor providing a certain pair of vO-DU and vO-CU functions would withdraw of their market due to business reason, operator can deploy new vO-DU and vO-CU functions alternatively from other vendor under this multi-vendor circumstance. This can reduce a risk for operators' business continuity. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.10.2 Entities/resources involved in the use case | 1) SMO Multi-vendor Slice App: a) Configures vO-DU and vO-CU. b) Configures O-RU to connect to vO-DU. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 57 2) Near-RT RIC: a) Shares MAC related data unique for UE among vO-DUs. b) Support communication of configuration parameters to RAN. 3) E2 Nodes (vO-CU, vO-DU, O-RU): a) Primary vO-DU processes SRB (Signalling Radio Bearer), DRB (Data Radio Bearer) and other vO-DU related functions. Secondary vO-DU processes only DRB related functions. Note that vO-DU and vO- CU are created as part of network slice creation procedure. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.10.3 Solutions | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.10.3.1 Data transmission call flow example for Multi-vendor slices use case | The context of data transmission call flow example for Multi-vendor slices use case is captured in table 4.10.3.1-1. Table 4.10.3.1-1: Data transmission call flow example for Multi-vendor slices use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal UE communicates on slice #1 and #2 respectively Actors and Roles - SMO Multi-vendor Slice App configures vO-DU and v-O-CU with radio resource assignment (via Orchestrator) and collects KPI data. - Near-RT RIC configures vO-DU and vO-CU for resource assignment and shares MAC related information unique for UE among vO-DUs. - Primary vO-DU processes SRB (Signalling Radio Bearer), DRB (Data Radio Bearer). Secondary vO-DU processes only DRB. Assumptions - All relevant functions and components are instantiated. - Slice #1 is created over primary vO-DU and vO-CU with Logical Channel ID #1 and #2, and slice #2 is created over secondary vO-DU and vO-CU with Logical Channel ID #3. - O-RU is shared between primary vO-DU and secondary vO-DU with one component carrier. - CU-CP is shared between primary vO-CU-UP and secondary vO-CU-UP. - TDD operation is assumed. - UE tries to transmit data on slice #1 and #2. Pre conditions - Slice #1 and #2 are created and activated on primary vO-DU, vO-CU and secondary vO-DU, vO-CU respectively. - Slice #1 is tied with Scheduling Request Resource 1 and Logical Channel ID #1 and #2, and slice #2 is tied with Scheduling Request Resource 2 and Logical Channel ID #3. - Primary vO-DU and secondary vO-DU know which timing/resource block they can utilize on for slice #1 and #2 respectively by direction from SMO via O1 interface. - UE has already performed RACH procedure with primary vO-DU. Begins when UE tries to perform registration procedure with RRC Connection Request message. Step 1 (M) [UE performs registration procedure] UE sends RRC Connection Request message to primary vO-DU and vO-CU through O-RU. Primary vO-CU and vO-DU responds with RRC Connection Setup. UE sends RRC connection Setup Complete message. Primary vO-DU sends initial RRC message and shared information such as C-RNTI to near RT-RIC. Near RT-RIC determines to transfer it to secondary vO-DU over E2 interface. Other registration procedure is performed. Step 2 (M) [PDU session establishment] UE starts PDU session establishment procedure with PDU session establishment request message with primary vO-DU and vO-CU. UE initiates PDU session establishment procedure with S-NSSAI 2 for Slice #2 via primary vO-DU. UE Context Modification is made at secondary vO-DU. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 58 Use Case Stage Evolution / Specification <<Uses>> Related use Step 3 (M) [U-plane data transmission between primary vO-DU and O-RU] At allocated timing/resources, primary vO-DU sends Scheduling Command message to O-RU to start transfer and receive DL and UL Data. UE sends Scheduling Request message on PUCCH with Scheduling Request Resource 1 to primary vO-DU over Open fronthaul. Primary vO-DU responds with UL Grant message to the UE. O- RAN.WG4.CUS.0 -v02.00 [24], "Figure 6‑5: C- Plane and U- Plane message transfer procedure (DL & UL shown)" Step 4 (M) [Buffer notification and transmission user data] UE notices buffer with Buffer Status Request message to primary vO-DU. Primary vO-DU acknowledges with UL Grant message. UE sends user data on PUSCH with Logical Channel ID #1 and #2. Primary vO-DU acknowledges with Ack or Nack. UE repeats step 3 until buffer becomes empty. Step 5 (M) [U-plane data transmission between secondary vO-DU and O-RU] At allocated timing/resources, secondary vO-DU sends Scheduling Command message to O-RU to start transfer and receive DL and UL Data. UE sends Scheduling Request message on PUCCH with Scheduling Request Resource 2 to secondary vO-DU over Open fronthaul. Secondary vO-DU responds with UL Grant message to the UE. O- RAN.WG4.CUS.0 -v02.00 [24], "Figure 6‑5: C- Plane and U- Plane message transfer procedure (DL & UL shown)" Step 6 (M) [Buffer notification and transmission user data] UE notices buffer with Buffer Status Request message to secondary vO-DU. Secondary vO-DU acknowledges with UL Grant message. UE sends user data on PUSCH with Logical Channel ID #3. Secondary vO-DU acknowledges with Ack or Nack. UE repeats step 5 until buffer becomes empty. Step 7 (M) [Collect Data] RAN related data from RAN nodes are collected at SMO Collector via O1 interface. Ends when UE finishes data transmission until buffer becomes empty. Exceptions None identified. Post Conditions None identified. The data transmission call flow example for Multi-vendor slices use case - Part 1 of 2 is given in figure 4.10.3.1-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 59 Figure 4.10.3.1-1: Data transmission call flow example for Multi-vendor slices use case - Part 1 of 2 The data transmission call flow example for Multi-vendor slices use case - Part 2 of 2 is given in figure 4.10.3.1-2. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 60 Figure 4.10.3.1-2: Data transmission call flow example for Multi-vendor slices use case - Part 2 of 2 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.10.3.2 Data transmission call flow example for RAN sharing use case | The context of data transmission call flow example for RAN sharing use case is captured in table 4.10.3.2-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 61 Table 4.10.3.2-1: Data transmission call flow example for RAN sharing use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal UE communicates on secondary vO-DU and vO-CU with PLMN #2. Actors and Roles - SMO Multi-vendor Slice App configures vO-DU and v-O-CU with radio resource assignment (via Orchestrator) and collects KPI data. - Near-RT RIC configures vO-DU and vO-CU for resource assignment and shares MAC related information unique for UE among vO-DUs. - Primary vO-DU processes SRB (Signalling Radio Bearer), DRB (Data Radio Bearer). Secondary vO-DU processes only DRB. Assumptions - All relevant functions and components are instantiated. - PLMN #1 is assigned to primary vO-DU and vO-CU, and PLMN #2 is assigned to secondary vO-DU and vO-CU respectively. - O-RU is shared between primary vO-DU and secondary vO-DU with one component carrier. - CU-CP is shared between primary vO-CU-UP and secondary vO-CU-UP. - TDD operation is assumed. - UE tries to transmit data with PLMN #2. Pre conditions - PLMN #1 and #2 are assigned to primary vO-DU, vO-CU and secondary vO-DU, vO-CU respectively. - Primary vO-DU and vO-CU advertise PLMN #1 and #2 over the air. - Primary vO-DU and secondary vO-DU know which timing/resource block they can utilize on for PLMN #1 and #2 respectively by direction from SMO via O1 interface. - UE has already performed RACH procedure with primary vO-DU. Begins when UE tries to perform registration procedure with RRC Connection Request message. Step 1 (M) [UE performs registration procedure with PLMN #2] UE sends RRC Connection Request message to primary vO-DU and vO-CU through O-RU. Primary vO-CU and vO-DU responds with RRC Connection Setup. UE sends RRC connection Setup Complete message with PLMN#2 in selected PLMN-Identity. Primary vO-DU sends initial RRC message with PLMN-Identity and shared information such as C-RNTI to near RT-RIC. Near RT-RIC determines to transfer it to secondary vO-DU over E2 interface. Other registration procedure is performed through secondary vO-DU. Step 2 (M) [PDU session establishment] UE starts PDU session establishment procedure with PDU session establishment request message through secondary vO-DU and vO-CU. Step 3 (M) [U-plane data transmission between secondary vO-DU and O-RU] At allocated timing/resources, secondary vO-DU sends Scheduling Command message to O-RU to start transfer and receive DL and UL Data. UE sends Scheduling Request message on PUCCH with Scheduling Request Resource 2 to secondary vO-DU over Open fronthaul. Secondary vO-DU responds with UL Grant message to the UE. O- RAN.WG4.CUS.0 -v02.00 [24], "Figure 6‑5 : C- Plane and U- Plane message transfer procedure (DL & UL shown)" Step 4 (M) [Buffer notification and transmission user data] UE notices buffer with Buffer Status Request message to secondary vO-DU. Secondary vO-DU acknowledges with UL Grant message. UE sends user data on PUSCH with Logical Channel ID #2. Secondary vO-DU acknowledges with Ack or Nack. UE repeats step 3 until buffer becomes empty. Step 5 (M) [Collect Data] RAN related data from RAN nodes are collected at SMO Collector via O1 interface. Ends when UE finishes data transmission until buffer becomes empty. Exceptions None identified. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 62 Use Case Stage Evolution / Specification <<Uses>> Related use Post Conditions None identified. The data transmission call flow example for RAN sharing use case is given in figure 4.10.3.2-1. Figure 4.10.3.2-1: Data transmission call flow example for RAN sharing use case |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.10.4 Required data | The measurement counters and KPIs (as defined by 3GPP and will be extended for O-RAN use cases) shall be appropriately aggregated by cell, QoS type, slice, etc. 1) Per-UE CSI. 2) Per slice performance statistics such as PDCP throughput, PRB usage. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.11 Use case 11: Dynamic Spectrum Sharing | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.11.0 Introduction | This use case provides the background, motivation, and requirements to realize Dynamic Spectrum Sharing (DSS) over the ORAN architecture. This is to enable operators to adapt radio resource allocation policies and control to dynamically share radio spectrum between 4G and 5G networks. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 63 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.11.1 Background and goal of the use case | As we transition from 4G to 5G, the spectral resources used for 5G deployment is a key consideration and this situation varies from one operator to another. Though, new C-band resources between 3-6 GHz and mmWave bands have been acquired by operators, these bands suffer from great propagation and penetration loss, limiting their coverage to those users close to the cell, this situation worsens in the UL as the UE device is power constrained. A cost-effective way to address this is the 5G deployment on lower bands (i.e. below 2 GHz), which are also used in 4G LTE deployments today. Operating on lower bands along with non-standalone mode of 5G deployment helps to cover large geography, enables seamless mobility between 4G and 5G while being sensitive to overall cost of deployment. In addition, DSS offers the advantage of dynamically sharing the available spectrum adapting to the varying workloads of the 4G and 5G network. DSS is compelling considering the need for operators to dynamically share already deployed spectral resources between LTE and NR devices without degrading the QoE of the current 4G subscribers while offering the same level of coverage and necessary QoS to NR devices, under the assumption that the two networks will co-exist in the near term. The objective of this use case is to propose DSS in the context of the ORAN architecture, specifically to realize it as an application in the RIC framework. This would particularly benefit vRAN implementations when the 4G/5G CU/DU are from different vendors and one could leverage RAN data over O-RAN's framework for traffic prediction, DSS related resource management and conduct control functions. Towards this, the intelligent control functions are identified, which can be realized as a DSS application to augment the L3/L2/L1 control functions specified as part of LTE-NR coexistence in 3GPP TS 23.501 [2] and in 3GPP TS 37.340 [12]. The architectural context set for this discussion is shown in figure 4.11.1-1. DSS enables 4G and 5G UEs to operate over the same spectrum identified as X (typically low band), while 5G itself could operate on new bands Y (typically high band) not used by current 4G deployment. In a typical setting, Y would offer higher capacity, low latency and smaller coverage, while X would be used to offer reasonable capacity along with larger coverage. 3GPP specifications offers DSS support over X2/Xn interface to enable dynamic sharing of the spectrum resource in addition to the L2/L1 adaptation for 5G-NR to co-exist with LTE. Considering the scenario of incremental deployment - in the 5G NSA mode, the 5G UE is required to have dual connectivity capability and be able to connect to eNBs on LTE bands for control plane requirements and user plane connectivity towards the LTE and/or 5G depending on deployment requirements. In the scenario where gNB only operates on 5G C or mmWave bands, the sharing of the LTE frequency band between 4G and 5G UEs can be solely fulfilled by eNB MAC scheduler, as the UE is expected to be dual stacked. While, if the gNB is required to operate on lower LTE bands as well, then spectral sharing needs to be coordinated between the LTE and 5G schedulers. When DSS is enabled in the SA mode, 5G UE would be capable of operating on lower LTE bands (below 2 GHz), C and mmWave bands and connects only to the gNBs. The sharing of the LTE bands between LTE and 5G data channels are achieved by both 4G scheduler and 5G scheduler using resource management and interference mitigation functions in the RIC between them. The use case proposes to conduct DSS related policy, configuration, resource management and control functions using the non-RT and near-RT functions over open interfaces proposed by ORAN. An abstracted view of how DSS application can be realized using the Non-Real Time and Near-Real Time RIC components is shown in figure 4.11.1-2. The DSS over RIC can be realized as multiple applications considering its multiple optimization and operational objectives. One possible logical breakdown is as a traffic prediction and resource management application (DSS-App) managing the shared spectrum resource adapting to dynamic 4G and 5G specific workload requirements in various local contexts, and another application (RAT-App) to configure, control and monitor DSS related functions in the CU/DU corresponding to the LTE and 5G cells. The DSS-App engineers at the Non-RT RIC level translates the global DSS policies based on workload requirements for a region and time-of-day to spectrum sharing policies such as max/min bandwidth threshold at a local level (e.g. edge or central office). The RAT-App at the Non-RT RIC level also translates the DSS-App's resource policies to RAT specific configuration and policies at the Near-RT RIC and the CU/DU entities. The DSS-App at the Near-RT RIC uses the data collected by the RAT-app to make dynamic resource sharing decisions that are enforced by the RAT-app using the E2 control APIs. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 64 Figure 4.11.1-1: RIC Based DSS Architecture Figure 4.11.1-2: RIC Based DSS Realization The main goal of the non-RT DSS-App is to provide long-term scheduling policy to 4G and 5G scheduler considering business, user, spatial and temporal workload factors. The main functionality of non-RT RAT-App is to translate the global DSS policies from non-RT DSS-App to RAT specific policies to the RAT-App in the Near-RT RIC over A1. The main functionalities of the near-RT DSS-App include policy translation between non-RT DSS-App to RAT specific configuration to the near-RT RAT-App. Furthermore, it is actively involved in closed loop decision using the KPIs from the RAN adapting to the needs of the 4G and 5G cells. The main functionality of near-RT RAT-App is to perform RAT specific configuration, control and data subscription over E2 interface with RAN (CU/DU components). |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.11.2 Entities/resources involved in the use case | 1) Non-RT RIC: a) Receive SMO's DSS specific service requirement for the RAN and translate them into resource sharing policies. b) Provide long-term policies in terms of scheduling guidance to 4G and 5G scheduler over A1 to Near-RT RIC, considering business, user, spatial and temporal workload factors, policies related to expected performance and actions when it deviates based on KPIs from the 4G and 5G network. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 65 c) Develop and train AI/ML models with the help of SMO functions for the Near-RT RIC to predict the short-term traffic demand for 4G and 5G network based on near-real-time metrics from RAN. Deployment of these ML model over O1 and xApps over O2 to the Near-RT RIC. d) Receive policy feedback from Near-RT RIC and update policy and re-train ML models whenever required. 2) Near-RT RIC: a) Support deployment, execution and ability to update DSS xApps from Non-RT RIC. b) Support interpretation of policies related to RAT specific resource allocation. c) Translate RAT specific SLA policy to configuration, control and data subscription over E2 interface to E2 Nodes (O-CU, O-DU). d) Share resource allocation performance and policy feedback report with Non-RT RIC for further evaluation and optimization over O1/A1. 3) RAN: a) Support discovery of DSS related configuration of E2 nodes over E2 interface. b) Share the data collection over O1 interface. c) Support resource management related metrics collection over E2 interface. d) Support control and policy enforcement from Near-RT RIC over E2 interface. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.11.3 Solutions | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.11.3.1 Dynamic Spectrum Sharing for 4G and 5G | The context of the Dynamic Spectrum Sharing for 4G and 5G is captured in table 4.11.3.1-1. Table 4.11.3.1-1: Dynamic Spectrum Sharing for 4G and 5G Use Case Stage Evolution / Specification <<Uses>> Related use Goal Enable operators to dynamically share spectrum in the existing 4G deployment with 5G systems, based on the dynamic loads of both networks and resource sharing policies. Actors and Roles Non-RT RIC: spectrum resource sharing policy function. Near-RT RIC: executes resource sharing models and algorithms, translating RAT specific policy to configuration, control and data subscription over E2 interface with RAN. RAN: executes resource sharing enforcement rules and policies, collects and reports RAN statistics and performance over E2 and O1. Assumptions All relevant functions and components are instantiated. DSS xApps are deployed over O1 with initial configuration. A1, E2 interface connectivity is established with Non-RT RIC and RAN respectively. Data report, policy and control subscription established on E2 interface. Pre conditions Network is operational. SMO has established the data collection and sharing interface with Non-RT RIC. Non-RT RIC analyses the historical data from RAN, develops, trains with help of SMO functions and deploys the relevant AI/ML models or algorithm as xApps to the Near-RT RIC. Begins when Operator specified trigger condition or event is detected. Step 1 (M) Near-RT RIC collects DSS related RAN function capabilities and configuration parameters from RAN over E2 interface. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 66 Use Case Stage Evolution / Specification <<Uses>> Related use Step 2 (M) Non-RT RIC communicates DSS relevant policies to the Near-RT RIC over the A1 interface. Step 3 (M) Near-RT RIC communicates RAT specific DSS relevant configuration, control policies to RAN over the E2 interface. Step 4 (M) RAN deploys the configuration and control policies received from the Near-RT RIC over the E2 interface. Step 5 (M) Near-RT RIC collects relevant observability data from RAN, executes xApp and outputs the optimal resource allocation and cell level resource scheduling decisions to RAN over E2 and policy feedback to Non-RT RIC over A1. Step 6 (M) RAN deploys the updated control policies received from the Near-RT RIC over the E2 interface and continues reporting data to SMO over O1 and E2 as configured. Step 7 (M) Non-RT RIC adjusts the policy based on PM data from SMO and feedback from Near-RT RIC. Step 8 (M) Non-RT RIC updates the resource sharing policy to Near-RT RIC over A1. Step 9 (O) Non-RT RIC re-trains/updates the AI/ML model with new data and performance, and deploys the new model or new model configurations to Near- RT RIC. Ends when Operator specified trigger condition or event is satisfied. Exceptions None identified. Post Conditions Non-RT RIC monitors loads and relevant KPI performance metrics of eNB/gNB to observe the resource sharing efficiency and sets up new policies based on the metrics and business needs. Near-RT RIC executes the resource sharing model or algorithm. RAN operates with the scheduling guidance from RIC and reports performance data to RIC. The flow diagram of the Dynamic Spectrum Sharing for 4G and 5G is given in figure 4.11.3.1-1. Figure 4.11.3.1-1: Flow diagram, Dynamic Spectrum Sharing for 4G and 5G ETSI ETSI TS 104 036 V12.0.0 (2025-04) 67 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.11.4 Required data | Multiple observability data from RAN need to be reported to SMO, Non-RT RIC and Near-RT RIC for DSS to operate. The required data for DSS use case is captured in table 4.11.4-1. Table 4.11.4-1: Required data for DSS use case Category Parameters / Measurements RAT Source / Interface Reference 4G/5G DSS configuration and operation parameters Geography location (e.g. cell site) 4G/5G External server DSS modality (static, semi-static (MBSFN), dynamic (sub-frame level)) 4G/5G E2 3GPP TS 38.211 [13] 3GPP TS 38.213 [14] Cell configuration information (e.g. FDD/TDD, Band, Signaling/RS Allocation Bitmap) 4G/5G E2 3GPP TS 36.423 [25] 3GPP TS 38.211 [13] 3GPP TS 38.213 [14] 4G/5G scheduling information Physical resource block used/reserved/requested/blocked Bitmap information 4G/5G E2 3GPP TS 36.423 [25] 4G/5G cell load statistics Number of active UEs (total, UL/DL, per QCI) 4G E2 and O1 3GPP TS 36.314 [26] Mean/Max number of Active UEs (DL/UL, total, per DRB(mapped 5QI)) 5G E2 and O1 3GPP TS 38.314 [27] 3GPP TS 32.425 [28] Traffic demand/buffer size (Total, per QCI/5QI) 4G/5G E2 and O1 PRB usage (DL, UL, Total, per QCI/5QI) 4G/5G E2 and O1 3GPP TS 36.314 [26] 3GPP TS 36.423 [25] 3GPP TS 28.552 [6] PDCCH CCE usage 4G/5G E2 and O1 3GPP TS 36.423 [25] RRC connection number 5G E2 and O1 3GPP TS 28.552 [6] 3GPP TS 32.425 [28] 4G/5G QoS configuration and parameters QoS Classes 4G/5G E2 3GPP TS 23.501 [2] (5G) 3GPP TS 36.300 [29] 3GPP TS 23.401 [30] 3GPP TS 23.203 [31] (4G) Slice types 5G E2 3GPP TS 23.501 [2] UE performance statistics Scheduled IP Throughput (DL, UL, per QCI) 4G E2 and O1 3GPP TS 36.314 [26] Data Volume (DL/UL per CQI) 4G E2 and O1 3GPP TS 36.314 [26] UL/DL PDCP SDU Data Volume 5G E2 and O1 3GPP TS 28.552 [6] PDCP Packet Delay DL/UL per CQI/QCI 4G/5G E2 and O1 3GPP TS 36.314 [26] (4G) ETSI ETSI TS 104 036 V12.0.0 (2025-04) 68 Category Parameters / Measurements RAT Source / Interface Reference UE mobility statistics RSRP/RSRQ/SINR/RSSI 4G/5G E2 and O1 3GPP TS 36.214 [32] 3GPP TS 36.331 [33] UE Location Information 45/5G External Server UE Capability 4G/5G E2 and O1 3GPP TS 36.331 [33] |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.12 Use case 12: NSSI Resource Allocation Optimization | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.12.0 Introduction | This use case provides the background, motivation, description, and requirements for the NSSI resource allocation optimization use case, allowing operators to optimize the allocation resources to NSSI(s) with wide range service requirements. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.12.1 Background and goal of the use case | 5G networks are becoming increasingly complex with the densification of millimeter wave small cells, and various new services, such as enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine Type Communications (mMTC) that are characterized by high speed high data volume, low speed ultra-low latency, and infrequent transmitting low data volume from huge number of emerging smart devices, respectively. It is a challenging task for 5G networks to allocate resources dynamically and efficiently among multiple network nodes to support the various services. However, as eMBB, URLLC, and mMTC services in 5G are typically realized as Network Slice Instance (NSI). Therefore, the resources allocated to Network Slice Subnet Instance (NSSI) to support the O-RAN nodes can be optimized according the service requirements. As the new 5G services have different characteristics, the network traffic tends to be sporadic, where there can be different usage pattern in terms of time, location, UE distribution, and types of applications. For example, most IoT sensor applications can run during off-peak hours or weekends. Special events, such as sport games, concerts, can cause traffic demand to shoot up at certain time and locations. Therefore, NSSI resource allocation optimization function trains the AI/ML model, based on the huge volume of performance data collected over days, weeks, months from O-RAN nodes. It then uses the AI/ML model to predict the traffic demand patterns of 5G networks in different times and locations for each network slice, and automatically re-allocates the network resources ahead of the network issues surfaced. The resource quota policies associated with RAN NFs (E2 Nodes) included in the respective NSSIs enable 5G network providers to optimize or prioritize the utilization of the RAN resources across slices and supports the flexibility to share resources optimally across critical service slices during resource surplus or scarcity. For example, an NSSI allocated for premium service can receive a major share of the resources compared to a slice allocated for a standard/best-effort service. Another such example is the scenario of additional resource allocation for emergency services. An important consideration here is that the NSSI resource quota policies focuses on maximization of resource utilization across the NSSIs. The resource quota policies can be used as a constraint for resource allocation that defines the range of resources that can be allocated per slice. One use case for applying such a constraint is the analysis and decision based on history of resource allocation failure that can be reflected in the RAN Node measurements. Here resource quota policy can be provisioned to control the minimum, maximum and dedicated resources that need to be allocated based on the historical pattern. The NSSI resource allocation Optimization on the Non-RT RIC is shown in figure 4.12.1-1, and can consist of the following steps: 1) Monitoring: monitor the radio network(s) by collecting data via the O1 interface, for example including the following performance measurements that are measured on per S-NSSAI (3GPP TS 28.552 [6] shall apply): - DL PRB used for data traffic (3GPP TS 28.552 [6], clause 5.1.1.2.5 shall apply) - UL PRB used for data traffic (3GPP TS 28.552 [6], clause 5.1.1.2.7 shall apply) ETSI ETSI TS 104 036 V12.0.0 (2025-04) 69 - Average DL UE throughput in gNB (3GPP TS 28.552 [6], clause 5.1.1.3.1 shall apply) - Average UL UE throughput in gNB (3GPP TS 28.552 [6], clause 5.1.1.3.3 shall apply) - Number of PDU Sessions requested to setup (3GPP TS 28.552 [6], clause 5.1.1.5.1 shall apply) - Number of PDU Sessions successfully setup (3GPP TS 28.552 [6], clause 5.1.1.5.2 shall apply) - Distribution of DL UE throughput in gNB (3GPP TS 28.552 [6], clause 5.1.1.3.2 shall apply) - Distribution of DL UE throughput in gNB (3GPP TS 28.552 [6], clause 5.1.1.3.4 shall apply) - Number of DRBs successfully setup (3GPP TS 28.552 [6], clause 5.1.1.10.2 shall apply) NOTE 1: The above measurements are indicative and are subject to change based on the progress of this use case in O-RAN. NOTE 2: Monitoring of the measurements related to O-Cloud (or transport network) that can be required for NSSI resource optimization is not supported in this version of the specification. 2) Analysis & Decision: consisting of the following steps: 2a. Utilize AI/ML models to analyse the measurements and predict the future traffic demand, including the RRMPolicyRatio IOC limits, for each NSSI for a given time and location. 2b. Determine the actions needed to add or reduce the resources (e.g. capacity, VNF resources, slice subnet attributes (3GPP TS 28.541 [5] shall apply, etc.) for the RAN NFs (E2 Nodes included in the respective NSSI at the given time, and location. 3) Execution: execute the actions to reallocate the NSSI resources that include: 3a. Re-configure the NSSI attributes, including RRMPolicyRatio IOC (3GPP TS 28.541 [5] shall apply) via the OAM Functions in SMO which uses O1 interface to configure the E2 Nodes. 3b. Update the cloud resources via the O2 interface. Service management & Orchestration Framework Near-RT RIC O-DU O-CU- CP O-CU- UP O-Cloud E2 E1 F1-C F1-U Open Fronthaul interface O-RU A1 O1 O2 E2 Network functions Non-RT RIC NSSI Resource Allocation Optimization 2. analysis & decision 1. monitoring O-Cloud M&O E2 3.a. execution Figure 4.12.1-1: The realization of NSSI resource allocatıon optimization over Non-RT RIC For association of resource quota policies for the RAN NFs (E2 Nodes) per NSSI or group of NSSIs, RRMPolicyRatio IOC (realization of abstract IoC RRMPolicy_) is currently being specified in 3GPP TS 28.541 [5] which allows definition of maximum, minimum and dedicated values for the percentage of resources to be used per RRMPolicyMemberList - that is group of members with specific plmnID and sNSSAI (applied at NRCellDU, NRCellCU, GNBDUFunction, GNBCUCPFunction or in GNBCUUPFunction) via RRMPolicyManagedEntity. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 70 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.12.2 Entities/resources involved in the use case | 1) SMO: a) Pre-provision the default NSSI resource quota policy as constraint for NSSI resource allocation optimization. This information is optionally used by the Non-RT RIC in case the resource quota that needs to be allocated per slice is not specified during the slice creation and for conflict resolution at the time of resource scarcity. 2) Non-RT RIC: a) Collect the performance measurements related to NSSI resource usage from the O-RAN nodes via the O1 interface. b) Train the AI/ML model based on the analysis of historical performance measurements, to predict of the traffic demand patterns of NSSI at different times and locations. c) Determine the time/date and locations (i.e. which O-RAN nodes) to add or reduce the resources (e.g. capacity, VNF resources, slice subnet attributes (3GPP TS 28.541 [5] shall apply), RRMPolicyRatio IOC, etc.) for a given NSSI based on inference. d) Perform the following action(s) to optimize the NSSI resource allocation, at the time determined by the model: i) Re-configure the NSSI attributes via the O1 interface. ii) Update the cloud resources via the O2 interface. 3) RAN Nodes (O-CU-CP, O-CU-UP, O-DU, O-RU): a) Support the performance measurement collection with required granularity over O1 interface. b) Support the configuration related to the NSSI resource allocation update over O1 interface. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.12.3 Solutions | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.12.3.1 NSSI Resource Allocation Optimization | The context of the NSSI Resource Allocation Optimization is captured in table 4.12.3.1-1. Table 4.12.3.1-1: NSSI Resource Allocation Optimization Use Case stage Evolution/Specification <<Uses>> Related use Goal To automatically optimize the NSSI resource allocation by leveraging the AI/ML model that was trained via the analysis of performance measurements collected from the RAN nodes. Actors and Roles SMO: Pre-Provision the default resource quota policy as constraint for resource allocation optimization and monitor runtime context change. Non-RT RIC: analysis of performance measurements and AI/ML model training. RAN nodes (O-CU-CP, O-CU-UP, O-DU, O-RU): performance measurements collection and configuration changes execution. O-Cloud M&O: the cloud resources modification via the O2 interface. OAM Functions: Part of SMO which manages the O1 based OAM functionality. O-Cloud: Manages virtualization infrastructure and virtualized resources. Assumptions - All relevant functions and components are instantiated. - Non-RT RIC is able to receive performance measurements from RAN nodes via the O1 interface. Pre-conditions - RAN is operational. - OAM Function is pre-provisioned with default NSSI resource quota policy - Non-RT RIC has been collecting the RAN performance measurements from RAN nodes. Begins when An AI/ML model has been trained based on the analysis of performance measurements predict of the traffic demand patterns of NSSI at different times and locations, resource quora per slice. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 71 Use Case stage Evolution/Specification <<Uses>> Related use Step 1 (M) Non-RT RIC collects the RAN performance measurements from RAN nodes Step 2 (M) i. Non-RT RIC utilizes the AI/ML models to analyse the measurements and predict future the traffic demand for each NSSI for a given time and location. ii. Non-RT RIC determines the action based on model inference to update the NSSI resources that can include the following information: a) the time/date, b) locations (e.g. gNB ID), c) NSSI ID, d) slice subnet attributes, e) VNF resources update (e.g. scaling in/out), f) NSSI resource quota policy to be enforced per slice over O1 interface. Step 3 (M) Non-RT RIC executes the action at the time determined by the model inference by performing the following operations: a) re-configure the slice subnet attributes, including RRMPolicyRatio IOC (3GPP TS 28.541 [5] shall apply) via the OAM Functions in SMO which uses O1 interface to configure the E2 Nodes, b) request O-Cloud M&O to update the O-Cloud resources via the O2 interface. The execution of these steps is carried out by SMO based on the recommendation of the Non-RT RIC. Ends when All the steps identified above are successfully completed. Exceptions One of the steps identified above fails. Post-conditions Near-RT RIC continues monitoring the NSSI resource usages. The flow diagram of the NSSI Resource Allocation Optimization is given in figure 4.12.3.1-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 72 Figure 4.12.3.1-1: Flow diagram, NSSI Resource Allocation Optimization |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.12.4 Required data | The measurement counters, as specified in 3GPP TS 28.552 [6], which are measured on per S-NSSAI include: • DL PRB used for data traffic (3GPP TS 28.552 [6], clause 5.1.1.2.5 shall apply) • UL PRB used for data traffic (3GPP TS 28.552 [6], clause 5.1.1.2.7 shall apply) • Average DL UE throughput in gNB (3GPP TS 28.552 [6], clause 5.1.1.3.1 shall apply) • Average UL UE throughput in gNB (3GPP TS 28.552 [6], clause 5.1.1.3.3 shall apply) • Number of PDU Sessions requested to setup (3GPP TS 28.552 [6], clause 5.1.1.5.1 shall apply) • Number of PDU Sessions successfully setup (3GPP TS 28.552 [6], clause 5.1.1.5.2 shall apply) • Distribution of DL UE throughput in gNB (3GPP TS 28.552 [6], clause 5.1.1.3.2 shall apply) • Distribution of DL UE throughput in gNB (3GPP TS 28.552 [6], clause 5.1.1.3.4 shall apply) • Number of DRBs successfully setup (3GPP TS 28.552 [6], clause 5.1.1.10.2 shall apply) ETSI ETSI TS 104 036 V12.0.0 (2025-04) 73 NOTE: The above measurements are indicative and are subject to change based on the progress of this use case in O-RAN. The Monitoring of the measurements related to O-Cloud (or transport network) that can be required for NSSI resource optimization is not supported in this version of the specification. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.13 Use case 13: Local Indoor Positioning in RAN | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.13.0 Introduction | This use case provides the background and motivation for the O-RAN architecture to support local indoor positioning. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.13.1 Background and goal of the use case | Real-time indoor positioning based on cellular network has aroused attention with the development of 5G vertical industries, individuals and operators. NR positioning is introduced by 3GPP Rel.16. The location management function (LMF) resides in core network request the NG-RAN node to report positioning measurements, which is used by LMF to compute the location of UE. The messages between LMF and the NG-RAN need the AMF to route transparently. However, this long route messages between the NG-RAN node and centralized LMF can suffer network jitters and leads to un-real-time UE location results. The main objective is to ensure local positioning be supported within the O-RAN architecture and its open interfaces. In the context of O-RAN architecture, the positioning function can be deployed as a positioning xApp in the Near-RT RIC. The positioning xApp computes the UE location and optional velocity based on the positioning measurement obtained via the E2 interface. The local indoor positioning results can be acquired via positioning xApp to support positioning applications (e.g. indoor navigation, electric security fence, etc.). |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.13.2 Entities/resources involved in the use case | 1) Non-RT RIC: a) Retrieve necessary positioning-related indicators (e.g. RSSI, labeled user location by manual or by minimal drive test, etc.) from positioning measurement report or network level measurement report or enrichment information from SMO (can acquire data from application). The data is for constructing/training relevant AI/ML model that will be deployed in Near-RT RIC to assist in the Position Computation function. b) Training of potential ML models for real-time positioning optimization, which can be used to compute the position, correct positioning errors, and predict motion. c) Send policies/intents to Near-RT RIC to drive the positioning optimization at RAN level. 2) Near-RT RIC: a) Support selection of positioning algorithms (e.g. according to QoS requirements, etc.). b) Support the calculation of positioning results based on the measurements from RAN. c) Support update of AI/ML models from Non-RT RIC. d) Support execution of the AI/ML models from Non-RT RIC, e.g. positioning result calculation. e) Sending positioning results to Non-RT RIC for evaluation and optimization. 3) RAN: a) Support positioning related measurement report over E2 interface. b) Support positioning related measurement report over O1 interface. 4) Application Server: a) Request/subscribe RAN analytics information from Near-RT RIC. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 74 b) Support positioning related enrichment information (e.g. labeled user location by manual or by minimal drive test, etc.) to SMO. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.13.3 Solutions | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.13.3.1 Local Indoor Positioning in RAN (1) | The context of the Local Indoor Positioning in RAN (1) is captured in table 4.13.3.1-1. Table 4.13.3.1-1: Local Indoor Positioning in RAN (1) Use Case Stage Evolution / Specification <<Uses>> Related use Goal Expose positioning results to external applications. Actors and Roles Near-RT RIC, SMO, application server. Assumptions All relevant functions and components are instantiated. Pre conditions Editor's Note: security related procedure is not defined in the present document. Begins when The application server wants to request/subscribe RAN positioning results of target UE. Step 1 (M) Application server sends positioning request of target UE to Near-RT RIC, or subscribes positioning results from Near-RT RIC to get periodic or event triggered position reporting. Step 2 (M) Near-RT RIC receives the request or subscription from application server, and requests or subscribes measurements to RAN through E2 interface. The Near-RT RIC selects the positioning algorithm based on the request or the measurement data from RAN. Step 3 (M) RAN reports the measurements to Near-RT RIC according to the request or subscription. Step 4 (M) Near-RT RIC calculates the positioning results based on the measurement report from RAN, using the selected positoning algorithm. Step 5 (M) Near-RT RIC sends the response or notification command to expose radio performance analytics towards application server. Ends when Application server gets response, or sends subscription deletion toward the Near-RT RIC. Exceptions None identified. The flow diagram of the Local Indoor Positioning in RAN (1) is given in figure 4.13.3.1-1. Figure 4.13.3.1-1: Local Indoor Positioning in RAN (1) flow diagram |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.13.3.2 Local Indoor Positioning in RAN (2) | The context of the Local Indoor Positioning in RAN (2) is captured in table 4.13.3.2-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 75 Table 4.13.3.2-1: Local Indoor Positioning in RAN (2) Use Case Stage Evolution / Specification <<Uses>> Related use Goal Expose positioning results to external applications. Actors and Roles Non-RT RIC, Near-RT RIC, SMO, application server Assumptions All relevant functions and components are instantiated. A1/O1 interface connectivity is established with Non-RT RIC. Pre conditions Positioning related models have been deployed in Non-RT RIC and Near- RT RIC respectively. Editor's Note: security related procedure is not defined in the present document. Begins when The application server wants to request/subscribe RAN positioning results of target UE. Step 1 (M) Application server sends positioning request of target UE to Near-RT RIC or subscribes positioning results from Near-RT RIC to get periodic or event triggered position reporting. Step 2 (M) Near-RT RIC receives the request or subscription from application server and requests or subscribes measurements to RAN through E2 interface. The Near-RT RIC selects the positioning algorithm based on the request or the measurement data from RAN and can update the positioning related models from Non-RT RIC. Step 3 (M) RAN reports the measurements to Near-RT RIC according to the request or subscription. Step 4 (M) Near-RT RIC calculates the positioning results based on the positiong report from RAN, usingthe selected positoning algorithm. Step 5 (M) Near-RT RIC sends the response or notification command to expose radio performance analytics towards application server. Near-RT RIC can also pass the positioning results to the Non-RT RIC for further analysis. Ends when Application server gets response or sends subscription deletion toward the Near-RT RIC. Exceptions None identified. The flow diagram of the Local Indoor Positioning in RAN (2) is given in figure 4.13.3.2-1. Figure 4.13.3.2-1: Local Indoor Positioning in RAN (2) flow diagram ETSI ETSI TS 104 036 V12.0.0 (2025-04) 76 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.13.4 Required data | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.13.4.0 Non-RT RIC and Near-RT RIC required data | Multi-dimensional data are expected to be retrieved by Non-RT RIC for AI/ML model training and policies/intents generation. 1) Network level measurement report, including UE level radio channel information, mobility related metrics, e.g. RSRP, RSSI, etc. 2) Positioning measurement report, including UE level E-CID, OTDOA, UTDOA, TOA, RSSI, AOA, etc. 3) Enrichment information (optional) collected from SMO (can acquire data from application), can including labeled user location by manual or by minimal drive test, etc. Near-RT RIC required data to select the positioning algorithm and calculate the positioning results. 1) Positioning measurement report, including UE level E-CID, OTDOA, UTDOA, TOA, RSSI, AOA, etc. 2) Performance requirements in positioning requests (optional) such as QoS. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.13.4.1 RAN Analytics Information | Radio performance analytics data are expected to be exposed by Near-RT RIC to application server. 1) UE positioning results, including location coordinates, coordinate system, position methods used (in the case of success indication provided), failure cause (in the case of failure indication provided), achieved location QoS accuracy (optional). 2) Velocity estimation (optional). |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.14 Use case 14: Massive SU/MU-MIMO Grouping Optimization | Void. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.15 Use case 15: O-RAN Signalling Storm Protection | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.15.0 Introduction | This use case provides the background, motivation, and requirements for the O-RAN Signaling Storm Protection use case, allowing protecting the mobility network against signaling storms initiated by devices. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.15.1 Background and goal of the use case | Society is increasingly dependent on network connectivity at any time and in any place and increasing diversity of device types ranging from complex devices such as smart phone to very simple and low-cost IoT devices are connecting to the network. The sheer number of connected devices, as well as the wide range of device types, makes the mobility network subject to accidental or intentional attacks that can disrupt the regular usage of the network. Given that life- critical applications are moving to wireless networks, such network disruptions are not only an inconvenience but can have impact on life and health of individuals. The O-RAN architecture offers an opportunity to address such security challenges in customizable and creative ways by utilizing the Near-RT RIC xApps and Non-RT RIC rApps. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 77 Currently, the main defense mechanism standardized in 3GPP against attacks coming from the devices toward the network is based on configuration of the devices themselves and trust that the devices will indeed comply with restrictions defined by mobility standards. One such defense mechanism is the back-off timer that restricts the number of repeated device registrations, thus preventing devices from overloading the network with attaches. If this trust is breached there are no other options for defending the network rather than rejecting (denying service) randomly to both benign and malicious devices, a state which is equivalent to DDoS. Unfortunately, even today the network has few hundreds of device types that under certain conditions accidently breach this trust and allow devices to aggressively attach to the network in a rate of few thousand times per hour (the maximum allowed number by standard is less than 20 attaches per hour). An attacker that finds a way to manipulate vulnerabilities in a large set of these devices remotely can cause an attach storm that could lead to a long outage of large parts of the network. Furthermore, this attacker can continue this attack over many hours, each time picking few thousand of devices from a large pool of millions of vulnerable devices connected to the same carrier network; the network carrier will not be able to stop this attack without intelligent and fine-grained controls to act against a certain patterns of behavior. Fortunately detecting these aggressive devices is possible as their behavior is very different from the other devices in the network. What the network really needs is to apply dynamic restriction over these devices to prevent them from overloading the control plane of the network. This restriction should be smart enough to still allow benign devices to register to the network without interruption. Having smart security control at the RAN can stop such attack and without overloading deeper parts of the network in the core. The goal of this use case is utilize O-RAN to detect and mitigate signaling storms DDoS quick and as close to the network edge, thus minimizing affected network nodes. The Near-RT RIC would detect these signaling storms by analyzing signaling events from RAN nodes it controls. When such a storm is detected the Near-RT RIC creates fine grained filters, which cover the aggressive UEs that cause the storm. These UEs registration requests will then be blocked/throttled while the behaving UEs will continue to get service as usual. In some cases the attack can be spread across many locations. It could be that the volume of signaling per location has not crossed a critical threshold but the moderate increase in many locations do cause an overload of central nodes such as the network core elements. In this case a network-wide view is required; thus the Non-RT RIC performs the network-wide analysis and in the case of a network signaling storm, it pushes policies to the local Near-RT RIC to adjust detection parameters to reduce the moderate increase of signaling from a set of one or more E2 Nodes. This combined view of both Non-RT RIC and Near-RT RIC ensures quick reaction to local signaling storms as well as response to widely distributed attacks. While flows in this use case focus on the signaling storm scenario, they could be easily extend to include other attack scenarios both in terms of detection and mitigation. For example, the scenario where rogue devices report false CQI measurements that indicate high values while the real channel quality is poor. When exploited by attackers and applied to large set of devices this attack can cause to waste of radio resources and eventually to DoS. Detection of the attack can be achieved by analyzing anomalous CQI reports or abnormal volume of NACK messages based on signaling messages. For mitigation actions either rejecting the rogue devices or limiting radio resources can be applied. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.15.2 Entities/resources involved in the use case | 1) Non-RT RIC in SMO domain: a) Maintains overall view of network wide phenomenon of signaling storms using Signaling Storm Detection rApp. The detection of distributed signaling storms that spread over many geographical locations and are more difficult to be observed locally. This overall view is broken down by location and corresponding policies are pushed to specific instances of Near-RT RIC to respond to abnormal signaling activity in affected geographical areas, over the A1 interface. b) Uses enrichment data from non-RAN source (i.e. 5G core or probing framework) to maintain global view and support more accurate detection and classification of attacks. c) Utilizes AI/ML models in the Signaling Storm Detection rApp that monitor network-level signaling behavior to support signaling anomalies detection. 2) Near-RT RIC in RAN domain: a) Monitors E2 interface for connection establishment messages and identifies abnormal levels of signaling activity using the Signaling Storm Detection xApp. b) Signaling Storm Mitigation xApp utilizes policies over E2 to enforce appropriate mitigation action (e.g. reject, throttle, alert) over misbehaving UEs connection establishment. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 78 c) Signaling Storm Detection xApp utilizes AI/ML models that monitor cell-level signaling behavior to support signaling anomalies detection. d) Applies appropriate detection policy based on policies received from Non-RT RIC (e.g. false-positive levels, UE thresholds, throttling ratios). 3) E2 Nodes in RAN domain: a) Support sending connection establishment messages over the E2 interface. b) Support control and policy enforcement from Near-RT RIC over E2 interface. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.15.3 Solutions | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.15.3.1 Mode 1 - Local Signaling Storm Protection Policy | The context of the Local Signaling Storm Protection Policy is captured in table 4.15.3.1-1. Table 4.15.3.1-1: Local Signaling Storm Protection Policy Use Case Stage Evolution / Specification <<Uses>> Related use Goal Detect localized signaling storms based on "default parameters" and apply policy to mitigate the attack. Actors and Roles Near-RT RIC: detection of local cell-level signaling storms; execution of mitigation policies and controls, maintenance of cell-level normal behavior models. E2 Nodes: execute mitigation policies, collects and reports RAN signaling events and policy specific statistics over E2. Assumptions All relevant functions and components are instantiated. Signaling Storms Detection and Signaling Storm Mitigation xApps are deployed over E2 with initial configuration. E2 interface connectivity is established with Non-RT RIC and RAN respectively. Data report, policy and control subscription established on E2 interface. Pre conditions Network is operational. SMO has established the data collection and sharing interface with Non-RT RIC. Near-RT RIC already established relevant detection mechanisms of normal signaling behavior and adjusted detection parameters accordingly. Non-RT RIC analyses the historical data from RAN, develops, trains with help of SMO functions and deploys the models or algorithm as part of the Signaling Storm Detection xApp to the Near-RT RIC. Begins when Network is in normal state (attack is described later on). Step 1 (M) Signaling Storm Detection xApp subscribes on connection establishment signaling messages report from the RAN over the E2 interface. Step 2 (M) E2 Node sends report to Signaling Storm Detection xApp. Step 3 (M) Near-RT RIC Signaling Storm Detection xApp monitors reports to detect aggressive UEs that act with abnormal signaling. Steps 4-7 (M) UEs send establish connection messages and E2 Node accepts these requests. Step 8 (M) E2 Node sends a connection establishment reports. Step 9 (M) Signaling Storm Detection xApp detects aggressive activity. Step 10 (M) Signaling Storm Detection xApp updates Signaling Storm Mitigation xApp. Step 11 (M) Near-RT RIC Signaling Storm Mitigation xApp creates a filter to block/throttle signaling messages from the aggressive UEs. Filter is applied in the E2 Nodes as POLICY + REPORT to track filter activity. Near-RT RIC shall notify the Non-RT RIC to avoid conflicts. Step 12 (M) Aggressive UE sends connection establishment message. Step 13 (M) E2 Node evaluate policy with respect to the connection establishment message. Step 14 (M) E2 Node rejects/throttles connection establishment request. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 79 Use Case Stage Evolution / Specification <<Uses>> Related use Step 15 (M) Near-RT RIC Signaling Storm Mitigation xApp receives relevant signaling messages that the POLICY filter blocked/ throttled to track changes in attack status and aggressive devices (list of UEs blocked, blocked signaling volume, trend). Step 16 (M) Near-RT RIC Signaling Storm Mitigation xApp is finds that some devices are no longer aggressive or no longer present. It decides to update filter by updating the E2 Node POLICY. Steps 17-19 (M) Near-RT RIC Signaling Storm Detection xApp detects a new set of aggressive devices and updates the Signaling Storm Mitigation xApp, which updates the filter by updating the E2 Node POLICY. Steps 20-21 (M) Near-RT RIC Signaling Storm Mitigation xApp evaluates signaling level and decides that there is no more aggressive UE activity. The xApp removes the E2 Node policy. Ends when Attack is over and signaling messages level is back to normal. Exceptions None identified. Post Conditions Return to normal signaling activity monitoring (Step 1). The flow diagram of the Local Signaling Storm Protection Policy is given in figure 4.15.3.1-1. Figure 4.15.3.1-1: Local Signaling Storm Protection Policy flow diagram |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.15.3.2 Mode 1 - Local Signaling Storm Protection Insert-Control (Optional) | The context of the Local Signaling Storm Protection Insert-Control is captured in table 4.15.3.2-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 80 Table 4.15.3.2-1: Local Signaling Storm Protection Insert-Control Use Case Stage Evolution / Specification <<Uses>> Related use Goal Detect localized signaling storms based on "default parameters" and apply control to mitigate the attack. Actors and Roles Near-RT RIC: detection of local cell-level signaling storms; execution of mitigation policies and controls, maintenance of cell-level normal behavior models. E2 Nodes: execute UE level mitigation policies, collects and reports RAN signaling events and policy specific statistics over E2. Assumptions All relevant functions and components are instantiated. Signaling Storms Detection and Signaling Storm Mitigation xApps are deployed over E2 with initial configuration. E2 interface connectivity is established with Non-RT RIC and RAN respectively. Data report, policy and control subscription established on E2 interface. Pre conditions Network is operational. SMO has established the data collection and sharing interface with Non-RT RIC. Near-RT RIC already established relevant detection mechanisms of normal signaling behavior and adjusted detection parameters accordingly. Non-RT RIC analyses the historical data from RAN, develops, trains with help of SMO functions and deploys the models or algorithm as part of the Signaling Storm Detection xApp to the Near-RT RIC. Begins when Network is in normal state (attack is described later on). Step 1 (M) Signaling Storm Detection xApp subscribes on connection establishment signaling messages report from the RAN over the E2 interface. Step 2 (M) E2 Node sends report to Signaling Storm Detection xApp. Step 3 (M) Near-RT RIC Signaling Storm Detection xApp monitors reports to detect aggressive UEs that act with abnormal signaling. Steps 4-7 (M) UEs send establish connection messages and E2 Node accepts these requests. Step 8 (M) E2 Node sends a report indicating aggressive devices behavior. Step 9 (M) Signaling Storm Detection xApp detects aggressive activity. Step 10 (M) Signaling Storm Detection xApp updates Signaling Storm Mitigation xApp. Step 11 (M) Signaling Storm Mitigation xApp updates subscription to INSERT-CONTROL. Use control filter to block/throttle aggressive UEs by rejecting some of the messages. Step 12 (M) E2 Node receives another connection establishment from an aggressive UE. Step 13 (M) E2 Node forwards the message to the Signaling Storm Mitigation xApp. Step 14 (M) Signaling Storm Mitigation xApp determines that message is from an aggressive device. Step 15 (M) Signaling Storm Mitigation xApp sends a reject/throttle message to the E2 Node. Step 16 (M) E2 Node rejects/throttles connection establishment request. Step 17 (M) Signaling Storm Mitigation xApp continues to monitor its control filter. Step 18 (M) Near-RT RIC DDoS Mitigation xApp evaluates signaling level and decides that there is no more aggressive UE activity. The xApp updates subscription back to REPORT. Ends when Attack is over and signaling messages level is back to normal. Exceptions None identified. The flow diagram of the Local Signaling Storm Protection Insert-Control is given in figure 4.15.3.2-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 81 Figure 4.15.3.2-1: Local Signaling Storm Protection Insert-Control flow diagram |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.15.3.3 Mode 2 - Distributed Signaling Storm Protection | The context of the Distributed Signaling Storm Protection is captured in table 4.15.3.3-1. Table 4.15.3.3-1: Distributed Signaling Storm Protection Use Case Stage Evolution / Specification <<Uses>> Related use Goal Detect distributed signaling using Non-RT RIC and A1 policy initiates Mode 2 handling in Near-RT RIC with "stricter parameters" mitigation. Actors and Roles Non-RT RIC: detection of network-level distributed signaling storms, maintenance of cell-level, network slice level and node level normal behavior models. Near-RT RIC: detection of local cell-level signaling storms; execution of mitigation policies and controls, maintenance of cell-level normal behavior models RAN: executes UE level or network slice level mitigation policies, collects and reports RAN signaling events and policy specific statistics over E2. Assumptions All relevant functions and components are instantiated. Signaling Storms Detection and Signaling Storms Mitigation xApps are deployed over E2 with initial configuration. A1, E2 interface connectivity is established with Non-RT RIC and RAN respectively. Data report, policy and control subscription established on E2 interface. Pre conditions Network is operational. SMO has established the data collection and sharing interface with Non-RT RIC. Non-RT RIC and Near-RT RIC already established relevant detection mechanisms of normal signaling behavior and adjusted detection parameters accordingly. Non-RT RIC analyses the historical data from RAN, develops, trains with help of SMO functions and deploys the models or algorithm as xApps to the Near-RT RIC. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 82 Use Case Stage Evolution / Specification <<Uses>> Related use Begins when Network is in normal state when (attack is described later on). Step 1 (M) OAM Functions start to collect enrichment information (EIs) from external sources (e.g. network core probing framework). Step 2 (M) OAM Functions start to collect alarms & metrics from E2 Nodes. Step 3 (M) OAM Functions sends signaling statistics based on collected information to Non-RT RIC. Step 4 (M) Non-RT RIC uses AI/ML model to analyse overall network signaling activity levels based on signaling statistics. Step 5 (M) Non-RT RIC applies initial configurations to all Near-RT RIC elements regarding detection and mitigation parameters, including: accepted signaling volume thresholds, throttle/block ratio, accepted false negative levels, filter pause periods, etc. Step 6 (M) Non-RT RIC detects distributed signaling storm activity originated from a list of locations. Step 7 (M) Non-RT RIC updates configuration to a stricter one in the relevant Near-RT RIC locations over A1 interface. Step 8 (M) Near-RT RIC performs detection and mitigation as described in clause 4.15.3.1 or clause 4.15.3.2 with stricter configuration (e.g. lower thresholds). Step 9 (M) Non-RT RIC determines that distributed signaling storm attack is over based on signaling statistics information. Step 10 (M) Non-RT RIC updates Near-RT RICs back to initial configuration parameters over the A1 interface. Step 11 (M) Near-RT RIC Signaling Storm Detection xApp observed aggressive behavior where temporal identifiers cannot be correlated with the underlying devices. Step 12 (M) Near RT RIC alarms the OAM Functions over O1. Step 13 (M) OAM Functions report suspicious behavior to Non-RT RIC. Step 14 (M) Non-RT RIC sends Enrichment Information to Near-RT RIC over A1-EI to support detection of aggressive devices. Step 15 (M) Near-RT RIC performs detection and mitigation as described in clause 4.15.3.1 or clause 4.15.3.2 with stricter configuration (e.g. lower thresholds). Step 16 (M) Non-RT RIC evaluates data and decides that there is no more distributed signaling storm activity. Step 17 (M) Non-RT updates configuration of relevant Near-RT RICs over the A1 back to normal. Ends when Attack is over and signaling messages level is back to normal. Exceptions None identified. Post Conditions Non-RT RIC monitors network-level signaling messages statistics Near-RT RIC monitors cell-level signaling messages statistics The flow diagram of the Distributed Signaling Storm Protection is given in figure 4.15.3.3-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 83 Figure 4.15.3.3-1: Distributed Signaling Storm Protection flow diagram |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.15.4 Required data | The measurement counters, Detection within the Near-RT RIC is based upon analyzing per UE connection establishment messages events that include the following data: 1) Basic registration event parameters: timestamp, cell ID, temporary ID (e.g. C-RNTI, 5G-GUTI). 2) RAN parameters to correlate between a UE and registration events: e.g. RSRP/RSRQ, Timing Advance, Beam ID. Tracking status of ongoing attack by monitoring statistics of active filters that include the following data: 3) Number of UEs in the filter, number of requests blocked, trend (change over last x periods of time). Enrichment information from a non-RAN source regarding network-wide DDoS information: 4) Overloaded regions, overloaded sites, severity (% above normal). |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.16 Use case 16: Congestion Prediction and Management | Void. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.17 Use case 17: Industrial IoT Optimization | Void. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 84 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.18 Use case 18: BBU Pooling to achieve RAN Elasticity | Void. 4.19 Use case 19: Integrated SON Function within the O-RAN framework |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.0 Introduction | This use case provides the motivation, description, and requirements for enabling the O-RAN framework to support a minimum SON function set. This use case enables realization of SON functions in the O-RAN architectural framework to help operators address issues seen from vendor specific SON implementation in earlier generation of cellular networks. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.1 Background and goal of the use case. | SON (Self-Organizing Network) functionalities reduces the cost of running a mobile network by providing control on aspects of network configuration and control and thus eliminating manual configuration of network elements right from initial deployment through the network operation. SON also helps better network performance and customer experience and can significantly improve OpEx-to-revenue ratio and help in realizing avoidable CapEx. SON is an automation technology that enables the network to set itself up and self-manage resources and configuration to achieve optimal performance. The SON functions are handled by SON algorithms either individually or in groups. SON algorithms perform functionalities like monitoring the network(s) by collecting management data including MDAS (Management Data Analytics Service) data, analysis of the data to determine issues in the network(s) and their resolution. SON intends to achieve the following: Self-Configuration: Aids in seamlessly integrating into the network through automatic configuration of key parameters (Initial PCI and ANR functions). Self-Optimization: Aids in enhanced network performance through near real time optimization of radio & network configurations. It is valuable throughout the lifetime of the network and includes SON functionalities such as Mobility Load Balancing [MLB], Mobility Robustness Optimization [MRO], Random Access Channel [RACH] Optimization etc. Self-Healing: It allows adjacent cells to maintain network quality in case a cell/sector fails, providing resiliency (reliability) in the face of unforeseen outage conditions. It is relevant throughout the lifetime of the network and includes SON functions such as Cell Outage Detection, Compensation and Recovery. The definitions for the SON functionality are specified in 3GPP TS 28.313 [20] but the realization of the SON functions is left to implementation. The SON coordination function for detecting, preventing and resolving conflicts or negative influences between multiple SON functions when there is an attempt to change some (same or associated) network configuration parameters of some (same or associated) nodes is also specified in 3GPP TS 28.313 [20]. Based on the deployment of SON algorithm, the SON solution can be termed as Centralized SON (C-SON - where the SON algorithms are executed in the 3GPP management system), Distributed SON (D-SON - where SON algorithms are executed in the Network Function layer) and Hybrid SON (where SON algorithm execution is spread across the network function layers and the management layers). The objective of this use case is to enable the realization of SON functions in the O-RAN architecture framework i.e. as rApps, xApps or as management entity functions through open interfaces in a way that inter vendor interoperability issues can be addressed. NOTE: Other deployment options other than the ones mentioned in this use-case are also possible. The scope of the Integrated SON use case covers the following functions: 1) Self Configuration: - PCI initial allocation and conflict resolution. - ANR (Automatic neighbor relations). ETSI ETSI TS 104 036 V12.0.0 (2025-04) 85 2) Self Optimization: - Mobility Load Balancing (MLB). - Mobility Robustness Optimization (MRO). - Coverage and Capacity Optimization (CCO). - RACH Optimization (RO). Editor's Note: R1 interface needs to be shown in the UMLs. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.2 Entities/resources involved in the use case | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.2.1 SON Inventory and Deployment Management | 1) SMO: - Support SON inventory and deployment management for Non-RT RIC(s), Near-RT RIC(s) and E2 Node(s). - Support collection of SON configurations from Non-RT RIC(s), Near-RT RIC(s) and E2 Node(s). - Support decision making on setup of the SON functions in Non-RT RIC(s), Near-RT RIC(s) and E2 Node(s). - Configure Non-RT RIC(s), Near-RT RIC(s) and E2 Node(s) based on the decided SON function deployment model. 2) Non-RT RIC: - Support exposure of SON functionalities and configurations. - Support configuration and setup of the SON functions from SMO. 3) Near-RT RIC: - Support exposure of SON functionalities and configurations. - Support configuration and setup of the SON functions from SMO via O1 interface. 4) E2 Node: - Support exposure of SON functionalities and configurations. - Support configuration and setup of the SON functions from SMO via O1 interface. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.2.2 Self Configuration (PCI Conflict detection/resolution, ANR) | 1) SMO: - Configure Self configuration SON functionality (PCI Conflict detection/resolution, ANR) in Non-RT RIC, Near-RT RIC or E2 Node. - Configure/Reconfigure the respective SON related parameters and measurements in the Non-RT RIC, Near-RT RIC and E2 Node. - Support collection of measurements or notifications from the respective O-RAN nodes. 2) Non-RT RIC: - Retrieve necessary data from SMO. - Support setup of SON function and configuration of relevant SON data inputs from SMO. - Support AI/ML training and inference and provide output via O1 or A1 to the relevant O-RAN nodes. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 86 3) Near-RT RIC: - Support setup of SON function and configuration of relevant SON data inputs from SMO. - Configure and receive necessary input data for AI/ML training of SON functions. - Support notifications to SMO related to SON functions. - Support AI/ML training and inference and provide output to E2 Node via E2 interface. - Support inputs from Non-RT RIC for AI/ML training and conversion of policies via A1 into E2 inputs. 4) E2 Node: - Support setup of SON function and configuration of relevant SON data inputs from SMO. - Support configuration and retrieval of necessary SON function related data for AI/ML model training via E2/O1 interfaces. - Support policies or configuration changes or relevant inputs via E2/O1 interface to execute RRM functionalities for the respective SON functions. - Support notifications to SMO related to SON functions. - Support AI/ML model inference and execute relevant RRM functionalities. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.2.3 Self Optimization (MLB, MRO, CCO, RO) | 1) SMO: - Configure the Self Optimization (MLB, MRO, CCO, RO) SON functionality in Non-RT RIC, Near-RT RIC or E2 Node. 2) Non-RT RIC: - Support setup of SON function and configuration of relevant SON data inputs from SMO. - Configuration and collection of cell load related information, HO related reports, CCO related measurement reports (RLF (Radio Link Failure), MDT (Minimization Drive Test), RCEF (RRC Connection Establishment Failure)), RACH performance reports for constructing relevant AI/ML models to assist in the Self Optimization SON functionality. - Support AI/ML model training and inference based on the input data received. - Re-configure inter-site/inter-rat Cell reselection parameters, HO related parameters, CCO related control parameters and RACH parameters based on AI/ML output. - Generate relevant A1 policies to execute any RRM function for the configured SON function. 3) Near-RT RIC: - Support setup of SON function and configuration of relevant SON data inputs from SMO. - Configuration and collection of load reports, HO related reports (HO failure and RLF), CCO related measurement reports (RLF, MDT, RCEF), RACH performance reports from E2 Nodes over E2 interface. - Support AI/ML model training and inference based on the input data received via O1 and E2. - Re-Configure HO related, Cell reselection parameters, CCO related control parameters and RACH parameters based on AI/ML output. - Support initiation of RRM functions like HO initiation, trigger Cell reselection at E2 Node via E2 Policies or Controls based on AI/ML output. - Support conversion of A1 policy into relevant E2 actions for executing RRM function for a specific SON function. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 87 4) E2 Node: - Support setup of SON function and configuration of relevant SON data inputs from SMO. - Report Measurement Report (MR), HO related information, coverage information, RACH performance over E2/O1 interface. - Support reconfiguration of HO related parameters, Cell reselection parameters, CCO related parameters, RACH parameters based on inputs via E2 or O1 interface. - Support initiation of RRM functions like HO initiation, Cell reselection, etc. based on inputs received via E2/O1 interface. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.3 Solutions | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.3.1 SON Inventory and Deployment Management | The context of the SON Inventory and Deployment Management is captured in table 4.19.3.1-1. Table 4.19.3.1-1: SON Inventory and Deployment Management Use Case Stage Evolution / Specification <<Uses>> Related use Goal • Management of SON configurations of Non-RT RIC, Near-RT RIC and E2 Node by the SMO. • Management of deployment of SON functions in Non-RT RIC, Near-RT RIC and E2 Node by the SMO. Actors and Roles • SMO acting as Controller for the SON Inventory and Deployment Management. • Non-RT RIC, Near-RT RIC and E2 Node acting as supporting entities by providing the required information and adhering to SON configuration by SMO. • Operator providing the necessary inputs to SMO for decision making on the SON function deployment. Assumptions • O1 interface connectivity between the SMO and E2 Node and Near-RT RIC is established. • E2 interface connectivity is established between E2 Node and Near-RT RIC. • A1 interface connectivity is established between Near-RT RIC and Non-RT RIC. • Network is operational. Pre conditions • SMO is unaware of the SON configurations of the O-RAN nodes. • SMO has necessary inputs from operator to decide the deployment of SON functions in the respective O-RAN nodes. • O-RAN nodes are capable of providing their SON configurations to SMO. Begins when Network becomes operational and operator configures the SMO for SON inventory and deployment management. Step 1, 2, 3 (M) • Operator sets the SON targets and the SON function deployment model. • SMO analyses the SON targets and the SON Deployment model and notifies the operator on the decision. • SMO inspects the SON inventory and decides on the need for retrieval of SON configuration from O-RAN nodes. Step 4, 5, 6, 7, 8, 9 (O) • If required, SMO initiates request to retrieve the configuration from O-RAN Nodes in a loop until all the necessary configurations are retrieved. • Based on the retrieved configuration, SMO re-evaluates the SON deployment model and notifies the operator if any changes to SON deployment model. Step 10, 11, 12 (O) • Alternatively, SMO can decide to configure the O-RAN Nodes with the necessary SON functions by deploying rApps or xApps to cater to the SON deployment model. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 88 Use Case Stage Evolution / Specification <<Uses>> Related use • SMO can notify the operator on the rApp and xApp deployments if needed. Step 13 - 17 (O) Based on the revised deployment model, SMO communicates the changes to the SON configurations and the SON function setup to the O-RAN nodes. Step 18 (M) The O-RAN nodes collect data, analyse and decide if any changes are needed to the configuration and notify operator for any modifications done. This is done in a loop until SON targets are met. Step 19 - 27 (M) If the SON functions need to be terminated based on inputs from operator, then SMO initiates deletion of SON configurations in the respective O-RAN Nodes and notifies the operator when the termination is completed. Ends when The SON functions are terminated by the operator or when the SON targets are met. Exceptions None. Post Conditions SMO, Non-RT RIC, Near-RT RIC and E2 Nodes interwork with each other seamlessly adhering to the SON function setup input from SMO. The flow diagram of the SON Inventory and Deployment Management is given in figure 4.19.3.1-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 89 Figure 4.19.3.1-1: SON Inventory and Deployment Management flow diagram ETSI ETSI TS 104 036 V12.0.0 (2025-04) 90 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.3.2 Self Configuration (PCI Conflict detection/resolution, ANR) | The context of the Self Configuration is captured in table 4.19.3.2-1. Table 4.19.3.2-1: Self Configuration Use Case Stage Evolution / Specification <<Uses>> Related use Goal Enable flexible deployment of the Self configuration SON Functions like PCI Conflict Detection/Resolution, ANR by means of configuration parameter change, regulating RRM function actions and allowing AI/ML-based solutions. Actors and Roles • SMO acting as parameter configuration function. • Non-RT RIC/ Near-RT RIC: Configuration decision making function. • E2 Node: configuration enforcement function, and measurement reporting function. Assumptions • O1 interface connectivity between the SMO and E2 Node, Near-RT RIC is established. • E2 interface connectivity is established between E2 Node and Near-RT RIC. • A1 interface connectivity is established between Near-RT RIC and Non-RT RIC. • Network is operational. Pre conditions SMO has configured the SON functions and required initial parameters in the respective O-RAN nodes via SON Inventory and Deployment Management as shown in clause 4.19.3.1. Begins when Operator enables the Self Configuration SON Functions like PCI Conflict Detection/Resolution, ANR and E2 Node becomes Operational. Step 1a, 1b, 1c (O) • Non-RT RIC initiates the specific measurement data collection request towards SMO and SMO towards E2 Node for AI/ML model training and for analysis of data for optimization. • E2 Node sends the configured measurement data via O1 interface to SMO and Non-RT RIC retrieves the required data from SMO. Step 2a, 2b, 2c, 2d, 2e, 2f (O) • Non-RT RIC can train the AI/ML model with the collected data from O1 interface and constantly monitors the performance of the E2 Node(s) for optimization. • Based on the output of the AI/ML processing the Non-RT RIC can trigger modification of configuration parameters through SMO via O1 interface to E2 Node. • Optionally Non-RT RIC can also generate and send A1 policies for initiation of HO etc. to Near-RT RIC. Near-RT RIC converts the A1 policies to E2 actions and forwards them to E2 Nodes. • Non-RT RIC continues to monitor the performance of the E2 Nodes and re-trains the AI/ML model in a loop. Step 3a, 3b (O) • Near-RT RIC initiates the specific measurement data collection request towards E2 Node. Near-RT RIC can use the collected data for optionally AI/ML model training and for analysis of data for optimization. • E2 Node sends the configured measurement data via E2 interface to Near-RT RIC. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 91 Use Case Stage Evolution / Specification <<Uses>> Related use Step 4a, 4b, 4c, 4d (O) • Near-RT RIC can train the AI/ML model with the collected data from E2 interface and constantly monitors the performance of the E2 Node(s) for optimization. • Upon trigger from the AI/ML processes, Near-RT RIC performs reconfiguration of parameters. • Optionally Near-RT RIC can request initiation of certain E2 Node Actions like HO or Cell Reselection, etc. • Near-RT RIC continues to monitor the performance of the E2 Nodes and re-trains the AI/ML model in a loop. Step 5 (O) E2 Node receives required inputs from SMO/Non-RT RIC and Near-RT RIC for execution of Self Configuration SON functions. Step 6a, 6b (O) • Based on the inputs received and the inputs from inbuilt RRM algorithm, E2 Node reconfigures parameters related to Self Configuration. • E2 Node can initiate certain RRM Actions like HO or Cell Reselection, etc. Ends when E2 Node becomes non-Operational or when the operator disables the Self Configuration SON functions. Exceptions One of the steps identified above fails. Post Conditions SMO/ Non-RT RIC, Near-RT RIC continues real time close loop optimization of Self Configuration SON functions. The E2 Node operates using the newly deployed parameters. The flow diagram of the Self Configuration is shown in figure 4.19.3.2-1. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 92 Figure 4.19.3.2-1: Self Configuration |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.3.3 Self Optimization (MLB, MRO, CCO, RO) | The context of the Self Optimization is captured in table 4.19.3.3-1. Table 4.19.3.3-1: Self Optimization Use Case Stage Evolution / Specification <<Uses>> Related use Goal Enable flexible optimization of the Self Optimizing SON Functions like MRO, MLB, CCO and RACH by means of configuration parameter change, regulating RRM function actions and allowing AI/ML-based solutions. Actors and Roles • SMO acting as parameter configuration function. • Non-RT RIC/ Near-RT RIC: Self Optimization decision making function. • E2 Node: configuration enforcement function, and measurement reporting function ETSI ETSI TS 104 036 V12.0.0 (2025-04) 93 Use Case Stage Evolution / Specification <<Uses>> Related use Assumptions • O1 interface connectivity between the SMO and E2 Node, Near-RT RIC is established. • E2 interface connectivity is established between E2 Node and Near-RT RIC. • A1 interface connectivity is established between Near-RT RIC and Non-RT RIC. • Network is operational. Pre conditions SMO has configured the SON functions and required initial parameters in the respective O-RAN nodes via SON Inventory and Deployment Management as shown in clause 4.19.3.1. Begins when Operator enables the optimization functions for SON Functions like MRO, MLB, CCO or RACH and E2 Node becomes Operational. Step 1a, 1b, 1c (O) • Non-RT RIC initiates the specific measurement data collection request towards SMO and SMO towards E2 Node for AI/ML model training and for analysis of data for optimization. • E2 Node sends the configured measurement data via O1 interface to SMO and Non-RT RIC retrieves the required data from SMO. Step 2a, 2b, 2c, 2d, 2e, 2f (O) • Non-RT RIC can train the AI/ML model with the collected data from O1 interface and constantly monitors the performance of the E2 Node(s) for optimization. • Based on the output of the AI/ML processing the Non-RT RIC can trigger modification of configuration parameters through SMO via O1 interface to E2 Node. • Optionally Non-RT RIC can also generate and send A1 policies for initiation of HO etc.to Near-RT RIC. Near-RT RIC converts the A1 policies to E2 actions and forwards them to E2 Nodes. • Non-RT RIC continues to monitor the performance of the E2 Nodes and re-trains the AI/ML model in a loop. Step 3a, 3b (O) • Near-RT RIC initiates the specific measurement data collection request towards E2 Node. Near-RT RIC can use the collected data to optionally train the AI/ML model and for analysis of data for optimization. • E2 Node sends the configured measurement data via E2 interface to Near-RT RIC. Step 4a, 4b, 4c, 4d (O) • Near-RT RIC can train the AI/ML model with the collected data from E2 interface and constantly monitors the performance of the E2 Node(s) for optimization. • Upon trigger from the AI/ML processes, Near-RT RIC performs reconfiguration of parameters related to Self Optimization. • Optionally Near-RT RIC can request initiation of certain E2 Node Actions like HO or Cell Reselection, etc. • Near-RT RIC continues to monitor the performance of the E2 Nodes and re-trains the AI/ML model in a loop. Step 5 (O) E2 Node receives required inputs from SMO/Non-RT RIC and Near-RT RIC for execution of Self Optimization SON functions. Step 6a, 6b (O) • Based on the inputs received and the inputs from inbuilt RRM algorithm E2 Node reconfigures parameters related to Self Optimization. • E2 Node can initiate certain RRM Actions like HO or Cell Reselection, etc. Ends when E2 Node becomes non-Operational or when the operator disables the optimization functions for SON functions like MRO, MLB, CCO or RACH. Exceptions One of the steps identified above fails. Post Conditions SMO/ Non-RT RIC, Near-RT RIC continues real time close loop optimization of Self Optimization SON functions. The E2 Node operates using the newly deployed parameters. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 94 The flow diagram of the Self Optimization is given in figure 4.19.3.3-1. Figure 4.19.3.3-1: Self Optimization |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.4 Required data | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.4.1 Self Configuration (PCI Conflict detection/resolution, ANR) | 1) SMO: - Network topology, GPS coordinates of the E2 Nodes, PCI allocation range as inputs from operator. - Information on PCI confusion or PCI conflict from E2 Node via O1 interface. - Neighbor information (PCI, ECGI, PLMN, TANAC, TAC, frequency bands) based on network topology as input from operator. 2) Non-RT RIC/ Near-RT RIC and E2 Node: - 3GPP RRC Measurement Reports with PCI information of the neighboring cells via E2 interface. - PCI allocation range via O1 interface from SMO. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 95 - Information on PCI confusion or PCI conflict from E2 Node via O1 interface. - Neighbour Cell Relation Table information of the neighboring cells via E2 interface. - 3GPP XN, X2 and NG mobility related messages via E2 interface from E2 Node. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.19.4.2 Self Optimization (MLB, MRO, CCO, RO) | 1) Non-RT RIC/ Near-RT RIC and E2 Node: - MLB - Load reports from Xn/X2/F1/E1 interface Resource Status Reporting procedures and Xn, X2 and NG mobility messages defined in 3GPP via E2 interface from E2 Node. - MLB - HO trigger control parameters available as specified in 3GPP TS 28.541 [5], clause 7.1.5. to E2 Node via E2 interface or O1 interface. - MRO - Xn, X2 and NG HO Reports, RLF reports, NG Uplink and Downlink RRC transfer messages, UE History information, coverage and quality information and XN/NG/X2 mobility related messages defined in 3GPP via E2 interface from E2 Node. - MRO - HO target and control parameters available as specified in 3GPP TS 28.541 [5], clause 7.1.2. to E2 Node via E2 interface or O1 interface. - CCO - RLF, MDT, Measurement Reports and RCEF related reports defined in 3GPP via E2 interface from E2 Node. - CCO related control parameters and control information available as specified in 3GPP TS 28.541 [5], clause 7.2.3. to E2 Node via E2 interface or O1 interface. - RACH Optimization - PRACH parameters available over XN/X2 interface, Contention detection per RACH attempt, number of RACH preambles per SSB, information on SSB threshold per RACH attempt defined in 3GPP via E2 interface from E2 Node. - HO target and control parameters available as specified in 3GPP TS 28.541 [5], clause 7.1.1. to E2 Node via E2 interface or O1 interface. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20 Use case 20: Shared O-RU | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1 Background and goal of the use case | |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.1 Common aspects & background for all Shared O-RU use cases | This use case provides the background, motivation, and requirements for the Lower Layer Split Multi Node Support, allowing to share an O-RU between multiple O-DU nodes, including single operator and multi-operator use cases. Shared O-RU use cases deliver a range of different benefits depending on specific scenarios. Shared O-RU support for single-MNO use cases delivers important resiliency and load balancing capabilities. Shared O-RU support for multiple-MNO use cases delivers important network sharing capabilities to complement established MOCN, MORAN and DAS approaches. Shared O-RU use cases cover the Class 2 BBU Pooling specified in clause 4.18. Shared O-RU use cases are associated with RAN Sharing use case. In particular, RAN Sharing depicts a Shared O-RU configuration, specified in clause 4.7. Shared O-RU feature also serves to support the Multi-Vendor (MV) Network Slicing use case, its operation and scenarios, specified in clause 4.10. Multi-vendor network slicing has implications to the front-haul as well. The Multi- vendor slicing use case will use dynamic resource allocation aspects of the Shared O-RU feature. The following subsections describe different configurations and deployment scenarios for Shared O-RU. They also describe use cases to accomplish key functionality such as a resiliency Use case. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 96 The Shared O-RU will have common solutions that span the sub-use cases. The sections that follow describe different aspects, configurations, and deployment scenarios for Shared O-RU; however, they will likely share common solutions which are described in the solution section. Expected use cases that accomplish a purpose between actors comprise the sub-use cases: such as software upgrade of a Shared O-RU, Start up of a Shared O-RU, Recovery from failed primary O-DUs that are sharing a O-RU, rehoming of a Shared O-RU in a network. State Management: The Shared O-RU supports Lock / Unlock operations (administrative state) of a Shared O-RU administrative state management and these are used throughout the sub-use cases. State management of administrative, operation and availability state are specified in Recommendation ITU-T X.731 [18] and in 3GPP TS 28.624 [9], in 3GPP TS 28.625 [10] (for all classes in the NRM), and in 3GPP TS 28.626 [11]. Some of the sub-use cases can use them, some cannot. Role-based admission control. The Host can change administrative state of the O-RU while the tenant cannot. The Shared O-RU sub-use cases shall be as specified in IETF RFC 8348 [19] which is similar to what is specified in Recommendation ITU-T X.731 [18]. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.2 Resource portioning use case of Shared O-RU | This sub-use case describes the procedures of how to decide on the partitioning of a Shared O-RU. The actors are the sharing co-ordinator, SMO, and the resource partitioning rApp (Shared O-RU). The sharing co-ordinator recovers the inventory from the SMO and decides on how to partition the resources of a Shared O-RU. The sharing co-ordinator uses the rApp to partition the resources of a Shared O-RU between multiple O-DUs. The outcome is that rApp has details on how a Shared O-RU's resource are to be partitioned. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.3 Start-up use case of a Shared O-RU | This sub-use case describes the start-up of a Shared O-RU. The actors are the SMO, the O-DUs, the Shared O-RU, and their interactions that are needed for a Shared O-RU to boot up and enter into operation. The outcome is that the Shared O-RU is operating with the necessary software version and has established network connectivity with the O-DUs and, for hybrid deployments, the SMO. The start-up for a Shared O-RU is the basis of the other Shared O-RU sub-use cases. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.4 Configuration use case of a Shared O-RU | This sub-use case describes the configuration of a Shared O-RU. The configuration is invoked after the start-up use case has completed. Configuration can occur with different actors in hybrid vs hierarchical management mode. The common aspects of the Shared O-RU configuration are configured by the O-DU when in hierarchical management mode and by the SMO in hybrid management mode. The common aspects include the security, operational, transmission, and connectivity related parameters. The O-DUs are always responsible for configuring the partitioned carrier information on the Shared O-RU through the open front-haul interface. The use case enables the SMO to be notified of the configured carrier parameters. This sub-use case includes the configuration of multi-operator role-based access control (configuration) for the management sessions associated with a tenant operator. The Shared O-RU uses the PLMN-Id associated with the management account and used in other aspects of the Shared O-RU's configuration to prevent a tenant from reading configuration associated with a second tenant's partitioned resources or subscribing to performance measurements associated with a second tenant's partitioned resources. This sub-use case also describes the procedures of how a sharing co-ordinator can confirm that a tenant operator is complying with the sharing agreements that cover operation of a Shared O-RU. The outcome is that the Shared O-RU has been configured with the configuration parameters necessary for operation. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.5 Supervision use case of a Shared O-RU | This sub-use case applies to the running / operation of Shared O-RU. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 97 This sub-use case describes the supervision operations of the Shared O-RU. This sub-use case is triggered after the O-DUs have configured the partitioned carrier information of a Shared O-RU. It is invoked during run-time. The actors are the SMO, O-DUs, and Shared O-RU. The objective that the use case accomplishes is the establishment of watchdog supervision of the Shared O-RU by multiple O-DUs. Supervision enables the Shared O-RU to autonomously cease transmitting on a partitioned carrier if it loses supervision with the O-DU responsible for that carrier. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.6 Performance Management use case of a Shared O-RU | This sub-use case applies to the running / operation of Shared O-RU. This sub-use case describes the performance management operations of the Shared O-RU. This sub-use case is triggered after the O-DUs have configured the partitioned carrier information of a Shared O-RU. It is invoked during run-time. The actors are the O-DUs, and Shared O-RU. The objective that the use case accomplishes is for each O-DU to establish subscriptions to receive performance management notifications regarding operation of the fronthaul between the O-DUs and Shared O-RU. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.7 Antenna Line Device (ALD) control use case of a Shared O-RU | This sub-use case applies to the running / operation of Shared O-RU. This sub-use case describes the operation of antenna line devices with the Shared O-RU. This sub-use case is triggered after the O-DUs have configured the partitioned carrier information of a Shared O-RU. The actors are one of the O- DUs, and Shared O-RU. The objective that the use case accomplishes is for the selected O-DU to operate the ALD controller and control the ALD connected to the Shared O-RU. ALD control is an end-to-end operation from the operator using the SMO at one end issuing operations to the O-DU and realising that ALD operation through the O-RU on through to the terminating ALD device at the other end. Because there is a single ALD controller, and the Shared O-RU can be connected to multiple O-DUs, one of them needs to be nominated as the ALD controller. There are many types of operations for ALD control. These include but not limited to software update of ALD devices, setting of RET mechanical/electrical tilt setting, Reset of ALD devices, etc. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.8 Basic Resiliency use case (Primary O-DU failure) for Single MNO | This sub-use case describes system recovery from a failure (Operational State = Disabled) of the Primary O-DU#1 for a Shared O-RU for Single MNO situation. A switch of the Primary O-DU to Secondary O-DU is done. This sub-use case only applies to the hierarchical management of the O-RU. The advanced resiliency sub-use case(s) will cover corner cases for a O-DU(s) connected to a Shared O-RU that is undergoing maintenance, or different elements (O-DU, or Shared O-RU) that have partially (e.g. Availability Status = Degraded) or fully failed (Operational State = Disabled). The actors are the SMO, O-DUs and Shared O-RU. The actors work together to recover operation or maintain operation for failures of O-DUs. This basic resiliency sub-use case, covers failure of the Primary O-DU#1 (Operational State = Disabled) which is unable to provide service. For example, if the Primary O-DU fails completely, this use case covers a flow of operations that occurs in this situation. This use case is triggered when the Primary O-DU#1 fails (Operational State = Disabled). This Use Case describes the switch-over of the other O-DU#2 to become the new Primary O-DU. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.9 Antenna calibration use case of a Shared O-RU (deferred) | This sub-use case applies to the running / operation of Shared O-RU. This sub-use case describes the operation of antenna calibration using a Shared O-RU. This sub-use case is triggered after the O-DUs have configured the partitioned carrier information of a Shared O-RU. The actors are the O-DUs, and Shared O-RU. The objective that the use case accomplishes is for the Shared O-RU to be able to perform antenna calibration when connected to multiple O-DUs. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 98 |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.10 Rehoming use case of a Shared O-RU (deferred) | This sub-use case describes how a Shared O-RU is moved within a network and paired with new O-DUs. This might be a virtual rehoming in a cloud native deployment, or a physical move. This is typically done with replanning of a network, or rolling a new of an existing deploying (greenfield pocket in a brownfield network), moving tiger sites into a network. [MNO] ODU#1 ODU#2 EdgeCloud#20 ORU#700 [MNO] ODU#19 ODU#24 EdgeCloud#900 ORU#700 [MNO] ODU#19/OP#1 ODU#24/OP#2 ODU#85/OP#3 EdgeCloud#900 ORU#700 Radio with two operators, add a third operator or other O-DUs. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.11 Reset use case of a Shared O-RU | The reset of a Shared O-RU sub-use case describes the operations related to taking a Shared O-RU out of service and resetting it. There are some important aspects to reset of Shared O-RU. Only the Shared O-RU Host would have permissions to perform the reset operation. In a multiple MNO configuration, reset operations would need to be coordinated between operators. The Shared O-RU Host operator has the permission to perform the reset of a Shared O-RU. It would expect that the Shared O-RU Host operator would try to coordinate with the Shared Resource Operator (SRO). This can entail something as simple as those two operators talking to each other; it can also entail automated coordination between management entities of these operators. The reset of a Shared O-RU would impact Availability, Reliability, and Maintenance (ARM) metrics, and uptime KPIs. The reset of a Shared O-RU operation is the basis for maintenance activities, debugging operations, the physically moving, physical rehoming, and recovery from malfunctions of the Shared O-RU. There are situations where the Shared O-RU would autonomously reset itself. When the Shared O-RU has lost M-Plane connectivity to all of its connected O-DUs the Shared O-RU would autonomously reset itself. This would be the same as if the O-RU was not in a shared configuration. A software update will result in a reset of the Shared O-RU. The software update of a Shared O-RU is expected to be coordinated between the Shared O-RU Host and the SRO. Before removing the Shared O-RU from service, the Shared O-RU carriers and its associated cells on O-DU/O-CU shall be deactivated. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.12 Advanced Resiliency Sub-use cases of a Shared O-RU | Advanced Resiliency sub-use cases describes other more intricate interactions and response for Resiliency operations. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.13 Load-balancing Sub-use case of a Shared O-RU | Load-balancing is a use case where relevant actors can reallocate Share O-RU resources based on triggers, metrics, or policies. A key actor is a policy-enforcer that makes Shared O-RU resource allocation decisions based on inputs from measurements. For example, measurements can indicate the amount of traffic on each of the two O-DUs connected to the Shared O-RU. The policy-enforcer decides when a reallocation of Shared O-RU resources is needed. For example, if the policy-enforcer observes that one O-DU has a disproportionate amount of traffic (e.g. more users per MHz) than the other O-DU, the policy-enforcer makes a decision to re-allocate a component carrier from one O-DU to the other O-DUs. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 99 The policy-enforcer role could be played by the SMO/Non-RT RIC or the Near-RT RIC, depending on the time granularity of load balancing decision-making needed. There are limitations that are governed from the air interface which will influence the mechanisms and time granularity for load balancing. Policies can reflect a variety of triggers or mechanisms to be used to balance traffic or carriers. These might include guaranteed bandwidth, O-DU computational load/capacity (processor occupancy), and time of day triggers among others. Whatever the policy rules are and how often they are evaluated, the policy-enforcer would evaluate the situation, probably periodically, and make a resource re-allocation decision. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.1.14 Coordinated Reset of a Shared O-RU Sub-use case | The coordinated reset of a Shared O-RU sub-use case describes the operations related to resetting a Shared O-RU when there are multiple Shared Resource Operator (SRO) O-DUs connected to the Shared O-RU. In a multiple MNO configuration, reset operations would need to be coordinated between operators. The active Shared O-RU Host (SOH) operator coordinates the reset of a Shared O-RU. The definition of the active SOH is a SOH that has a state = active. The active SOH can perform a reset on its own volition. The active Shared O-RU Host (SOH) coordinates the reset operation; thus, the SROs can request a reset of the Shared O-RU to be performed by the active SOH. So, the active SOH approves or rejects a reset request of the Shared O-RU coming from other SROs. If the request is accepted by the active SOH, the x would inform all the other SROs x. Then the SOH would perform the reset of the Shared O-RU. If the SOH rejects the operation, then the SOH that originated the SRO that the command was rejected. The SOH can reject a command for example in the middle of a software update or other possible conditions that might prohibit a reset. The reset of a Shared O-RU would impact Availability, Reliability, and Maintenance (ARM) metrics, and uptime KPIs. The reset of a Shared O-RU operation is the basis for maintenance activities, debugging operations, the physically moving, physical rehoming, and recovery from malfunctions of the Shared O-RU. Before removing the Shared O-RU from service, the Shared O-RU carriers and its associated cells on O-DU/O-CU shall be deactivated. The reset of a Shared O-RU is a "hard reset". There can be other variations of coordinated reset of a Shared O-RU possibly based on a policy. 4.20.1.15 Management of Shared O-RU during O-DU Software Update sub-use case for Shared O-RU As part of WG6 Cloudification and Orchestration Use Cases and Requirements for O-RAN, a generic case of software upgrade of Network Function is described. In the practical implementation context, a variety of strategies are incorporated for updating the network function with a new software version, with the objective of minimizing the impact of modifications and mitigating any disruptions to the overall service delivered to end users. These strategies include well-established practices such as Canary update, Rolling update, Blue/Green update etc. The choice of a specific approach relies on various factors such as the extent of the changes, the associated risk potential and the cost of incorporating the change. Moreover, such changes necessitate careful consideration of dependencies and the need to re-provision and optimize resources accordingly. Furthermore, it is crucial to incorporate contingency/mitigation plans in the event that the software update does not align with the intended plan for implementing the changes. This sub-use case focuses on the management of Shared O-RU during the software update of O-DU. It is a critical consideration for Shared O-RU, particularly when O-RU resources are shared between the updated O-DU and existing O-DUs. To address this, well-defined procedures are necessary to identify the specific O-RU that can be shared, determine the particular O-DU with which the O-RU resources can be shared, and optimize the shared resources effectively. Prior to the software update, it can be essential to identify the candidate O-DU and associated O-RU resources that requires evacuation, thereby necessitating the provisioning of target RAN nodes. Moreover, in the event of performance degradation or negative impact on performance indicators following the completion of O-DU software update and activation, it is essential to establish efficient risk mitigation strategies and evaluate possibility of rolling back the introduced changes. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 100 Here the consideration of the Shared O-RU arises from the need to minimize the impact of the software update of O-DU. This is achieved by validating the update with minimal traffic or allocating minimal resources, thereby effectively reducing the footprint of affected end users. Utilizing a Shared O-RU effectively fulfils this objective which otherwise can require deployment of dedicated O-RUs solely for validating the O-DU software update. An additional aspect to be considered is the prevention of Shared O-RU restart following a software update at the O-DU level. This is particularly relevant as a software update on the O-DU can entail the establishment of m-lane connections and initiation of the call home procedure, both of which typically require an O-RU restart to be initiated. A high-level view of the software update scenario of O-DU wherein the Software (SW) updated O-DU shares the O-RU resources with an existing O-DU is shown in figure 4.20.1.15-1. Figure 4.20.1.15-1: Shared O-RU Management Scenario during O-DU Software Update Shared O-RU Management Scenario during O-DU Software update follows a high-level approach as given below: 1) Selection of O-DU Node for software update based on inventory, FM/PM data 2) Deployment and call-home procedures of O-DU 3) O-DU Registration with O-CU, Near-RT RIC 4) Common aspect provisioning on Shared O-RU (e.g. ODU-ID, User account) based on SW updated O-DU 5) Selection of the policy for load sharing (e.g. Load balancing policy, or selection based on cells having low priority user sessions, S-NSSAI, anticipated traffic etc.) with the SW updated O-DU 6) O-RU initiates m-plane connection establishment with SW updated O-DU 7) Translation of the load sharing policy to O-RU configuration 8) Inactivation and evacuation of component carriers selected for allocation to SW updated O-DU 9) Provisioning of O-RU with carrier configuration details by SW updated O-DU (e.g.: end points, list entries) 10) Activation of carrier configuration associated with SW updated O-DU 11) Initiate KPI and functional log monitoring of SW updated O-DU and associated O-RU resources 12) O-DU SW Update Mitigation : In case of degraded KPIs reverse SW update process of O-DU It is to be noted that this subusecase does not differentiate based on the extent of change introduced to the O-DU through the software upgrade because it primarily depends on the implementation context. ETSI ETSI TS 104 036 V12.0.0 (2025-04) 101 This use case does not enforce a specific policy for the SW update as this depends on the particular deployment and implementation choice. Such policies are typically incorporated to define the rules, control logic, constraints and thresholds to be considered during the particular change being initiated. As an example, if an implementation scenario calls for such flexibility, the component carrier shifting could be omitted by utilizing appropriate SW update policy and limit only to the general health check after O-DU SW update. This use case introduces couple of new actors such as O-DU SW Change Management rApp. An O-DU SW Update lifecycle can be managed using these optional functions or using appropriate implementation specific extensions of SMO function. Currently this use case considers only single operator Shared O-RU scenario. The impact of SW update on multi-operator Shared O-RU scenarios is not in scope of the current version of this sub-use case. |
90bcf7b13befe222ebcc419f28dd32b6 | 104 036 | 4.20.2 Entity/resources involved in the use case |
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