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4.7.2.3.1 AI/ML-assisted Non-GoB BF mode selection
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Note that data collection over E2 interface and E2 control/policy procedures shown in table 4.7.2.3.1-2 and in figure 4.7.2.3.1-2 are under the scope of WG3. Note that external interface between the Non-RT RIC and the external sources (e.g. application servers) is not specified by O-RAN. The context of the AI/ML-assisted Non-GoB BF mode selection - model training, deployment, and update is captured in table 4.7.2.3.1-1. Table 4.7.2.3.1-1: AI/ML-assisted Non-GoB BF mode selection - model training, deployment, and update Use Case Stage Evolution / Specification <<Uses>> Related use Goal To train an AI/ML model to select the best Non-GoB BF modes given wireless conditions and per-UE configurations. Actors and Roles SMO, Non-RT RIC, Near-RT RIC, O-DU, external sources, e.g. application server. Assumptions • All relevant functions and components are instantiated. Pre-conditions • O1 interface is established between SMO and O-DU to enable SMO/Non-RT RIC to obtain the number of supported Non-GoB BF modes and to collect performance measurement data and associated per-UE configuration. • A1 interface is established between Non-RT RIC and Near-RT RIC to enable enrichment information transfer. • O-DU supports Non-GoB BF. Begins when Operator specified trigger condition or event is satisfied. Step 1 (M) SMO requests the number of supported Non-GoB BF modes in O-DU via the O1 interface. Step 2 (M) SMO collects the number of supported Non-GoB BF modes in O-DU via the O1 interface. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 60 Use Case Stage Evolution / Specification <<Uses>> Related use Step 3 (M) Non-RT RIC retrieves collected information. Step 4 (M) For each Non-GoB BF mode, SMO requests performance measurement data and associated UE context information (e.g. SRS periodicity) from O-DU for model training via the O1 interface. Step 5 (M) SMO collects required performance measurement data and UE context information (e.g. SRS periodicity) from O-DU via the O1 interface. Step 6 (O) SMO collects enrichment information (e.g. UE mobility and location info) from external sources, e.g. application server. Step 7 (O) Non-RT RIC retrieves collected data and enrichment information. Step 8 (O) For each Non-GoB BF mode, Non-RT RIC associates received enrichment information with measurement data and UE context information. Step 9 (M) Non-RT RIC performs model training. Step 10 (M) Non-RT RIC requests to deploy the trained AI/ML model. Step 11 (M) SMO/Non-RT RIC deploys trained model to the Near-RT RIC via O1 or O2 interface. Step 12 (M) SMO requests performance measurement data, including the active Non- GoB BF mode index, from O-DU for performance monitoring via the O1 interface. Step 13 (M) SMO collects performance measurement data, including the active Non- GoB BF mode index, from O-DU for performance monitoring via the O1 interface. Step 14 (O) SMO collects enrichment information (e.g. UE mobility and location info) from external sources, e.g. application server. Step 15 (O) Non-RT RIC retrieves collected performance monitoring data and enrichment information. Step 16 (M) Non-RT RIC evaluates the performance of deployed AI/ML model. Step 17 (M) Non-RT RIC re-trains the model. Step 18 (M) Non-RT RIC requests to deploy the updated AI/ML model. Step 19 (M) SMO/Non-RT RIC updates model in the Near-RT RIC via O1 or O2 interface. Ends when Operator specified trigger condition or event is satisfied. Exceptions None identified. Post Conditions Near-RT RIC executes the deployed model for AI/ML-assisted Non-GoB BF. Traceability REQ-Non-RT-RIC-FUN1, REQ-Non-RT-RIC-FUN4, REQ-Non-RT-RIC- FUN5, REQ-Non-RT-RIC-FUN9, REQ-A1-FUN2 The flow diagram of the AI/ML-assisted Non-GoB BF mode selection - model training, deployment, and performance monitoring is given in figure 4.7.2.3.1-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 61 Figure 4.7.2.3.1-1: AI/ML-assisted Non-GoB BF mode selection - model training, deployment, and performance monitoring The context of the AI/ML-assisted Non-GoB BF mode selection - model inference is captured in table 4.7.2.3.1-2. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 62 Table 4.7.2.3.1-2: AI/ML-assisted Non-GoB BF mode selection – model inference Use Case Stage Evolution / Specification <<Uses>> Related use Goal To generate Non-GoB control/policy message. Actors and Roles SMO, Non-RT RIC, Near-RT RIC, O-DU, external sources, e.g. application server. Assumptions • All relevant functions and components are instantiated. Pre-conditions • A1 interface is established between Non-RT RIC and Near-RT RIC to enable enrichment information transfer. • E2 interface is established between Near-RT RIC and O-DU to enable Non-GoB BF mode selection via E2 control/policy message. • O-DU supports Non-GoB BF. Begins when Operator specified trigger condition or event is satisfied. Step 1 (O) The Near-RT RIC queries available EI type identifiers. Step 2 (O) The Non-RT RIC responds an array of identifiers of all available EI types. Step 3 (O) The Near-RT RIC queries the EI type to support Non-GoB BF inference (e.g. UE mobility/location info). Step 4 (O) The Non-RT RIC responds detailed information related to the queried EI type. Step 5 (O) The Near-RT RIC creates an EI job. Step 6 (O) The Non-RT RIC responds to the EI job creation request. Step 7 (O) SMO collects enrichment information (e.g. UE mobility/location info) from external sources, e.g. application server. Step 8 (O) Non-RT RIC retrieves collected enrichment information. Step 9 (O) Non-RT RIC delivers collected enrichment information as EI job results to the Near-RT RIC via the A1 interface. Step 9 (M) Near-RT RIC requests measurement data and UE context information (e.g. SRS periodicity) from O-DU via the E2 interface. Step 9 (M) Near-RT RIC collects measurement data and UE context information (e.g. SRS periodicity) from O-DU via the E2 interface. Step 9 (M) Near-RT RIC associates received enrichment information with collected measurement data and UE context information. Step 9 (M) Near-RT RIC selects the best Non-GoB BF mode, e.g. by performing model inference. Step 9 (M) Near-RT RIC generates Non-GoB control/policy message based on inferred Non-GoB BF mode selection. Step 9 (M) Near-RT RIC sends Non-GoB control/policy message to O-DU via the E2 interface. Ends when Operator specified trigger condition or event is satisfied. Exceptions None identified. Post Conditions Non-RT RIC monitors the performance of AI/ML-assisted Non-GoB BF mode selection in the Near-RT RIC. Traceability REQ-Non-RT-RIC-FUN9, REQ-A1-FUN3 The flow diagram of the AI/ML-assisted Non-GoB BF mode selection - inference is given in figure 4.7.2.3.1-2. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 63 Figure 4.7.2.3.1-2: AI/ML-assisted Non-GoB BF mode selection - inference
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4.7.2.4 Required data
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The specification of the data communicated over O1 is outside the scope of WG2. The following enrichment information from external sources (e.g. application server) are used in model training and inference: • UE location • UE mobility • Time granularity of the enrichment information reports (e.g. integer multiple of a second) Note that for model inference, above EI is sent from Non-RT RIC to Near-RT RIC via the A1 interface.
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4.7.2.5 A1 enrichment information example
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In training phase, the retrieved enrichment information (e.g. UE mobility and location information) needs to be associated with collected per-UE L1/L2 measurement reporting (e.g. L1-RSRP and/or L1-SINR, etc.) and UE context information (e.g. UE-specific SRS periodicity) by the Non-RT RIC. In the inference phase, such data association is performed by the Near-RT RIC. Therefore, the EI delivered over the A1 interface should contain necessary UE identification to facilitate the data association at the Near-RT RIC. The Near-RT RIC shall be able to recognize the UE identification and be able to map it to the UE identification used over the E2 interface. For example, the A1 enrichment information contains the following information elements: • UE identifier • Position of the UE • Height of the UE ETSI ETSI TS 104 226 V10.1.0 (2025-08) 64 • Time stamp when the position and height was recorded 4.7.3 MIMO optimization via MIMO DL Tx power optimization, MU-MIMO pairing, and MIMO mode selection
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4.7.3.0 Introduction
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This use case will provide the objective, solutions, and data requirements related to MIMO optimization based on three key sub-features involving downlink transmit power, MIMO pairing enhancement (user separability), and user MIMO mode selection (MU-MIMO or SU-MIMO) that are described in detail in the O-RAN.WG1.MMIMO-USE-CASES- TR-v00.13 [i.3]. The use-case leverages Non-RT RIC to train and host the relevant models and applications that rely on O1 interface services to intelligently optimize MIMO capacity and user experience.
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4.7.3.1 Background and goal of the use case
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4.7.3.1.1 MIMO downlink transmit power optimization
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For general downlink precoding, the downlink transmit power is usually evenly distributed across the UEs. However, depending on the UE separability and path loss deltas, this can result in good cell capacity at the expense of individual UE quality. This can be due to several issues such as cell edge UEs having general downlink SINR issues (even without MU-MIMO), poor UE separability between cell edge UEs, and poor uplink SINR resulting in degraded SRS which are a few example issues. The result of these issues can be manifested by observations of very poor individual UE SINRs (either downlink, uplink, or both) when running in a MU-MIMO mode. Therefore, although the capacity of the cell has been significantly increased, certain customer experiences can become unacceptable in this MU-MIMO mode. The solution to the problem described above is to simply provide observations of UE performance in the form of periodic histograms of UE channel quality as well as the overall cell capacity in order to compute an optimal solution via AI/ML with control of the downlink minimum required SINR threshold to achieve a minimal UE quality requirement that is set by the operator. The minimum required SINR is a threshold recommendation and thus does not require real time AI/ML adjustment of transmit power directly but rather leaves this to the scheduler to adjust and optimize consistent with its numerous other priorities and requirements. The value of this observability and adjustability allows the operator to optimize the trade-off between cell capacity and individual user/customer quality which is essential to provide the best customer experience. The trade-off, for example, can reduce a very high cell centre data rate (which would likely be unnoticeable for the user) to allow more power to be allocated to the cell edge user (who is noticing low throughput and large latencies) to improve the cell edge data rate situation.
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4.7.3.1.2 MU-MIMO pairing enhancement (user separability)
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Existing channel orthogonality between multiple users is critical to create user separability and allow for the opportunity to share radio frequency resources simultaneously. Failing this, residual interference will be too high to maintain adequate post pairing radio link signal quality levels required to sustain MU-MIMO mode assignments. With mobility there is an added demand to adjust beamforming weight assignments to not only maintain signal power levels at the user end (beam quality), but also to continuously limit the inter user interference experienced between users assigned with the same radio resource allocations. If these challenges are left unaddressed, a 5G massive MIMO deployment will fail to utilize the full capability of large antenna arrays powered by transceivers designed to transmit data channel signals towards a spatially confined direction. Further, the network will also fail to realize potential multiplexing gains as fewer radio resource blocks are shared between users within the same cell, reducing spectral efficiency. Another important aspect is the need to efficiently identify users with low demand for radio resources - sources of bursty traffic. An intelligent assessment of how best such users can be effectively paired, if at all, with other users, needs to be pre-determined by the RIC. In summary, this use case suggests various measurement objects that are recommended as input into the AI/ML analytics Apps to optimally determine the outputs required to optimize the MU-MIMO feature operation. The AI/ML assisted modelling and training output, along with the Non-RT RIC based enhancement/inference, will strive to deliver end goal solution selections and system configuration options that upon adoption within the respective domains where they reside, realize an optimization framework that maximizes the potential of a MU-MIMO feature. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 65 Capacity augmentation will be realized by successfully assigning MU-MIMO layers to a greater number of users simultaneously, more often, and more uniformly across the serving area of each gNB.
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4.7.3.1.3 MIMO mode selection optimization (MU-MIMO vs SU-MIMO selection)
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A successful MU-MIMO operation involves the realization of as many orthogonal radio frequency channel links between multiple spatially separated users as possibly as supported by the implementation software at the digital domain. Key to such realization is the successful beamforming weight determination that enables not only the phase addition of multipath signals at the user receiver, but also the choice of precoding algorithms which limit the residual interference between the paired users. It can make sense for the scheduler to prioritize the assignment of radio resources to a MU-MIMO mode of operation during periods of congestion or when high latency requiring applications are supported (to free up other resources that can be assigned sooner). However, doing so at the expense of undesirably lower spectral efficiency on these assigned radio resources will reduce overall sector throughput levels and create poor user experience. It is important to find a means through the AI/ML agent to distinguish users and identify sectors where optimal operation means a greater assignment of SU-MIMO modes independently to users, especially those requiring higher throughput, using devices that are capable of supporting higher layer SU-MIMO count, and operating in an environment that sustains a greater channel rank. With increased loading massive MIMO systems will incur rising levels of interference on the uplink from connected users and on the downlink from the gNB. In addition to normal SINR measurements, the diagnosis of interference from all spatial directions uniformly (white spatial noise) versus specific directions (spatially correlated noise) will be of interest and will require MIMO modes (SU-MIMO vs MU-MIMO) to be properly selected for assignment on a user basis. Such implementation will optimize the per user and per cell throughputs, taking into consideration channel orthogonality conditions rank realizable, and per user effective bandwidth requirement.
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4.7.3.2 Entities/resources involved in the use case
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1) SMO/Non-RT RIC: a) Retrieve relevant performance measurement data and RAN configurations from O-DU via the O1 interface. b) Perform model training and model deployment based on identified measurement data. c) Perform model performance monitoring and model re-training as required. d) Provide RAN configuration recommendations based on identified parameters to O-DU over O1 interface. e) Allow rApps to access the measurement data and to provide configuration recommendations via relevant R1 interface services. 2) O-DU: a) Send measurement data and RAN configurations to SMO/Non-RT RIC via the O1 interface. b) Support implementation of MIMO configuration parameters received from the SMO/Non-RT RIC via the O1 interface.
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4.7.3.3 Solutions
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4.7.3.3.1 MIMO optimization via DL SINR threshold, MU-MIMO pairing, and MIMO mode selection The context of the MIMO optimization via DL Tx power, pairing enhancement, and mode selection is captured in table 4.7.3.3.1-1. Table 4.7.3.3.1-1: MIMO optimization via DL Tx power, pairing enhancement, and mode selection Use Case Stage Evolution / Specification <<Uses>> Related use Goal To train and deploy AI/ML models for MIMO optimization that given wireless conditions and RAN configuration information as input will generate configuration recommendations for DL SINR threshold, MU-MIMO user pairing, and MIMO mode selection. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 66 Use Case Stage Evolution / Specification <<Uses>> Related use Actors and Roles SMO, Non-RT RIC, O-DU Assumptions • All relevant functions and components are instantiated. • O1 interface connectivity is established. Pre-conditions • O1 interface is established between SMO and O-DU to enable SMO/Non-RT RIC to collect performance measurement data and associated RAN configurations. • O-DU supports the implementation of identified configuration parameters when configuration recommendation is received via O1 interface. Begins when Operator specified trigger condition or event is satisfied. Step 1 (M) SMO requests performance measurement data and associated RAN configurations from O-DU for model training via the O1 interface. Step 2 (M) SMO collects required performance measurement data and RAN configurations from O-DU via the O1 interface. Step 3 (M) Non-RT RIC FW retrieves collected information. Step 4 (O) Non-RT RIC performs model training/update. Step 5 (O) Non-RT RIC deploys trained model for inference. Step 6 (M) SMO requests performance measurement data from O-DU for performance monitoring via the O1 interface for rApp execution and optionally model inference. Step 7 (M) SMO collects performance measurement data from O-DU for performance monitoring via the O1 interface for rApp execution and optionally model inference. Step 8 (M) Non-RT RIC FW retrieves the collected data. Step 9 (M) rApp accesses the collected data via R1 interface services. Step 10 (M) rApp performance monitoring and evaluation and optional model inference. Step 11 (M) rApp generates configuration recommendation. Step 12 (M) Non-RT RIC FW retrieves the configuration recommendation via R1 interface services. Step 13 (M) Non-RT RIC provides configuration output to SMO O1 termination. Step 14 (M) SMO communicates MIMO configuration recommendation to O-DU via O1 interface. Ends when Operator specified trigger condition or event is satisfied. Exceptions None identified. Post Conditions O-DU implements the configuration recommendations provided by MIMO optimization App. Traceability REQ-Non-RT-RIC-FUN1, REQ-Non-RT-RIC-FUN2, REQ-Non-RT-RIC- FUN5, REQ-Non-RT-RIC-FUN8 REQ-R1-FUN9 The call flow for MIMO optimization use case is given in figure 4.7.3.3.1-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 67 Figure 4.7.3.3.1-1: Call flow for MIMO optimization use case
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4.7.3.4 Required data
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The specification of the data communicated over O1 is outside the scope of WG2. There are no data that are relevant for the A1 interface.
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4.7.4 AI/ML-based initial access (SS Burst Set) configuration optimization
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4.7.4.1 Background and goal of the use case
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3GPP NR based wireless cellular networks promises to provide leaner system design compared to its predecessors in a bid to improve spectral efficiency, power consumption performance and reduce interferences. Ultra-lean design aims to minimize "always on" reference signal transmissions in the downlink. Synchronization Signals Burst (SSB) sets are one of the high-overhead "always on" reference signals. In large scale NR networks with thousands of gNB/Transmission- Reception Points (TRPs) deployed, system configurations derived statically/manually aiming to accommodate worst case scenario which may arise only for a small window of time can impact on the followings: 1) Increased overhead i.e. degraded utilization of time-frequency resources affecting Spectral Efficiency (SE). 2) Increased interferences among the cells. 3) Increased power consumptions in both network and UEs leading increased network CAPEX and reduced UE battery life respectively. In this context, this sub-use case proposes an AI/ML assisted optimization framework wherein AI/ML agent/engine running at Non-RT RIC can infer optimal SSB set configuration (i.e. number of SS blocks, SS beam directions and SS burst periodicity) based on Configuration (CM) parameters, Performance Measurements (PM), Key Performance Indicators (KPI), and measurement report traces obtained from the E2 nodes (O-CU-CP, O-CU-UP, O-DU, O-eNB) and O-RU. At high-level, the goal of the optimization problem is to minimize SS signal transmissions overhead i.e. determine the minimum number of SSB beams required, their directions and periodicity subject to KPI (integrity, mobility, etc.) targets. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 68 The overall scheme can be summarized as follows. Based on the history/trace/distribution of UE-specific beams (e.g. P2 beams), the AI/ML engine determines the minimum number of SSB beams, their directions and the maximum periodicity of the SS burst required to achieve KPI targets as per history/trace/distribution of UE-specific beams. Furthermore, in order to handle the lower probability occurrences in the statistical models e.g. UEs appearing in a completely new direction that has not been considered in the training data, the AI/ML engine (if required) updates the optimal set (e.g. adds beam directions that compliments the optimal directions, updates the SS burst periodicity, etc.). Finally, the AI/ML engine shares the optimized SSB set configuration with gNB.
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4.7.4.2 Entities/resources involved in the use case
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1) SMO & Non-RT RIC framework: a) Collect the necessary Configuration (CM) parameters, Performance Measurements (PM), Key Performance Indicators (KPI), and measurement report traces from the E2 nodes (O-CU-CP, O-CU-UP, O-DU, O-eNB) and O-RU. b) Allow the rApp to receive the CMs, PMs, KPIs measurement data (collected via O1) to perform the AI/ML model training and provide inference on the SSB set configuration parameters (number of SS blocks, SS beam directions and SS burst periodicity). c) Write/update the optimized SSB set configuration via O1 (to O-DU) interface. 2) rApps: a) Retrieve the necessary Configuration (CM) parameters, Performance Measurements (PM), Key Performance Indicators (KPI), and measurement report traces pertaining to the E2 nodes and O-RU from the SMO/Non-RT RIC framework via R1 for the purpose of optimizing SSB set configuration. b) Train AI/ML model to optimize the SSB set configurations. c) Modify/update the SSB set configurations, optimized by the inference engine of the rApp, and write the configuration output to the SMO/Non-RT RIC framework via R1. d) Monitor the performance of the respective cells. Upon KPI degradation, initiate rollback to the previous version of the AI/ML model, if any, and/or trigger the AI/ML model retraining. e) Execute the inference/control loop periodically or on an event-triggered based. 3) E2 nodes and O-RU: a) Report the desired performance measurements and KPIs, configuration parameters and CM changes, trace reports and measurements to the SMO via O1. b) Receive the optimized SSB set configurations from the SMO via O1 and apply the configuration on the O-DU which may further exercise the configuration update on the O-RU. NOTE: Both aggregated and disaggregated gNB architectures are supported.
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4.7.4.3 Solutions
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The context of the creation and deployment of mMIMO SSB set optimization applications is captured in table 4.7.4.3-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 69 Table 4.7.4.3-1: Creation and deployment of mMIMO SSB set optimization applications Use Case Stage Evolution / Specification <<Uses>> Related use Goal SSB set optimization. Actors and Roles SMO/Non-RT RIC framework, SS burst set optimization rApp, E2 nodes, O-RU. Assumptions All relevant functions and components are instantiated. O1 interface connectivity is established. Pre-conditions SMO/Non-RT RIC framework is instantiated. O1 interface is established between SMO and E2 nodes. Begins when SSB optimization rApp with initial ML model is deployed. Step 1-3(M) SMO/Non-RT RIC framework collects the necessary configurations, performance indicators, and measurement reports from E2 nodes. Step 4-5 (M) rApp retrieves the necessary configurations, performance indicators, and measurement data from SMO/Non-RT RIC framework via R1 and trains AI/ML model for the purpose of optimizing SSB set configuration. Step 6 (M) SMO/Non-RT RIC framework collects observation data from E2 nodes. Step 7-9 (M) rApp retrieves the observation data from SMO/Non-RT RIC framework via R1, infers SSB set configuration, and shares the configuration to SMO/Non-RT RIC framework. Step 10-11 (M) SMO/Non-RT RIC framework writes/updates SSB set configuration at E2 nodes. Step 12-18(M) rApp continuously monitors KPIs in respective cells. In case of unsatisfactory performance, it initiates fallback and retrains/updates the respective AI/ML model(s). Ends when On operator request for rApp to be disabled. Exceptions None identified. Post Conditions SSB set configuration is active. Traceability REQ-Non-RT-RIC-FUN1, REQ-Non-RT-RIC-FUN2, REQ-Non-RT-RIC- FUN5, REQ-Non-RT-RIC-FUN6 The flow diagram of SSB set optimization is given in figure 4.7.4.3-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 70 Figure 4.7.4.3-1: Flow diagram of SSB set optimization
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4.7.4.4 Required data
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4.7.4.4.1 Input data
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1) Supported SSB configurations per cell (as specified in 3GPP TS 38.331 [20], clause 6.3.2 and in 3GPP TS 28.541 [4], clause 4.3.5). 2) Key Performance Indicator (KPIs) such as integrity and cell/beam mobility KPIs, etc., for service level assurance (as specified in 3GPP TS 28.552 [5] and in 3GPP TS 28.554 [17]). 3) CSI-RS beam configuration and CSI-RS beam-specific UE measurement reporting from tracing of RRC messages (as specified in 3GPP TS 38.331 [20], clause 5.5.5.2). 4) PMs, such as the distribution of SS-RSRP, SS-RSRQ across of UEs measured per cell, number of RRC connected UEs (mean, max) measured per cell, intra-NRCell SSB beam switch measurement and received random access preambles on a per SSB/per cell basis (as specified in 3GPP TS 28.552 [5], clauses 5.1.1.22, 5.1.1.31, 5.1.1.4, 5.1.1.20 and 5.1.1.21). 5) Radio Link Failure (RLF) tracing across UEs across SSBs per cell (as specified in 3GPP TS 37.320 [19], clause 5.4.1.2, 3GPP TS 32.422 [18], clause 4.3 and in 3GPP TS 38.331 [20], clause 5.3.10). 6) DL/UL throughput/spectral efficiency per cell (as specified in 3GPP TS 28.552 [5], clause 5.1.1.3 and in 3GPP TR 38.913 [i.6], clause 7.13).
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4.7.4.4.2 Output data
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1) Inferred SSB set (number of SS blocks, SS beam directions and SS burst periodicity) configuration per cell (as specified in 3GPP TS 28.541 [4], clauses 4.3.39 and 4.3.40). ETSI ETSI TS 104 226 V10.1.0 (2025-08) 71
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4.8 Use case 8: Network energy saving use cases
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4.8.0 Introduction
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This clause contains the set of energy saving use cases.
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4.8.1 Carrier and cell switch off/on
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4.8.1.1 Background and goal of the use case
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Mobile networks often utilize multiple frequency layers (carriers) to cover the same service area. At low load, i.e. when the expected traffic volume is lower than a fixed threshold, ES can be achieved by switching off one or more carriers or entire cells without impairing the user experience. UEs previously served by the carrier or cell will be offloaded by the E2 node(s) to a new target carrier or cell prior to the switch off. However, the switch off/on decisions are not a trivial task. There are conflicting targets between system performance and energy savings. Other carriers / cells will have to serve the additional traffic and traffic is changing over time. E2 node(s) and O-RU(s) support several techniques effecting energy consumption which might also be load dependent. While energy savings for the switched off carrier/cell is maximized, the overall energy consumption of the network might even increase. Carrier and cell switch off/on control by the Non-RT or Near-RT RIC can consider overall network energy efficiency instead of local optimization. The switch off/on decision can optionally be made by an AI/ML model within the inference host, deployed at the Non-RT RIC to further improve decision making. Among others, the AI/ML models' functionality can include prediction of future traffic, user mobility, and resource usage and can also predict expected energy efficiency enhancements, resource usage, and network performance for different ES optimization states. Also, with addition of per-UE geographical location information such as trace record for immediate MDT measurement (as specified in 3GPP TS 32.423 [11], clause 4.34.1) and trace record for UE location information (as specified in 3GPP TS 32.423 [11], clause 4.34.2) as input data, the increased accuracy for UE location/trajectory prediction could be expected for more efficient solution so that it could prevent switched off cell(s) from being switched on even though meaningful number of UEs generating/receiving traffic do not exist in that cell(s). In that sense these collections could be conditionally activated during some cells being switched off and be deactivated once all cells switched on in terms of UE energy saving. Possible differences among collected types of geographical location information such as between MDT and LMF are expected to be absorbed and exposed to rApp(s) based on R1 data type definition. Before switching off/on carrier(s) or cell(s), there is a possibility the E2 node(s) and O-RU(s) of performing some preparation actions for off switching (e.g. check ongoing emergency calls and warning messages, to enable, disable, modify carrier aggregation and/or dual connectivity, to trigger HO traffic and UEs from cells/carriers to other cells or carriers, informing neighbour nodes via X2/Xn interface, etc.) as well as for on switching (e.g. cell probing, informing neighbour nodes via X2/Xn interface, etc.).This solution proposes a framework that allows the operator to flexibly configure carrier and cell switch off/on parameters in a cell or in a cluster of cells through O1 configuration formulated by rApp towards E2 node(s) and O-RU(s) or A1 policies formulated by rApp towards Near-RT RIC through SMO/Non- RT RIC framework assisted by machine learning (ML) techniques.
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4.8.1.2 Entities/resources involved in the use case
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1) SMO/Non-RT RIC framework function: a) Collect the configurations, performance indicators and measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports) from E2 node(s) and trace records (e.g. per-UE measurement metrics and location information) through O1 Interface, for the purpose of decision making, optionally using training and inference of AI/ML models that assist such EE/ES functions. It is assumed that configurations, performance indicators and measurement reports collected from the O-DU contain the related information for the corresponding O-RU(s). b) Transfer collected data towards rApp. c) Signal updated configurations for EE/ES optimization towards E2 node(s) (O-CU) through O1 Interface. d) (Optionally) Retrain, update, configure EE/ES AI/ML models in Non-RT RIC. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 72 e) Provide A1 policies to Near-RT RIC via A1 interface based on the request from energy saving rApp in the case of A1 policy-based solution. f) Send enrichment information to the Near-RT RIC for calculation of coverage overlap via the A1 interface in the case of A1 policy-based solution. 2) rApps: Energy saving rApp a) Collect the necessary configurations, performance indicators, and measurement reports from SMO/Non-RT RIC framework, for the purpose of training and execution of relevant AI/ML models. b) (Optionally) Retrain, update, configure EE/ES AI/ML model. c) Infer an optimized O1 configuration for EE/ES based on the data collected using R1 interface. d) Infer an optimized A1 policy for EE/ES based on the data collected using R1 interface in the case of A1 policy-based solution. EI producer rApp ( For A1-EI solution) a) Support to produce enrichment information data requested by Near-RT RIC to ascertain overlapping carriers/cells and the coverage of those carriers/cells (e.g. geo location information of carriers/cells , coverage samples mapped geo location, etc.) b) Send enrichment information to the Near-RT RIC through SMO/Non-RT RIC framework functions for calculation of coverage overlap via the A1 interface. 3) E2 nodes: a) Report cell configuration, performance indicators and measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports, trace records such as per-UE measurement metrics and location information) to SMO via O1 interface. b) Report measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports) to Near-RT RIC via E2 interface in the case of A1 policy-based solution. c) Perform actions required for EE/ES optimization: e.g. check ongoing emergency calls and warning messages, perform some preparation actions for off switching (e.g. to enable, disable, modify carrier aggregation and/or dual connectivity, to trigger HO traffic and UEs from cells/carriers to other cells or carriers, informing neighbour nodes via X2/Xn interface, etc.) as well as for on switching (e.g. cell probing, informing neighbour nodes via X2/Xn interface, etc.) and make final decision on switch off/on and notify SMO via O1 interface about performed actions in case of O1 based solution or notify Near-RT RIC via E2 interface about performed actions in case of A1 policy-based solution. 4) O-RU: a) Report EC and EE related information via open FH M-plane interface to O-DU. b) Support actions required to perform EE/ES optimization. Updated carrier configuration (i.e. activation, deactivation or sleep). 5) Near-RT RIC (For A1 policy-based solution): a) Collect measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports) from E2 nodes. b) (Optionally) Receive EE/ES AI/ML model for deployment via O1 or O2 interface. c) Receive EE/ES related policies via A1 interface for consideration during optimization. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 73 d) Analyse the received data from E2 nodes and perform AI/ML model inference to determine EE/ES. Optimization (i.e. if carriers or cells are recommended to be switched off/on) considering the optimization targets/policies. e) Provide policies or required information to E2 node (O-CU) via E2 to trigger actions for EE/ES optimization. f) Receive enrichment information via the A1 interface. g) Associate enrichment information with collected measurements.
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4.8.1.3 Solution
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4.8.1.3.1 O1 interface based carrier and cell switch off/on optimization for energy saving
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In this solution, decision making, potentially including AI/ML model training and inference, is done at the Non-RT RIC. The context of the O1 interface based carrier and cell switch off/on optimization for energy saving is captured in table 4.8.1.3.1-1. Table 4.8.1.3.1-1: O1 interface based carrier and cell switch off/on optimization for energy saving Use Case Stage Evolution / Specification <<Uses>> Related use Goal Enable carrier and cell switch off/on energy saving functions in the network by means of configuration parameter change and actions controlled by Non-RT RIC and allow for AI/ML-based solutions. Actors and Roles SMO/Non-RT RIC framework function: O1 termination. rApp: Carrier and cell switch off/on optimization. E2 node(s), O-RU: Enforces carrier and cell switch off/on optimization configurations. Assumptions O1 interface connectivity is established. Open FH M-plane interface is established between E2 node(s) and O- RU. Network is operational. Non-RT RIC has knowledge about overlapping carriers/cells and the coverage of those carriers/cells (e.g. which carrier/cell is a coverage layer, and which is a capacity layer). Pre-conditions Operator has set the targets for energy saving functions in the Non-RT RIC. Begins when Operator enables the optimization rApp along with initial ML model for carrier and cell switch off/on energy saving functions and E2 node(s) and O-RU(s) become operational. Step 1 (M) rApp requests to collect necessary configurations, performance indicators, and measurement data (e.g. cell load related information and traffic information, EE/EC measurement reports, cell level configurations, per-UE measurement metrics and location information) towards SMO/Non-RT RIC framework function over R1 interface. Step 2 (M) SMO/Non-RT RIC framework function requests data collection towards E2 node(s) and O-RU(s) (via E2 node(s)) over O1 Interface. Step 3 (M) E2 node(s) upon receiving request from SMO/Non-RT RIC framework function requests and collect necessary data form O-RU(s) over open FH M-plane interface. Step 4 (M) E2 node(s) sends the configuration data, configured measurement data to SMO/Non-RT RIC framework function periodically or event based. Step 5 (M) rApp collects the configuration data, collected measurement data for processing. Step 6 (O) AI/ML models can be retrained either on Non-RT RIC framework or on rApp. If Non-RT RIC framework is hosting retraining, then rApp to select AI/ML model and initiate retraining on Non-RT RIC framework. See note 1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 74 Use Case Stage Evolution / Specification <<Uses>> Related use Step 7 (O) Upon receiving retraining request from rApp. Non-RT RIC framework initiates AI/ML model retraining. Step 8 (O) rApp monitors retrained AI/ML model and retrieves retrained AI/ML model. See note 2. Step 9 (O) Upon receiving retrieval request from rApp, Non-RT RIC framework to transfer AI/ML model to rApp. See note 3. Step 10 (O) If AI/ML model retraining is hosted by rApp then AI/ML model to be retrained on rApp itself. Step 11 (O) Once AI/ML model retraining is performed, AI/ML models are deployed and activated for inferencing for which rApp constantly monitors, performance and energy consumption of the E2 node(s) energy consumption of O-RU(s). rApp monitors performance and energy consumption for evaluation of necessary O1 configurations required to perform cell and carrier shutdown. Step 12 (M) rApp generates O1 configurations to prepare and execute cell(s) and carrier(s) off/on SMO/Non-RT RIC framework function. Step 13 (M) SMO/Non-RT RIC framework function instructs E2 node(s) via O1 interface to perform the received request(s) from the rApp. Step 14 (M) O-RU(s) is informed about the updated O-RU(s) configuration via open FH M-plane interface by E2 node. O-RU(s) to notify E2 node(s) once O- RU(s) configuration is implemented. Step 15 (M) E2 node(s) will inform SMO/Non-RT RIC framework function once cell or carrier switch off/on is completed. Step 16 (M) SMO/Non-RT RIC framework inform rApp about completion of cell or carrier switch off/on over R1 interface. Step 17 (M) rApp monitors energy saving objectives and if energy saving objectives are not achieved, it can decide to initiate fallback mechanism for example, reverting changes over O1 interface for carrier and cell switch off/on optimization, and/or AI/ML model update or retraining. Ends when E2 node(s) becomes non-operational or when the operator disables the optimization functions or ML model for energy saving. Exceptions TBD Post Conditions rApp continues monitoring of energy saving function at E2 node(s) and O-RU. E2 node(s) and O-RU(s) operate using the newly deployed parameters/models and state (off/on). NOTE 1: AI/ML procedures mentioned here can be subject to change based on future work on AI/ML workflow in WG2. NOTE 2: AI/ML model retrieval procedure over R1 interface is for illustration purpose, can be subject to change based on future work on AI/ML workflow in WG2. NOTE 3: AI/ML model transfer procedure over R1 interface is for illustration purpose, can be subject to change based on future work on AI/ML workflow in WG2. The flow diagram of O1 interface based carrier and cell switch off/on optimization for energy saving is given in figure 4.8.1.3.1-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 75 Figure 4.8.1.3.1-1: Flow diagram of O1 interface based carrier and cell switch off/on optimization for energy saving
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4.8.1.3.2 A1 policy based carrier and cell switch off/on optimization for energy saving
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In this solution, decision making, potentially using AI/ML model inference, is done at Near-RT RIC. While AI/ML model training might be hosted in Non-RT or Near-RT RIC, the description below is based on AI/ML model training in the Non-RT RIC. The context of the A1 policy based carrier and cell switch off/on optimization for energy saving is captured in table 4.8.1.3.2-1 ETSI ETSI TS 104 226 V10.1.0 (2025-08) 76 Table 4.8.1.3.2-1: A1 policy based carrier and cell switch off/on optimization for energy saving Use Case Stage Evolution / Specification <<Uses>> Related use Goal Enable A1 policy based carrier and cell switch off/on energy saving functions in the network by means of configuration parameter change and actions controlled by Near-RT RIC and allow for AI/ML-based solutions. Actors and Roles SMO/Non-RT RIC framework function: A1 & O1 termination. rApp: Carrier and cell switch off/on optimization. E2 node(s), O-RU: Enforces carrier and cell switch off/on optimization configurations. Near -RT RIC: Energy savings decision making function. Assumptions O1 interface connectivity is established between SMO and E2 nodes. R1 interface connectivity is established. Open FH M-plane interface is established between E2 node(s) and O-RU. E2 interface connectivity is established between E2 node and Near-RT RIC. A1 interface is established between Non-RT RIC and Near-RT RIC. Network is operational. Pre-conditions Operator has set the targets for energy saving functions in the Non-RT RIC. Begins when Operator enables the optimization rApp along with initial ML model for carrier and cell switch off/on energy saving functions and E2 node(s) and O-RU(s) become operational. See note 1. Step 1 (M) rApp requests to collect necessary configurations, performance indicators, and measurement data (e.g. cell load related information and traffic information, EE/EC measurement reports, cell level configurations) towards SMO/Non-RT RIC framework function over R1 interface. Step 2 (M) SMO/Non-RT RIC framework function requests data collection towards E2 node(s) and O-RU(s) (via E2 node(s)) over O1 Interface. Step 3 (M) E2 node(s) upon receiving request from SMO/Non-RT RIC framework function requests and collect necessary data form O-RU(s) over open FH M-plane interface. Step 4 (M) E2 node(s) sends the configuration data, configured measurement data to SMO/Non-RT RIC framework function periodically or event based. Step 5 (M) rApp collects the configuration data, collected measurement data for processing. Step 6 (O) AI/ML models can be retrained either on SMO/Non-RT RIC framework or on rApp. If SMO/Non-RT RIC framework is hosting retraining, then rApp to select AI/ML model and initiate retraining on Non-RT RIC framework. See note 2. Step 7 (O) Upon receiving retraining request from rApp. SMO/Non-RT RIC framework initiates AI/ML model retraining. Step 8 (O) rApp monitors retrained AI/ML model before requesting to deploy towards Near- RT RIC. Step 9 (O) rApp request SMO/Non-RT RIC framework to deploy AI/ML model in Near-RT RIC over R1 Interface. See note 3. Step 10 (O) If AI/ML model retraining is hosted by rApp then AI/ML model to be retrained on rApp itself. Step 11 (O) Once AI/ML model retraining is performed rApp to transfer AI/ML model to Non- RT RIC framework for deployment to Near-RT RIC. Step 12 (O) Upon receiving request to deploy AI/ML model, Non-RT RIC framework to deploy AI/ML model in Near-RT RIC. Step 13 (M) rApp to trigger EE/ES optimization through A1 policy to prepare and execute cell(s) and carrier(s) off/on. Step 14 (M) Non-RT RIC framework receives policy from rApp and forwards it towards Near- RT RIC via A1 Interface. Step 15 (M) Near-RT RIC provides A1 policy response to SMO/Non-RT RIC framework. Step 16 (M) Non-RT RIC framework informs rApp about A1 policy feedback. Step 17 (M) rApp monitors energy saving objectives as per A1 policy. Step 18 (M) Near-RT RIC receives the policy from the Non-RT RIC over the A1 interface and inferences the EE/ES related AI/ML models and converts policy to specific E2 control or policy commands. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 77 Use Case Stage Evolution / Specification <<Uses>> Related use Step 19 (O) The Near-RT RIC creates an EI job to ascertain overlapping carriers/cells and the coverage of those carriers/cells (e.g. geo location information of carriers/cells, coverage samples mapped geo location, etc.). Step 20 (O) SMO/Non-RT RIC framework functions requests EI rApp to deliver EI data as per details mentioned by Near-RT RIC. Step 21 (O) EI rApp delivers requested EI data to SMO/Non-RT RIC framework functions. Step 22 (O) Non-RT RIC delivers collected enrichment information as EI job results to the Near-RT RIC via the A1 interface. Step 23 (M) Near-RT RIC requests data collection from E2 node(s) and O-RU(s) via E2 interface. Step 24 (M) Upon receiving data collection request E2 nodes requests and collect measurement data. Step 25 (M) Near-RT RIC collects measurement data from E2 node(s) and O-RU(s) via E2 interface. Step 26 (M) Inferencing AI/ML model to evaluate & generate E2 control or policies for cell and carrier switch off/on. Step 27 (M) Near-RT RIC generates and sends cell and carrier switch off/on control/policy message based on inferred AI/ML model to E2 nodes and O-RU(s) via E2 interface. Step 28 (M) O-RU(s) node to update configurations to execute cell or carrier switch off/on. Step 29 (M) E2 nodes feedbacks E2 control/policy to Near-RT RIC. Step 30 (M) Near-RT RIC to notify Non-RT RIC if any change in the state of policy. Step 31 (M) SMO/Non-RT RIC framework forwards notification received from Near-RT RIC to rApp. Step 32 (O) If energy saving objectives are not achieved rApp can decide to initiate fallback mechanism for example, updating or deleting A1 policy for carrier and cell switch off/on optimization. Step 33 (O) SMO/Non-RT RIC framework send update or delete A1 policies to Near-RT RIC. Step 34 (O) rApp can update or retrain AI/ML model based on evaluation of energy saving objectives. Step 35 (O) SMO/Non-RT RIC framework send update or retrain AI/ML model to Near-RT RIC. Ends when E2 node(s) becomes non-operational or when the operator disables the rApp or ML model for energy saving. Exceptions N/A. Post Conditions rApp continues monitoring of energy saving function at E2 node(s) and O-RU. E2 node(s) and O-RU(s) operate using the newly deployed parameters/models and state (off/on). NOTE 1: Operator can set policies through rApp or allow AI/ML model in rApp to infer policies. NOTE 2: AI/ML procedures mentioned here can be subject to change based on future work on AI/ML workflow in WG2. NOTE 3: AI/ML model deployment procedure over R1 interface is for illustration purpose, can be subject to change based on future work on AI/ML workflow in WG2. The flow diagram of A1 interface based carrier and cell switch off/on optimization for energy saving is given in figure 4.8.1.3.2-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 78 Figure 4.8.1.3.2-1: Flow diagram of A1 interface based carrier and cell switch off/on optimization for energy saving NOTE: Above mentioned AI/ML procedures are illustration purpose and details are not defined in the present document. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 79
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4.8.1.4 Required data
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4.8.1.4.0 Introduction
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This clause contains the input and output data of model training and inference for energy saving cell and carrier shutdown.
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4.8.1.4.1 Input data
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The measurement input data are used in model training and inference. They can include the following measurements to monitor energy consumption and energy efficiency of E2 node(s) and O-RU(s): a) DL PDCP SDU data volume per interface (data volume in DL delivered from O-CU-UP to O-DU, per PLMN, per QoS level, per slice, per interface (F1-U, Xn-U, X2-U)) (as specified in 3GPP TS 28.552 [5], clause 5.1.3.6.2.3). b) UL PDCP SDU data volume per interface (data volume in UL delivered to O-CU-UP from O-DU, per PLMN, per QoS level, per slice, per interface (F1-U, Xn-U, X2-U)) (as specified in 3GPP TS 28.552 [5], clause 5.1.3.6.2.4). c) RSRQ measurement per SSB per cell (as specified in 3GPP TS 28.552 [5], clause 5.1.1.31). d) RSRP measurement per SSB per cell (as specified in 3GPP TS 28.552 [5], clause 5.1.1.22). e) SINR measurement per SSB per cell (as specified in 3GPP TS 28.552 [5], clause 5.1.1.32). f) Energy consumption (as specified in 3GPP TS 28.552 [5], clause 5.1.1.19.3). g) Power consumed by physical network function & its components (as specified in 3GPP TS 28.552 [5], clause 5.1.1.19.2 and in O-RAN.WG4.MP.0 [10], clauses B.1, B.5). h) Transmit power (as specified in O-RAN.WG4.MP.0 [10], clauses B.1, B.2.1). i) M1 MDT measurement in trace record for immediate MDT measurements (as specified in 3GPP TS 32.423 [11], clause 4.34.1). j) UE location in trace record for UE location information (as specified in 3GPP TS 32.423 [11], clause 4.34.2).
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4.8.1.4.2 Output data
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rApps to deliver energy saving & energy efficiency policies for cell/carrier switch off/on optimization through R1 interface.
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4.8.2 RF channel reconfiguration
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4.8.2.1 Background and goal of the use case
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In mobile networks m-MIMO antennas are used for beamforming techniques to enhance cell capacity and throughput. To achieve beamforming, O-RU(s) need to concentrate the power amplifiers at the radome by combining radiating elements. At low load, i.e. when the expected traffic volume or number of connected users are lower than the configured threshold, ES can be achieved by reducing the power consumption of O-RU(s) by switching off certain Tx/Rx arrays. For example, 32 out of 64 Tx/Rx arrays of an O-RU(s) can be switched off in a digital m-MIMO architecture and correspondingly the number of spatial layers and SSBs can be reduced. The procedure (involvement of respective O-RAN interfaces) of the RF channel reconfiguration depends on the management architecture model (hybrid or hierarchical) and the deployment option. The switch off/on decision can be made by an AI/ML model within the inference host deployed at the Non-RT RIC. Among others the AI/ML models can include prediction of future traffic, user mobility, and resource usage and can also predict expected energy efficiency enhancements, resource usage, and network performance for different ES optimization states. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 80 This solution proposes a framework that allows the operator to flexibly configure RF channel reconfiguration parameters in a cell or in a cluster of cells through O1 configuration formulated by rApp towards E2 node(s) and O-RU(s) through SMO/Non-RT RIC or A1 policies formulated by rApp towards Near-RT RIC through SMO/Non-RT RIC framework assisted by Machine Learning (ML) techniques.
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4.8.2.2 Entities/resources involved in the use case
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1) SMO/Non-RT RIC framework function: a) Collect the configurations, performance indicators and measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports) from E2 node(s), for the purpose of decision making, optionally using training and inference of AI/ML models that assist such EE/ES functions. b) Transfer collected data towards rApp. It is assumed that configurations, performance indicators and measurement reports collected from the O-DU contain the related information for the corresponding O-RU(s). c) Signal updated configurations for EE/ES optimization towards E2 node(s) (O-CU) through O1 interface. d) (Optionally) Retrain, update, configure EE/ES AI/ML models in Non-RT RIC. e) Provide A1 policies to Near-RT RIC via A1 interface based on the request from energy saving rApp in the case of A1 policy-based solution. 2) rApps: a) Collect the necessary configurations, performance indicators, and measurement reports from SMO/Non-RT RIC framework function, for the purpose of training and execution of relevant AI/ML models. b) (Optionally) Retrain, update, configure EE/ES AI/ML model. c) Infer an optimized RF channel configuration for EE/ES based on the data collected using R1 interface. d) Provide optimized A1 policy for EE/ES based on the data collected using R1 interface in the case of A1 policy-based solution. 3) E2 nodes: a) Report cell configuration, performance indicators and measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports) to SMO via O1 interface. b) Report measurement reports to Near-RT RIC via E2 interface in the case of A1 policy-based solution. c) Perform actions required to perform RF channel reconfiguration (i.e. O-RU Tx/Rx array selection, modification of the number of SSB beams, modification of the O-RU antenna transmit power, modification of the number of SU/MU MIMO data layers or spatial streams) as part of EE/ES optimization. 4) O-RU: a) Report EC and EE related information via open FH M-plane interface to O-DU. b) Perform actions required to be performed due to RF channel reconfiguration (i.e. O-RU Tx/Rx array selection, modification of the number of SSB beams, modification of the O-RU antenna transmit power, modification of the number of SU/MU MIMO spatial streams or data layers) as part of EE/ES optimization. 5) Near-RT RIC (For A1 policy-based solution): a) Collect measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports) from E2 nodes. b) (Optionally) Receive EE/ES AI/ML model for deployment via O1 or O2 interface. c) Receive EE/ES related policies via A1 interface for consideration during optimization. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 81 d) Analyse the received data from E2 nodes and perform AI/ML model inference to determine EE/ES. Optimization (i.e. if carriers or cells are recommended to be switched off/on) considering the optimization targets/policies. e) Provide policies or required information to E2 node via E2 to trigger actions for EE/ES optimization.
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4.8.2.3 Solution
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4.8.2.3.1 O1 policy based RF channel reconfiguration optimization for energy saving
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In this solution, decision making, potentially including AI/ML model training and inference, is done at the Non-RT RIC. The context of the O1 interface based RF channel reconfiguration optimization for energy saving is captured in table 4.8.2.3.1-1. Table 4.8.2.3.1-1: O1 interface based RF channel reconfiguration optimization for energy saving Use Case Stage Evolution / Specification <<Uses>> Related use Goal Enable RF channel reconfiguration energy saving functions in the network by means of configuration parameter change and actions controlled by Non-RT RIC and allow for AI/ML-based solutions. Actors and Roles SMO/Non-RT RIC framework function: Termination point for O1 interface. E2 node(s), O-RU(s): Enforces optimized RF channel configuration. rApp: RF channel reconfiguration optimization. Assumptions O1 interface connectivity is established. Open FH M-plane interface is established between E2 node(s) and O-RU. Network is operational. Pre-conditions Operator has set the targets for energy saving functions in the Non-RT RIC. Begins when Operator enables the optimization rApp along with initial ML model for RF channel reconfiguration saving functions and E2 node(s) and O-RU(s) become operational. Step 1 (M) rApp requests to collect necessary configurations, performance indicators, and measurement data towards SMO/Non-RT RIC framework function over R1 interface. Step 2 (M) SMO/Non-RT RIC framework function requests data collection towards E2 node(s) and O-RU(s) (via E2 node(s)) over O1 interface. Step 3 (M) E2 node(s) upon receiving request from SMO/Non-RT RIC framework function requests and collect necessary data from O-RU(s) over open FH M-plane interface. Step 4 (M) E2 node(s) send the configuration data, configured measurement data to SMO/Non-RT RIC framework periodically or event based. Step 5 (M) rApp collects the configuration data, collected measurement data for processing. Step 6 (O) AI/ML models can be retrained either on Non-RT RIC framework or on rApp. If Non-RT RIC framework is hosting retraining, then rApp to select AI/ML model and initiate retraining on Non-RT RIC framework. See note 1. Step 7 (O) Upon receiving retraining request from rApp. Non-RT RIC framework initiates AI/ML model retraining. Step 8 (O) rApp monitors retrained AI/ML model and retrieves retrained AI/ML model. See note 2. Step 9 (O) Upon receiving retrieval request from rApp, Non-RT RIC framework to transfer AI/ML model to rApp. See note 3. Step 10 (O) If AI/ML model retraining is hosted by rApp then AI/ML model to be retrained on rApp itself. Step 11 (O) Once AI/ML model retraining is performed, AI/ML models are deployed and activated for inferencing for which rApp constantly monitors performance and energy consumption to evaluate necessary O1 configurations to perform RF channel reconfiguration. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 82 Use Case Stage Evolution / Specification <<Uses>> Related use Step 12 (M) rApp to request the SMO/Non-RT RIC framework to configure E2 node(s) to prepare and execute RF channel reconfigurations as below: i) O-RU Tx/Rx array selection. ii) Modify the number of SU/MU MIMO spatial streams or data layers. iii) Modify the number of SSB beams. iv) Modify O-RU antenna transmit power. Step 13 (M) SMO/Non-RT RIC framework function to requests to configure E2 node(s) for RF channel reconfiguration through O1 interface. Step 14 (M) O-RU(s) is informed about the updated O-RU configuration via open FH M- plane interface by E2 node(s). O-RU(s) to inform E2 node(s) once O-RU(s) configuration is implemented. Step 15 (M) E2 node(s) will inform SMO/Non-RT RIC framework function once RF channel reconfiguration is completed. Step 16 (M) SMO/Non-RT RIC framework function to inform rApp about completion of RF channel reconfiguration over R1 interface. Step 17 (M) rApp monitors energy saving objectives. If energy saving objectives are not achieved, it can decide to initiate fallback mechanism for example, reverting changes over O1 interface for RF channel reconfigurations, and/or AI/ML model update or retraining. Ends when E2 node(s) becomes non-operational or when the operator disables the optimization functions or ML model for energy saving. Exceptions TBD. Post Conditions rApp continues monitoring of energy saving function at E2 node(s) and O-RU(s). E2 node(s) and O-RU(s) operate using the newly deployed parameters/models and state. NOTE 1: AI/ML procedures mentioned here can be subject to change based on future work on AI/ML workflow in WG2. NOTE 2: AI/ML model retrieval procedure over R1 interface is for illustration purpose, can be subject to change based on future work on AI/ML workflow in WG2. NOTE 3: AI/ML model transfer procedure over R1 interface is for illustration purpose, can be subject to change based on future work on AI/ML workflow in WG2. The flow diagram of O1 interface based RF channel reconfiguration optimization for energy saving is shown in figure 4.8.2.3.1-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 83 Figure 4.8.2.3.1-1: Flow diagram of O1 interface based RF channel reconfiguration optimization for energy saving
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4.8.2.3.2 A1 policy based RF channel reconfiguration optimization for energy saving
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In this solution, decision making, potentially using AI/ML model inference, is done at Near-RT RIC. While AI/ML model training might be hosted in Non-RT or Near-RT RIC, the description below is based on AI/ML model inferencing in the Near-RT RIC. The context of the A1 policy based optimization for energy saving is captured in table 4.8.2.3.2-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 84 Table 4.8.2.3.2-1: A1 policy based optimization for energy saving Use Case Stage Evolution / Specification <<Uses>> Related use Goal Enable A1 policy based RF channel reconfiguration energy saving functions in the network by means of configuration parameter change and actions controlled by Near-RT RIC and allow for AI/ML-based solutions. Actors and Roles SMO/Non-RT RIC framework function: A1 & O1 termination rApp: RF channel reconfiguration optimization E2 node(s), O-RU: Enforces RF channel reconfiguration Near-RT RIC: RF channel reconfiguration energy savings decision making function Assumptions O1 interface connectivity is established between SMO and E2 nodes. R1 interface connectivity is established. Open FH M-plane interface is established between O-DU and O-RU. E2 interface connectivity is established between E2 nodes and Near-RT RIC. A1 interface is established between Non-RT RIC and Near-RT RIC. Network is operational. Pre-conditions Operator has set the targets for energy saving functions in the Non-RT RIC. Begins when Operator enables the optimization rApp along with initial ML model for RF channel reconfiguration energy saving functions and E2 node(s) and O-RU(s) become operational. See note. Step 1 (M) rApp requests for necessary configurations, performance indicators, and measurement data towards SMO/Non-RT RIC framework function over R1 interface. Configurations may contain data related to node capability such as O-RU RF channel configuration/TRx control capability information which rApp needs to know before formulating A1 policies for ASM optimization. Step 2 (M) SMO/Non-RT RIC framework function requests data collection towards E2 node(s) and O-RU(s) (via E2 node(s)) over O1 Interface. Step 3 (M) E2 node(s) upon receiving request from SMO/Non-RT RIC framework function requests and collect necessary data form O-RU(s) over open FH M-plane interface. Step 4 (M) E2 node(s) sends the configuration data, configured measurement data to SMO/Non-RT RIC framework function periodically or event based. Step 5 (M) rApp collects the configuration data, collected measurement data for processing. Step 6 (M) rApp to trigger EE/ES optimization through A1 policy to prepare and execute RF channel configuration/Trx control. Step 7 (M) Non-RT RIC framework receives policy from rApp and forwards it towards Near-RT RIC via A1 interface. Step 8 (M) Near-RT RIC provides A1 policy response to SMO/Non-RT RIC framework. Step 9 (M) Non-RT RIC framework informs rApp about A1 policy feedback. Step 10 (M) rApp monitors energy saving objectives as per A1 policy. Step 11 (M) The Near-RT RIC receives the policy from the Non-RT RIC over the A1 interface. It then interprets the policy to determine the required data collection and E2 control/policies. Step 12 (M) Inferencing AI/ML model to evaluate & generate E2 control or policies for RF channel configuration/Trx Control optimization. Step 13 (M) Near-RT RIC to notify Non-RT RIC if any change in the state of policy. Step 14 (M) SMO/Non-RT RIC framework forwards notification received from Near-RT RIC to rApp. Step 15 (O) If energy saving objectives are not achieved rApp may decide to initiate fallback mechanism for example, updating or deleting A1 policy for RF channel configuration/Trx control optimization. Step 16 (O) SMO/Non-RT RIC framework send update or delete A1 policies to Near-RT RIC. Step 17 (O) rApp can update or retrain AI/ML model based on evaluation of energy saving objectives. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 85 Use Case Stage Evolution / Specification <<Uses>> Related use Step 18 (O) SMO/Non-RT RIC framework send update or retrained AI/ML model to Near-RT RIC. Ends when E2 node(s) becomes non-operational. Exceptions N/A. Post Conditions rApp continues monitoring of energy saving function at E2 node(s) and O-RU. E2 node(s) and O-RU(s) operate using the newly deployed parameters/models and state (off/on). NOTE: Operator can set policies through rApp or allow AI/ML model in rApp to infer policies. The flow diagram of A1 interface based RF channel reconfiguration optimization for energy saving is shown in figure 4.8.2.3.2-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 86 Figure 4.8.2.3.2-1: Flow diagram of A1 interface based RF channel reconfiguration optimization for energy saving
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4.8.2.4 Required data
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4.8.2.4.0 Introduction
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This clause contains the input and output data of model training and inference for energy saving using RF channel reconfiguration. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 87
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4.8.2.4.1 Input data
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The measurement input data are used in model training and inference. They can include the following measurements to monitor energy consumption and energy efficiency of E2 node(s) and O-RU(s): a) DL PDCP SDU data volume per interface (data volume in DL delivered from O-CU-UP to O-DU, per PLMN, per QoS level, per slice, per interface (F1-U, Xn-U, X2-U)) (as specified in 3GPP TS 28.552 [5], clause 5.1.3.6.2.3). b) UL PDCP SDU data volume per interface (data volume in UL delivered to O-CU-UP from O-DU, per PLMN, per QoS level, per slice, per interface (F1-U, Xn-U, X2-U)) (as specified in 3GPP TS 28.552 [5], clause 5.1.3.6.2.4). c) RSRQ measurement per SSB per cell (as specified in 3GPP TS 28.552 [5], clause 5.1.1.31). d) RSRP measurement per SSB per cell (as specified in 3GPP TS 28.552 [5], clause 5.1.1.22). e) SINR measurement per SSB per cell (as specified in 3GPP TS 28.552 [5], clause 5.1.1.32). f) Energy consumption (as specified in 3GPP TS 28.552 [5], clause 5.1.1.19.3). g) Power consumed by physical network function & its components (as specified in 3GPP TS 28.552 [5], clause 5.1.1.19.2 and in O-RAN.WG4.MP.0 [10], clauses B.1, B.5). h) Transmit power (as specified in O-RAN.WG4.MP.0 [10], clauses B.1, B.2.1).
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4.8.2.4.2 Output data
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rApps to deliver energy saving & energy efficiency A1 policies for RF channel reconfiguration optimization through R1 interface.
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4.8.3 Advanced sleep mode
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4.8.3.0 Introduction
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This use case describes a method to achieve intelligent energy saving by optimizing the sleep mode via Non-RT RIC-based guidance.
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4.8.3.1 Background and goal of the use case
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Mobile networks were often designed to provide higher data rates, better coverage, and ubiquitous connectivity. They have to be always available and well-dimensioned in order to ensure the best Quality of Service (QoS) even in peak hours and emergency or mass event scenarios. This may lead to an over-dimensioned and under-utilized network particularly when the traffic demand is low, such is the case during night hours. The energy consumption of a network is composed of two components: • A fixed component, which is mainly due to the system architecture and includes the power consumption of control signals, backhaul infrastructure, and load-independent consumption of baseband processors. • A variable, load-dependent component, which depends on the transported traffic. Over-provisioning of the network as well as low load periods translate into significant, and unnecessary, energy consumption, due to the fixed component. Sleep modes, which consist in shutting down the O-RU for a certain period of time, are an efficient way to handle this component. It consists in a progressive deactivation of the O-RU's components according to the time needed by each of them to shut down then reactivate again. According to this transition time, four levels of sleep modes have been specified in O-RAN. WG4.CUS.0 [12], clause 16. Deeper sleep levels allow more energy saving but induce larger delays for the users who arrive to the network and who need to wait longer for the components to be reactivated. Hence, Non-RT RIC can provide policies in such cases where reactivation time may not be concern or proactively reactivates. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 88 O-RU and E2 nodes (O-CU, O-DU) may implement various sleep modes. The sleep modes could be enabled by SMO/Non-RT RIC through A1 policy. When enabled, the Near-RT RIC selects among the sleep modes considering their capabilities, the actual traffic situation, and the network conditions. Different SM operate at different time scales (e.g. symbol, slot, frame). This solution proposes a framework that allows the operator to flexibly select various sleep modes parameters through A1 policy formulated by rApp towards Near-RT RIC through SMO/Non-RT RIC framework assisted by Machine Learning (ML) techniques.
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4.8.3.2 Entities/resources involved in the use case
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1) SMO/Non-RT RIC framework function: a) Collect the configurations, performance indicators and measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports) from E2 node(s) and trace records (e.g. per-UE measurement metrics and location information) through O1 interface, for the purpose of decision making, optionally using training and inference of AI/ML models that assist such EE/ES functions. b) Transfer collected data towards rApp. It is assumed that configurations, performance indicators and measurement reports collected from the O-DU contain the related information for the corresponding O-RU(s). c) Signal A1 policies for Near-RT RIC for ASM optimization A1 interface. d) (Optionally) Retrain, update, configure AI/ML models in Non-RT RIC. 2) rApps: a) Collect the necessary configurations, performance indicators, and measurement reports from SMO/Non-RT RIC framework function, for the purpose of training and execution of relevant AI/ML models. b) (Optionally) Retrain, update, configure EE/ES AI/ML model. c) Infer an optimized A1 policy for ASM based on the data collected using R1 interface. 3) E2 nodes: a) Report cell configuration, performance indicators and measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports, Trace records such as per-UE measurement metrics and location information) to SMO via O1 interface. b) Report measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports) to Near-RT RIC via E2 interface. c) Support actions required to perform ASM and Trx control for EE/ES optimization. 4) O-RU: a) Report EC and EE related information via open FH M-plane interface to O-DU . b) Support actions required to perform ASM and Trx control for EE/ES optimization. 5) Near-RT RIC (For A1 policy based solution): a) Collect measurement reports (e.g. cell load related information and traffic information, EE/EC measurement reports) from E2 nodes. b) (Optionally) Receive EE/ES AI/ML model for deployment via O1 or O2 interface. c) Receive EE/ES related policies via A1 interface for consideration during optimization. d) Analyse the received data from E2 nodes and perform AI/ML model inference to determine ASM & Trx control EE/ES. Optimization (i.e. ASM to be activated for certain time and cells) considering the optimization targets/policies. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 89 e) Provide policies or required information to E2 node (O-CU) via E2 to trigger actions for EE/ES optimization.
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4.8.3.3 Solution
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4.8.3.3.1 A1 policy based ASM optimization for energy saving
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In this solution, decision making, potentially using AI/ML model inference, is done at Near-RT RIC. While AI/ML model training might be hosted in Non-RT or Near-RT RIC, the description below is based on AI/ML model inferencing in the Near-RT RIC. The context of the A1 policy based ASM optimization for energy saving is captured in table 4.8.3.3.1-1. Table 4.8.3.3.1-1: A1 policy based ASM optimization for energy saving Use Case Stage Evolution / Specification <<Uses>> Related use Goal Enable A1 policy based ASM energy saving functions in the network by means of configuration parameter change and actions controlled by Near-RT RIC and allow for AI/ML-based solutions. Actors and Roles SMO/Non-RT RIC framework function: A1 & O1 termination. rApp: ASM optimization. E2 node(s), O-RU: Enforces ASM E2 controls or policies. Near-RT RIC: ASM energy savings decision making function. Assumptions O1 interface connectivity is established between SMO and E2 nodes. R1 interface connectivity is established. Open FH M-plane interface is established between E2 node(s) and O-RU. E2 interface connectivity is established between E2 node and Near-RT RIC. A1 interface is established between Non-RT RIC and Near-RT RIC. Network is operational. Pre-conditions Operator has set the targets for energy saving functions in the Non-RT RIC. Begins when Operator enables the optimization rApp along with initial ML model for ASM energy saving functions and E2 node(s) and O-RU(s) become operational. See note. Step 1 (M) rApp requests to collect necessary configurations, performance indicators, and measurement data towards SMO/Non-RT RIC framework function over R1 interface. Configurations may contain data related to node capability such as O-RU ASM capability information which rApp needs to know before formulating A1 policies for ASM optimization. Step 2 (M) SMO/Non-RT RIC framework function requests data collection towards E2 node(s) and O-RU(s) (via E2 node(s)) over O1 interface. Step 3 (M) E2 node(s) upon receiving request from SMO/Non-RT RIC framework function requests and collect necessary data form O-RU(s) over open FH M-plane interface. Step 4 (M) E2 node(s) sends the configuration data, configured measurement data to SMO/Non-RT RIC framework function periodically or event based. Step 5 (M) rApp collects the configuration data, collected measurement data for processing. Step 6 (M) rApp to trigger EE/ES optimization through A1 policy to prepare and execute ASM & Trx control. Step 7 (M) Non-RT RIC framework receives policy from rApp and forwards it towards Near- RT RIC via A1 interface. Step 8 (M) Near-RT RIC provides A1 policy response to SMO/Non-RT RIC framework. Step 9 (M) Non-RT RIC framework informs rApp about A1 policy feedback. Step 10 (M) rApp monitors energy saving objectives as per A1 policy. Step 11 (M) The Near-RT RIC receives the policy from the Non-RT RIC over the A1 interface. It then interprets the policy to determine the required data collection and E2 control/policies. Step 12 (M) Inferencing AI/ML model to evaluate & generate E2 control or policies for ASM optimization. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 90 Use Case Stage Evolution / Specification <<Uses>> Related use Step 13 (M) Near-RT RIC to notify Non-RT RIC if any change in the state of policy. Step 14 (M) SMO/Non-RT RIC framework forwards notification received from Near-RT RIC to rApp. Step 15 (O) If energy saving objectives are not achieved rApp may decide to initiate fallback mechanism for example, updating or deleting A1 policy for ASM optimization. Step 16 (O) SMO/Non-RT RIC framework send update or delete A1 policies to Near-RT RIC. Step 17 (O) rApp can update or retrain AI/ML model based on evaluation of energy saving objectives. Step 18 (O) SMO/Non-RT RIC framework send update or retrain AI/ML model to Near-RT RIC. Ends when E2 node(s) becomes non-operational or when the operator disables the rApp or ML model for energy saving. Exceptions N/A. Post Conditions rApp continues monitoring of energy saving function at E2 node(s) and O-RU. E2 node(s) and O-RU(s) operate using the newly deployed parameters/models and state (off/on). NOTE: Operator can set policies through rApp or allow AI/ML model in rApp to infer policies. The flow diagram of A1 interface based ASM optimization for energy saving is shown in figure 4.8.3.3.1-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 91 Figure 4.8.3.3.1-1: Flow diagram of A1 interface based ASM optimization for energy saving
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4.8.3.4 Required data
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4.8.3.4.0 Introduction
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This clause contains the input and output data of model training and inference for energy saving using ASM. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 92
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4.8.3.4.1 Input data
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The measurement input data are used in model training and inference. They can include the following measurements to monitor energy consumption and energy efficiency of E2 node(s) and O-RU(s): a) DL PDCP SDU data volume per interface (data volume in DL delivered per PLMN, per QoS level, per slice, per interface ((from O-CU-UP to O-DU over F1-U, to external O-CU-UP over Xn-U and to external O-eNB over X2-U)) (as specified in 3GPP TS 28.552 [5], clause 5.1.3.6.2.3). b) UL PDCP SDU data volume per interface (data volume in UL delivered per PLMN, per QoS level, per slice, per interface ((from O-DU to O-CU-UP over F1-U, to external O-CU-UP over Xn-U and to external O-eNB over X2-U)) (as specified in 3GPP TS 28.552 [5], clause 5.1.3.6.2.4). c) RSRQ measurement per SSB per cell (as specified in 3GPP TS 28.552 [5], clause 5.1.1.31). d) RSRP measurement per SSB per cell (as specified in 3GPP TS 28.552 [5], clause 5.1.1.22). e) SINR measurement per SSB per cell (as specified in 3GPP TS 28.552 [5], clause 5.1.1.32). f) Energy consumption (as specified in 3GPP TS 28.552 [5], clause 5.1.1.19.3). g) Power consumed by physical network function & its components (as specified in 3GPP TS 28.552 [5], clause 5.1.1.19.2 and in O-RAN.WG4.MP.0 [10], clauses B.1, B.5). h) Transmit power (as specified in O-RAN.WG4.MP.0 [10], clauses B.1, B.2.1). i) M1 MDT measurement in trace record for immediate MDT measurements (as specified in O-RAN. WG4.CUS.0 [12], clause 4.34.1). j) UE location in trace record for UE location information (as specified in O-RAN. WG4.CUS.0 [12], clause 4.34.2). k) O-RU ASM capability information (as specified in O-RAN.WG4.MP.0 [10], clause 20.4).
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4.8.3.4.2 Output data
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rApps to deliver energy saving & energy efficiency policies for ASM optimization through R1 interface.
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4.9 Use case 9: O-Cloud resource optimization use cases
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4.9.0 Introduction
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This clause contains the set of O-Cloud resource optimization use cases.
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4.9.1 Use case: O-Cloud node draining use case
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4.9.1.0 Introduction
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This use case describes the procedure for the SMO/Non-RT RIC to perform draining of specific O-Cloud node [O-Cloud node description based on O-RAN.WG6.O2-GA&P [13] recommendation by rApp through SMO, which can result in relocation of network functions or its components to another O-Cloud node, thereby restoring network function i.e. network healing.
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4.9.1.1 Background and goal of the use case
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When one or more NF(s) is (are) experiencing some performance degradation, and there is possibility that issue could not be fixed, or root cause could not be identified just by analysing O1 (FCAPS) data. There can be a requirement of co- relating O1 and O2 (FCAPS) data optionally with the help of AI/ML, which can result in identification of root cause. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 93 Examples for reasons to perform O-Cloud node draining: • There is possibility of faulty or misconfiguration of underneath O-Cloud node. By co-relating RAN OAM and O2 related data rApp will help in identifying issue in O-Cloud node which is found to be the root cause of NF's performance degradation, then O-Cloud can be drained, and relocation of the NF(s) on another O-Cloud node can be performed. This example explains a scenario where an action can be invoked when NF(s) performance degradation happens due to O-Cloud node(s), which can be resolved by draining the O-Cloud node. NOTE: The O-Cloud node(s) is set to maintenance mode (as specified in O-RAN.WG6.O2-GA&P [13], clause 3.10.2) by default, when this use case is called.
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4.9.1.2 Entities/resources involved in the use case
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1) SMO/Non-RT RIC framework: - To collect necessary performance, configuration, and other data for rApp to define and update policies which guides the SMO for O-Cloud resource management through O2 related functions over O2 interface. - Non-RT RIC framework should support rApp for managing data to and from O2 related functions related to resource management. - Train AI/ML model with data from O1 and O2, which supports rApp to predict possible requirement of O-Cloud node draining. 2) rApps: - Collect the necessary configurations, performance indicators, and measurement reports from SMO/Non-RT RIC framework. - (Optionally) Retrain, update, configure AI/ML model. - Infer an optimized policies/recommendations for O-Cloud node draining based on the data collected using R1 interface. 3) O2 related functions (NFO/FOCOM): - To support discovery and delivery of O-Cloud IMS/DMS FCAPS data. - To support reception of policy/recommendations from rApp and enforcing these policies/ recommendations towards O-Cloud over O2 interface. 4) RAN OAM functions: - Retrieve relevant PM, CM, FM data from E2 nodes via the O1 interface. - Allow rApps to access the PM, CM, FM data over R1 interface. 5) O-Cloud (IMS and DMS): - To support delivery of O-Cloud (IMS/DMS) resource performance, configuration, and other data to O2 related functions. - To provide feedback post completion or non-completion of recommendations to Non-RT RIC through O2 related functions. - To support to relocate network function and drain O-Cloud node. 6) E2 nodes: - Support to send fault and measurement data, RAN configurations to SMO/Non-RT RIC via the O1 interface. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 94
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4.9.1.3 Solutions
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In this solution, decision making, potentially using AI/ML model inference, is done at rApp. While AI/ML model training might be hosted in Non-RT RIC, the description below is based on AI/ML model training in the Non-RT RIC. The context of Non-RT RIC based O-Cloud node draining is captured in table 4.9.1.3-1. Table 4.9.1.3-1: Non-RT RIC based O-Cloud node draining Use Case Stage Evolution / Specification Goal Draining O-Cloud node through O2 related functions. Actors and Roles SMO/Non-RT RIC framework. rApp: O-Cloud policy/recommendations triggering function. O2 related functions (NFO/FOCOM): Orchestration and management related functions. RAN OAM functions: O1 FCAPS functions. O-Cloud IMS and DMS: O-Cloud policy enforcement and resource data reporting. E2 node: To report network function data to SMO/Non-RT RIC. Assumptions • All relevant functions and components are instantiated. • O1 and O2 interface connectivity is established with SMO including RAN OAM functions /O2 related functions. • R1 interface connectivity is established between rApp and Non-RT RIC framework. Pre-conditions • Network is operational. Begins when Operator specified trigger condition or event is detected. Step 1 (M) rApp sent discovery request to SMO/Non-RT RIC framework to discover O2 related services. Step 2 (M) Non-RT RIC framework resolves the request and sent service discovery result to rApp. Step 3 (M) rApp requests O2 related data from O2 related functions (NFO/FOCOM). rApp can request data such as compute utilization, memory usage, availability of network function, performance of API responses from NF deployments, status of AAL logical processing unit, etc. Step 4 (M) Non-RT RIC framework processes the data request and request O2 related function (NFO and FOCOM) to collect O2ims and O2dms related data. Step 5 (M) O2 related function (FOCOM) performs data request and collection from O-Cloud (IMS). Step 6 (M) O2 related function (NFO) performs data request and collection from O-Cloud (DMS). Step 7 (M) SMO/Non-RT RIC framework collect and store O2ims and O2dms related data. Step 8 (M) SMO/Non-RT RIC framework delivers O2 related data towards rApp over R1 Interface. Step 9 (M) rApp requests RAN OAM related data to be collected from E2 nodes such as availability of E2 node, accessibility KPIs, UEs connected, user traffic and alarms reported on interface level, etc., to understand performance of E2 nodes. Step 10 (M) Non-RT RIC framework forwards request to E2 nodes through RAN OAM functions. Step 11 (M) RAN OAM functions request and collect data from E2 nodes. Step 12 (M) SMO/Non-RT RIC framework to collect and store RAN OAM related PM data. Step 13 (M) SMO/Non-RT RIC framework delivers RAN OAM related O1 data towards rApp. Step 14 (O) AI/ML models can be retrained either on Non-RT RIC framework or on rApp. If Non-RT RIC framework is hosting retraining, then rApp selects AI/ML model and initiate retraining on Non-RT RIC framework. See note 1. Step 15 (O) Upon receiving retraining request from rApp. Non-RT RIC framework initiates AI/ML model retraining. Step 16 (O) rApp monitors retrained AI/ML model and retrieves retrained AI/ML model. See note 2. Step 17 (O) If AI/ML model retraining is hosted by rApp then AI/ML model to be retrained on rApp itself. Step 18 (O) Once AI/ML model retraining is performed, AI/ML models are deployed and activated for inferencing for which rApp constantly monitors O1 and O2 related data. Step 19 (M) rApp identifies the requirement of fault recovery or maintenance of O-Cloud node and formulate policy/recommendations to drain, which can relocate network function to another O-Cloud node. rApp can sent policy or recommendations to O2 related function (FOCOM) for O-Cloud node draining. rApp to include the necessary identifiers of the O-Clouds to be drained. See note 3. Step 20 (M) Post receiving policy/recommendations for draining of O-Cloud node, O-Cloud to perform NF relocation (optionally) and drain O-Cloud node as specified in O-RAN.WG6.ORCH-USE-CASES [14], clause 3.12.2, then O2 related function (FOCOM) Informs rApp about NF relocation (optionally) and drain O-Cloud node R1 Interface. Step 21 (M) rApp monitors O1 and O2 PM, FM data from NF for any undesirable behaviour. Ends when If network function/E2 node becomes non-operational or when the operator disables the rApp. Exceptions N/A. Post Conditions rApp continues close loop monitoring of O-Cloud node telemetry. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 95 Use Case Stage Evolution / Specification NOTE 1: AI/ML procedures mentioned here can be subject to change based on future work on AI/ML workflow in WG2. NOTE 2: AI/ML model retrieval procedure over R1 interface is for illustration purpose, can be subject to change based on future work on AI/ML workflow in WG2. NOTE 3: Identifiers mentioned above are not specified in the present document. The workflow for Non-RT RIC based O-Cloud node draining is shown in figure 4.9.1.3-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 96 Figure 4.9.1.3-1: Workflow for Non-RT RIC based O-Cloud node draining ETSI ETSI TS 104 226 V10.1.0 (2025-08) 97
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4.9.1.4 Required data
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4.9.1.4.0 Introduction
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This clause contains the input and output data required for O-Cloud node drain.
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4.9.1.4.1 Input data
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O2 related data (O-Cloud FCAPS data): 1) DMS telemetry data to understand state and health of network functions deployed on O-Cloud node. 2) IMS telemetry data to understand the state and health of O-Cloud nodes. 3) IMS/DMS inventory data to understand configuration of nodes and network functions deployments on O-Cloud nodes. 4) Monitoring data such as compute utilization, memory usage, availability of network function, performance of API responses from NF deployments, status of AAL logical processing unit, etc. RAN OAM related data (E2 node and O-RU/network function data): 1) The measurement counters and KPIs (as defined by 3GPP and will be extended for O-RAN use cases) should be appropriately aggregated by cell, slice, etc. 2) E2 node KPIs such as availability of E2 node, accessibility KPIs, UEs connected, user traffic and alarms reported on interface level, etc. to understand whether E2 nodes behaving as usual.
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4.9.1.4.2 Output data
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O2 related data: 1) rApp to provide policy based guidance or trigger recommendations to drain O-Cloud node towards O2 related function (NFO/ FOCOM).
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5 Requirements
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5.1 Functional requirements
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5.1.1 Non-RT RIC functional requirements
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The Non-RT RIC functional requirements are captured in table 5.1.1-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 98 Table 5.1.1-1: Non-RT RIC functional requirements REQ Description Note REQ-Non-RT-RIC-FUN1 Non-RT RIC shall support data retrieval and analysis; the data can include performance, configuration or other data related to the application (recommended data shown in required data clause for different use cases). REQ-Non-RT-RIC-FUN2 Non-RT RIC shall support relevant AI/ML model training based on the data in [REQ-Non-RT-RIC-FUN1] for non-real-time optimization of configuration parameters in RAN or Near-RT RIC, as applicable for the use case. REQ-Non-RT-RIC-FUN3 Non-RT RIC shall support relevant AI/ML model training based on the data in [REQ-Non-RT-RIC-FUN1] for generating/optimizing policies and intents to guide the behaviour of applications in Near-RT RIC or RAN, as applicable for the use case. REQ-Non-RT-RIC-FUN4 Non-RT RIC shall support training of relevant AI/ML models based on the data in [REQ-Non-RT-RIC-FUN1] to be deployed/updated in Near-RT RIC as required by the applications. REQ-Non-RT-RIC-FUN5 Non-RT RIC shall support performance monitoring and evaluation. REQ-Non-RT-RIC-FUN6 Non-RT RIC shall support a fallback mechanism to prevent drastic degradation/fluctuation of performance, e.g. to restore to the previous policy or configuration. REQ-Non-RT-RIC-FUN7 Non-RT RIC shall be able to produce enrichment information through data analysis. REQ-Non-RT-RIC-FUN8 Non-RT RIC shall be able to request O1 reconfiguration for non-real- time optimization of configuration parameters in E2 nodes and/or Near-RT RIC, as applicable for the use case. REQ-Non-RT-RIC-FUN9 Non-RT RIC shall support retrieval of external information as applicable for the use case.
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5.1.2 A1 interface functional requirements
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The A1 interface functional requirements are captured in table 5.1.2-1. Table 5.1.2-1: A1 interface functional requirements REQ Description Note REQ-A1-FUN1 A1 interface shall support communication of policies from Non-RT RIC to Near-RT RIC. REQ-A1-FUN2 A1 interface shall support AI/ML model deployment and update from Non-RT RIC to Near-RT RIC. REQ-A1-FUN3 A1 interface shall support communication of enrichment information from Non-RT RIC to Near-RT RIC. REQ-A1-FUN4 A1 interface shall support feedback from Near-RT RIC for monitoring AI/ML model performance. REQ-A1-FUN5 A1 interface shall support the policy feedback from Near-RT RIC to Non-RT RIC.
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5.1.3 R1 interface functional requirements
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The R1 interface functional requirements are captured in table 5.1.3-1. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 99 Table 5.1.3-1: R1 interface functional requirements REQ Description Note REQ-R1-FUN1 R1 interface shall support registration of services. Based on REQ- nRTRfW-R1r-10 REQ-R1-FUN2 R1 interface shall support discovery of registered services. Based on REQ- nRTRApp-R1r-30 REQ-R1-FUN3 R1 interface shall support authentication of rApp. Based on REQ- nRTRfW-R1r-10 REQ-R1-FUN4 R1 interface shall support authorization of service request. Based on REQ- nRTRfW-R1r-10 REQ-R1-FUN5 R1 interface shall support subscription and unsubscription of notifications for added/updated/removed registered services. Based on REQ- nRTRfW-R1r-120 REQ-R1-FUN6 R1 interface shall support registration of data types. Based on REQ- nRTRfW-R1r-30 REQ-R1-FUN7 R1 interface shall support subscription of data types. Based on REQ- nRTRfW-R1r-30 REQ-R1-FUN8 R1 interface shall support A1 related services. REQ-R1-FUN9 R1 interface shall support O1 related services. REQ-R1-FUN10 R1 interface shall support O2 related services. REQ-R1-FUN11 R1 interface shall support AI/ML workflow services. REQ-R1-FUN12 R1 interface shall support services related to network slice subnets. Refer to O-RAN.WG1.Slicin g-Architecture [15] for details.
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5.2 Non-functional requirements
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5.2.1 Non-RT RIC non-functional requirements
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The Non-RT RIC non-functional requirements are captured in table 5.2.1-1. Table 5.2.1-1: Non-RT RIC non-functional requirements REQ Description Note REQ-Non-RT-RIC-NON-FUN1 Non-RT RIC shall not update the same policy or configuration parameter for a given Near-RT RIC or RAN function more often than once per second. REQ-Non-RT-RIC-NON-FUN2 Non-RT RIC shall be able to update policies in several Near-RT RICs.
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5.2.2 A1 interface non-functional requirements
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The A1 interface non-functional requirements are captured in table 5.2.2-1. Table 5.2.2-1: A1 interface non-functional requirements REQ Description Note 5.2.3 R1 interface non-functional requirements The R1 interface non-functional requirements are captured in table 5.2.3-1. Table 5.2.3-1: R1 interface non-functional requirements REQ Description Note ETSI ETSI TS 104 226 V10.1.0 (2025-08) 100 Annex A (informative): Change history Date Version Information about changes 2019.05.06 01.00 • First version with initial set of use cases. Describes the set of use cases that have been approved within O-RAN WG2. 2020.03.01 02.00 • Updates to traffic steering use case. • Updates to QoE use case. • Addition of a new use case (QoS based resource optimization). • Addition of a new use case (Context-based dynamic handover management for V2X). • Removal of 3D-MIMO beamforming optimization use case from WG2 and moving it to UCTG as it does not have WG2 specific impact. 2020.11.01 02.01 • Updates to traffic steering use case with multi-access network scenario enhancements. 2021.03.10 03.00 • Addition of a new use case (RAN slice SLA assurance). 2021.07.19 04.00 • Updates to Non-RT RIC related definitions and abbreviations. • Additions of R1 functional requirements. 2021.11.24 05.00 • Addition of NSSI resource optimization use case. • Updates to RAN slice SLA assurance use case. 2022.04.15 06.00 • Load balancing related updates to RAN slice SLA assurance use case. • Addition of a section for multiple massive MIMO optimization use cases. • Addition of three massive MIMO sub-use cases to "Massive MIMO optimization use cases" section; 1) Massive MIMO Grid-of-Beams Beamforming (GoB BF) optimization use case, 2) Massive MIMO Non-GoB Beamforming (Non-GoB BF) optimization use case and 3) MIMO optimization via MIMO DL Tx power optimization, MU-MIMO pairing, and MIMO mode selection use case. 2023.03.24 07.00 • Alignment with latest O-RAN TS template. 2023.07.27 08.00 • Addition of a new use case (O-Cloud resource optimization). • Enhancements to one use case (Energy savings – Carrier switch off/on). • Minor addition to an A1 policy (frequency preference). 2023.11.16 09.00 • Alignment to the latest ODR template. • Addition of NES use case: - MDT/Trace measurement metrics for cell & carrier switched off/on - Advanced sleep mode - Policy-based RF channel reconfiguration 2024.03.30 10.00 • Addition of a massive MIMO sub-use case (initial access). • Updates for O-RAN Drafting Rules (ODR) compliancy. 2024.07.12 10.01 • Editorial changes for O-RAN Drafting Rules (ODR) compliancy. ETSI ETSI TS 104 226 V10.1.0 (2025-08) 101 History Document history V10.1.0 August 2025 Publication
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1 Scope
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This Technical Specification (TS) specifies the technical realization of the handling of calls originated by a GSM mobile subscriber and calls directed to a GSM mobile subscriber, up to the point where the call is established. Normal release of the call after establishment is also specified. The handling of DTMF signalling and Off-Air Call setup (OACSU) are not described in this specification. The details of the effects of GSM supplementary services on the handling of a call are described in the relevant GSM 03.8x and GSM 03.9x series of specifications. The specification of the handling of a request from the HLR for subscriber information is not part of basic call handling, but is required for both CAMEL (GSM 03.78 [5]) and optimal routeing (GSM 03.79 [6]). The use of the Provide Subscriber Information message flow is shown in GSM 03.78 [5] and GSM 03.79 [6]. The specification of the handling of data calls re-routed to a SIWFS is described in GSM 03.54 [4]. The logical separation of the MSC and VLR (shown in clauses 4, 5 & 7), and the messages transferred between them (described in clause 8) are the basis of a model used to define the externally visible behaviour of the MSC/VLR, which is a single physical entity. They do not impose any requirement except the definition of the externally visible behaviour. If there is any conflict between this specification and the corresponding stage 3 specifications (ETS 300 557 [14], ETS 300 590 [16] and ETS 300 599 [17]), the stage 3 specification shall prevail.
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2 Normative references
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The following documents contain provisions which, through reference in this text, constitute provisions of the present document. - References are either specific (identified by date of publication, edition number, version number, etc.) or non-specific. - For a specific reference, subsequent revisions do not apply. - For a non-specific reference, the latest version applies. - A non-specific reference to an ETS shall also be taken to refer to later versions published as an EN with the same number. [1] ETS 300 500 (1994): " Digital cellular telecommunications system (Phase 2); Principles of telecommunication services supported by a GSM Public Land Mobile Network (PLMN) (GSM 02.01)”. [2] ETS 300 523 (1994): " Digital cellular telecommunications system (Phase 2); Numbering, addressing & identification (GSM 03.03)”. [3] ETS 300 534 (1994): " Digital cellular telecommunications system (Phase 2); Security related network functions (GSM 03.20)”. [4] GSM 03.54 (TS 101 252) ”Digital cellular telecommunications system (Phase 2+);Description for the use of a Shared Inter Working Function /SIWF) in a GSM PLMN Stage 2 ”. [5] TS 101 044 (GSM 03.78): "Digital cellular telecommunications system (Phase 2+); Customized Applications for Mobile network Enhanced Logic (CAMEL) - Stage 2. [6] TS 101 045 (GSM 03.79): "Digital cellular telecommunications system (Phase 2+); Support of Optimal Routeing (SOR); Technical Realization". [7] ETS 300 542 (1994): " Digital cellular telecommunications system (Phase 2); Line identification supplementary services - Stage 2 (GSM 03.81)”. ETSI TS 101 043 V5.6.0 (1998-11) 9 GSM 03.18 version 5.6.0 Release 1996 [8] ETS 300 543 (1994): "Digital cellular telecommunications system (Phase 2); Call Forwarding (CF) supplementary services - Stage 2 (GSM 03.82)”. [9] ETS 300 544 (1994): "Digital cellular telecommunications system (Phase 2); Call Waiting (CW) and Call Hold (HOLD) supplementary services - Stage 2 (GSM 03.83)”. [10] ETS 300 545 (1994): "Digital cellular telecommunications system (Phase 2); Multi Party (MPTY) supplementary services - Stage 2 (GSM 03.84)”. [11] ETS 300 546 (1994): "Digital cellular telecommunications system (Phase 2); Closed User Group (CUG) supplementary services - Stage 2 (GSM 03.85)”. [12] ETS 300 547 (1994): "Digital cellular telecommunications system (Phase 2); Advice of Charge (AoC) supplementary services - Stage 2 (GSM 03.86)”. [13] ETS 300 548 (1994): "Digital cellular telecommunications system (Phase 2); Call Barring (CB) supplementary services - Stage 2 (GSM 03.88)”. [14] ETS 300 557 (1995): "Digital cellular telecommunications system (Phase 2); Mobile radio interface layer 3 specification (GSM 04.08)”. [15] ETS 300 582 (1994): "Digital cellular telecommunications system (Phase 2); General on Terminal Adaptation Functions (TAF) for Mobile Stations (MS) (GSM 07.01)”. [16] ETS 300 590 (1995): "Digital cellular telecommunications system (Phase 2); Mobile-services Switching Centre - Base Station System (MSC - BSS) interface Layer 3 specification (GSM 08.08)”. [17] ETS 300 599 Fourth Edition (1997): "Digital cellular telecommunications system (Phase 2); Mobile Application Part (MAP) specification (GSM 09.02)”. [18] ETS 300 604 (1994): "Digital cellular telecommunications system (Phase 2); General requirements on interworking between the Public Land Mobile Network (PLMN) and the Integrated Services Digital Network (ISDN) or Public Switched Telephone Network (PSTN) (GSM 09.07)”. [19] ETS 300 605 (1995): "Digital cellular telecommunications system (Phase 2); Information element mapping between Mobile Station - Base Station System (MS - BSS) and Base Station System - Mobile-services Switching Centre (BSS - MSC) Signalling procedures and the Mobile Application Part (MAP) (GSM 09.10)”. [20] ETS 300 627 (1996): "Digital cellular telecommunications system (Phase 2); Subscriber and equipment trace (GSM 12.08)”. [21] ETS 300 356-1 (1995): "Integrated Services Digital Network (ISDN); Signalling System No. 7; ISDN User Part (ISUP) version 2 for the international interface; Part 1: Basic services”. [22] ITU-T Recommendation Q.850 (1996): "Usage of cause and location in the Digital Subscriber Signalling System No. 1 and the Signalling System No. 7 ISDN User Part”.
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3 Definitions and abbreviations
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3.1 Definitions
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For the purposes of the present document, the following definitions apply: A subscriber: The calling mobile subscriber. B subscriber: The mobile subscriber originally called by the A subscriber. C subscriber: The subscriber to whom the B subscriber has requested that calls be forwarded. The C subscriber may be fixed or mobile. ETSI TS 101 043 V5.6.0 (1998-11) 10 GSM 03.18 version 5.6.0 Release 1996 Location Information: Information to define the whereabouts of the MS, and the age of the information defining the whereabouts.
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3.2 Abbreviations
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For the purposes of the present document, the following abbreviations apply: A&O Active & Operative ACM Address Complete Message ANM ANswer Message AoC Advice of Charge BC Bearer Capability BOIC-exHC&BOIZC Barring of Outgoing International Calls except those directed to the HPLMN Country & Barring of Outgoing InterZonal Calls BOIZC Barring of Outgoing InterZonal Calls BOIZC-exHC Barring of Outgoing InterZonal Calls except those directed to the HPLMN Country CFB Call Forwarding on Busy CFNRc Call Forwarding on mobile subscriber Not Reachable CFNRy Call Forwarding on No Reply CFU Call Forwarding Unconditional CLIP Calling Line Identity Presentation CLIR Calling Line Identity Restriction COLP COnnected Line identity Presentation COLR COnnected Line identity Restriction CUG Closed User Group CW Call Waiting FTN Forwarded-To Number FTNW Forwarded-To NetWork GMSCB Gateway MSC of the B subscriber HLC Higher Layer Compatibility HLRB The HLR of the B subscriber HPLMNB The HPLMN of the B subscriber IAM Initial Address Message IPLMN Interrogating PLMN - the PLMN containing GMSCB IWU Inter Working Unit LLC Lower Layer Compatibility MO Mobile Originated MPTY MultiParTY MT Mobile Terminated NDUB Network Determined User Busy NRCT No Reply Call Timer PRN Provide Roaming Number SIFIC Send Information For Incoming Call SIFOC Send Information For Outgoing Call SIWF Shared Inter Working Function SIWFS SIWF Server. SIWFS is the entity where the used IWU is located. SRI Send Routeing Information UDUB User Determined User Busy VLRA The VLR of the A subscriber VLRB The VLR of the B subscriber VMSCA The Visited MSC of the A subscriber VMSCB The Visited MSC of the B subscriber VPLMNA The Visited PLMN of the A subscriber VPLMNB The Visited PLMN of the B subscriber ETSI TS 101 043 V5.6.0 (1998-11) 11 GSM 03.18 version 5.6.0 Release 1996
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4 Architecture
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Subclauses 4.1 and 4.2 show the architecture for handling a basic MO call and a basic MT call. A basic mobile-to- mobile call is treated as the concatenation of an MO call and an MT call.
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4.1 Architecture for an MO call
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A basic mobile originated call involves signalling between the MS and its VMSC via the BSS, between the VMSC and the VLR and between the VMSC and the destination exchange, as indicated in figure 1. MS VMSCA VLRA VPLMNA Radio I/F signalling SIFOC Complete call IAM (ISUP) BSSA 'A' I/F signalling Figure 1: Architecture for a basic mobile originated call In figure 1 and throughout this specification, the term ISUP is used to denote the telephony signalling system used between exchanges. In a given network, any telephony signalling system may be used. When the user of an MS wishes to originate a call, the MS establishes communication with the network using radio interface signalling, and sends a message containing the address of the called party. VMSCA requests information to handle the outgoing call (SIFOC) from VLRA, over an internal interface of the MSC/VLR. If VLRA determines that the outgoing call is allowed, it responds with a Complete Call. VMSCA: - establishes a traffic channel to the MS; and - constructs an ISUP IAM using the called party address and sends it to the destination exchange. NOTE: When the non-loop method is used for data calls, the IAM is sent to the SIWFS.
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4.2 Architecture for an MT call
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A basic mobile terminated call involves signalling as indicated in figure 2. Communication between VMSCB and the MS is via the BSS, as for the mobile originated case. The IPLMN, containing GMSCB, is in principle distinct from HPLMNB, containing HLRB, but the practice for at least the majority of current GSM networks is that a call to a GSM MS will be routed to a GMSC in HPLMNB. ETSI TS 101 043 V5.6.0 (1998-11) 12 GSM 03.18 version 5.6.0 Release 1996 IPLMN GMSCB VPLMNB HLRB HPLMNB IAM (ISUP) IAM (ISUP) Send Routeing Info/ack Provide Roaming Number/ack Radio I/F signalling MS VLRB VMSCB SIFIC Page/ack Complete call BSSB Figure 2: Architecture for a basic mobile terminated call When GMSCB receives an ISUP IAM, it requests routeing information from HLRB using the MAP protocol. HLRB requests a roaming number from VLRB, also using the MAP protocol, and VLRB returns a roaming number in the Provide Roaming Number Ack. HLRB returns the roaming number to GMSCB in the Send Routeing Info ack. GMSCB uses the roaming number to construct an ISUP IAM, which it sends to VMSCB. When VMSCB receives the IAM, it requests information to handle the incoming call (SIFIC) from VLRB, over an internal interface of the MSC/VLR. If VLRB determines that the incoming call is allowed, it requests VMSCB to page the MS. VMSCB pages the MS using radio interface signalling. When the MS responds, VMSCB informs VLRB in the Page ack message. VLRB instructs VMSCB to connect the call in the Complete call, and VMSCB establishes a traffic channel to the MS.
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5 Information flows
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5.1 Information flow for an MO call
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An example information flow for an MO call is shown in figure 3; many variations are possible. Signalling over the radio interface between MSA and BSSA or VMSCA is shown by dotted lines; signalling over the "A" interface between BSSA and VMSCA is shown by dashed lines; signalling over the B interface between VMSCA and VLRA is shown by chain lines; and ISUP signalling between VMSCA and the destination exchange is shown by solid lines. ETSI TS 101 043 V5.6.0 (1998-11) 13 GSM 03.18 version 5.6.0 Release 1996 Authenticate BSSA VLRA VMSCA MSA CM service req Process access req Authenticate (note 1) Authenticate resp Authenticate ack CM service req Authenticate Authenticate resp Set cipher mode Process access req Start ciphering Cipher mode cmd Cipher mode comp Cipher mode comp Setup SIFOC Complete call Call proceeding Allocate channel Assignment cmd Assignment comp Allocation complete IAM ACM Alert ANM Connect Connect ack (note 2) (note 3) (note 3) ack Figure 3: Information flow for a basic mobile originated call NOTE 1: Authentication may occur at any stage during the establishment of an MO call; its position in this message flow diagram is an example. NOTE 2: Ciphering may be initiated at any stage after authentication; its position in this message flow diagram is an example. NOTE 3: If ciphering is not required, the MSC may send a CM service accept towards the MS; optionally it may instead send a "start ciphering" request indicating that no ciphering is required. NOTE 4: The network may request the IMEI from the MS, and may check the IMEI, at any stage during the establishment of an MO call, either as part of the procedure to start ciphering or explicitly after ciphering has started; this is not shown in this message flow diagram. ETSI TS 101 043 V5.6.0 (1998-11) 14 GSM 03.18 version 5.6.0 Release 1996 When the user wishes to originate a call, MSA establishes a signalling connection with BSSA, and sends a Connection Management (CM) service request to BSSA, which relays it to VMSCA. VMSCA sends a Process access request to VLRA. VLRA may then initiate authentication, as described in ETS 300 534 [3]. VLRA may also initiate ciphering at this stage, as described in ETS 300 534 [3]. If VLRA determines that MSA is allowed service, it sends a Process access request ack to VMSCA. If VMSCA has received a Set cipher mode message from VLRA, the Process access request ack message triggers a Start ciphering command message towards BSSA; otherwise VMSCA sends a CM service accept message towards BSSA. If BSSA receives a Start ciphering command from VMSCA, it initiates ciphering as described in ETS 300 534 [3]; when ciphering is successfully initiated, MSA interprets this in the same way as a CM service accept. If ciphering is not required at this stage, BSSA relays the CM service accept to MSA. When MSA has received the CM service accept, or ciphering has been successfully initiated, MSA sends a Setup message containing the B subscriber address via BSSA to VMSCA. MSA also uses the Setup message to indicate the bearer capability required for the call; VMSCA translates this bearer capability into a GSM basic service, and determines whether an interworking function is required. VMSCA sends to VLRA a request for information to handle the outgoing call, using a Send Info For Outgoing Call (SIFOC) message containing the B subscriber address. If VLRA determines that the call should be connected, it sends a Complete Call message to VMSCA. VMSCA sends a Call Proceeding message via BSSA to MSA, to indicate that the call request has been accepted, and sends an Allocate channel message to BSSA, to trigger BSSA and MSA to set up a traffic channel over the radio interface. The Call Proceeding message includes bearer capability information if any of the negotiable parameters of the bearer capability has to be changed. When the traffic channel assignment process is complete (indicated by the Allocation complete message from BSSA to VMSCA), VMSCA constructs an ISUP IAM using the B subscriber address, and sends it to the destination exchange. When the destination exchange returns an ISUP Address Complete Message (ACM), VMSCA sends an Alert message via BSSA to MSA, to indicate to the calling user that the B subscriber is being alerted. When the destination exchange returns an ISUP ANswer Message (ANM), VMSCA sends a Connect message via BSSA to MSA, to instruct MSA to connect the speech path. The network then waits for the call to be cleared. For an emergency call, a different CM service type (emergency call) is used, and the mobile may identify itself by an IMEI. It is a network operator option whether to allow an emergency call when the mobile identifies itself by an IMEI. Details of the handling are shown in clause 7. 5.2 Information flow for retrieval of routeing information for an MT call The information flow for retrieval of routeing information for an MT call is shown in figure 4. ISUP signalling between the originating exchange and GMSCB, and between GMSCB and VMSCB is shown by solid lines; signalling over the MAP interfaces between GMSCB and HLRB and between HLRB and VLRB is shown by chain lines. IAM GMSC HLRB VLRB VMSCB SRI SRI ack PRN PRN ack IAM Figure 4: Information flow for retrieval of routeing information for a basic mobile terminated call ETSI TS 101 043 V5.6.0 (1998-11) 15 GSM 03.18 version 5.6.0 Release 1996 When GMSCB receives an IAM, it analyses the called party address. If GMSCB can derive an HLR address from the B party address, it sends a request for routeing information (SRI) to HLRB. HLRB sends a request for a roaming number (PRN) to VLRB. VLRB returns the roaming number in the PRN ack, and HLRB relays the roaming number to GMSCB in the SRI ack. GMSCB constructs an IAM using the roaming number, and sends it to VMSCB.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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5.3 Information flow for an MT call
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An example information flow for an MT call is shown in figure 5; many variations are possible. ISUP signalling between GMSCB and VMSCB is shown by solid lines; signalling over the B interface between VMSCB and VLRB is shown by chain lines; signalling over the "A" interface between VMSCB and BSSB is shown by dashed lines; and signalling over the radio interface between VMSCB or BSSB and MSB is shown by dotted lines. ETSI TS 101 043 V5.6.0 (1998-11) 16 GSM 03.18 version 5.6.0 Release 1996 GMSCB VLRB VMSCB BSSB MSB IAM SIFIC Page MS Page Page Chan req Imm ass Page resp MS conn estab Process access req Set cipher mode (note 1) Process access req ack Start ciphering (note 2) Cipher mode command Cipher mode complete Setup Complete call Call conf Allocate Allocation channel complete Assignment Assignment command complete ACM ANM Complete call ack Alerting Connect Connect ack Figure 5: Information flow for a basic mobile terminated call NOTE 1: Ciphering may be initiated at any stage after the network has accepted the page response; its position in this message flow diagram is an example. NOTE 2: If ciphering is not required, the MSC may send a "start ciphering" request indicating that no ciphering is required. NOTE 3: This message flow diagram assumes that the MS has already been authenticated on location registration. If this is not so (for the first MT call after VLR restoration), the network may initiate authentication after the MS responds to paging. NOTE 4: The network may request the IMEI from the MS, and may check the IMEI, at any stage after the MS responds to paging, either as part of the procedure to start ciphering or explicitly after ciphering has started; this is not shown in this message flow diagram. ETSI TS 101 043 V5.6.0 (1998-11) 17 GSM 03.18 version 5.6.0 Release 1996 When VMSCB receives an IAM from GMSCB it sends to VLRB a request for information to handle the incoming call, using a Send Info For Incoming Call (SIFIC) message containing the roaming number received in the IAM. If VLRB recognizes the roaming number, and MSB is allowed service, it sends a request to VMSCB to page MSB. If a radio connection between the network and MSB is already established, VMSCB responds immediately to the page request. If no radio connection exists, VMSCB sends a page request to BSSB, and BSSB broadcasts the page on the paging channel. If MSB detects the page, it sends a channel request to BSSB, which responds with an immediate assignment command, to instruct MSB to use the specified signalling channel. MSB then sends a page response on the signalling channel; BSSB relays this to VMSCB. VMSCB sends a Process access request message to VLRB to indicate that MSB has responded to paging. VLRB may then initiate authentication, as described in ETS 300 534 [3]. VLRB may also initiate ciphering at this stage, as described in ETS 300 534 [3]. If VLRB determines that MSB is allowed service, it sends a Process access request ack to VMSCB. The Process access request ack message triggers a Start ciphering command message towards BSSB; if VMSCB has not received a Set cipher mode message from VLRB, the Start ciphering command indicates no ciphering. VLRB then sends a Complete call message to VMSCB. VMSCB sends a Setup message towards MSB. The Setup message may include bearer capability information for the call. When MSB receives the Setup message from BSSB, it responds with a Call confirmed message. The Call Confirmed message includes bearer capability information if any of the negotiable parameters of the bearer capability has to be changed. When VMSCB receives the Call confirmed message via BSSB, it sends an Allocate channel message to BSSB. BSSB instructs MSB to tune to a traffic channel by sending an Assignment command. When MSB has tuned to the specified traffic channel it responds with an Assignment complete, message, which BSSB relays to VMSCB as an Allocation complete, and sends an Alerting message to indicate that the called user is being alerted. VMSCB sends an ACM to GMSCB, which relays it to the originating exchange. When the called user answers, MSB sends a Connect message, which BSSB relays to VMSCB. VMSCB: - responds with a Connect ack message towards MSB; - sends an ANM to GMSCB, which relays it to the originating exchange; - sends a Complete call ack to VLRB. The network then waits for the call to be cleared. 6 Principles for interactions with supplementary services This clause specifies the principles used to describe the invocation of the GSM supplementary services which were standardized when this specification was drafted. Registration, erasure, activation, deactivation and interrogation are call-independent operations; they are therefore outside the scope of this specification. Descriptions may be found in the stage 2 specifications for each supplementary service. In the modelling used in this specification, each supplementary service which a network entity supports is managed by a supplementary service handler, which handles data in the entity in which it runs. The call handling processes defined in this specification use the data to define the contents of messages to other entities. The basic call handling processes defined in this specification interact with the supplementary service handlers as shown in the SDL diagrams and the supporting text. If a network entity does not support a supplementary service, it bypasses the interaction with the handler for that supplementary service. Exceptions to this general principle are described later in this clause. ETSI TS 101 043 V5.6.0 (1998-11) 18 GSM 03.18 version 5.6.0 Release 1996
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.1 Line identification services (GSM 03.81)
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.1.1 Calling Line Identification Presentation (CLIP)
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The basic call handling processes ICH_VLR and ICH_MSC interact with the processes CLIP_MAF001 and CLIP_MAF002 (ETS 300 542 [7]) as described in subclauses 7.3.2 and 7.3.1.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.1.2 Calling Line Identification Restriction (CLIR)
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The basic call handling processes OCH_MSC and OCH_VLR interact with the processes CLIR_MAF004 and CLIR_MAF003 (ETS 300 542 [7]) as described in subclauses 7.1.1 and 7.1.2.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.1.3 Connected Line Identification Presentation (COLP)
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The basic call handling processes OCH_MSC and OCH_VLR interact with the processes COLP_MAF006 and COLP_MAF005 (ETS 300 542 [7]) as described in subclauses 7.1.1 and 7.1.2. The basic call handling processes MT_GMSC and ICH_MSC interact with the process COLP_MAF039 [7] as described in subclauses 7.2.1 and 7.3.1.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.1.4 Connected Line Identification Restriction (COLR)
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The basic call handling processes ICH_VLR and ICH_MSC interact with the processes COLR_MAF040 and COLR_MAF041 (ETS 300 542 [7]) as described in subclauses 7.3.2 and 7.3.1.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.2 Call forwarding services (GSM 03.82)
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101 043
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6.2.1 Call Forwarding Unconditional (CFU)
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The basic call handling process SRI_HLR interacts with the process MAF007(ETS 300 543 [8]) as described in subclause 7.2.2
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.2.2 Call Forwarding on mobile subscriber Busy (CFB)
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The basic call handling process ICH_VLR interacts with the process MAF008 (ETS 300 543 [8]) as described in subclause 7.3.2
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.2.3 Call Forwarding on No Reply (CFNRy)
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The basic call handling process ICH_VLR interacts with the process MAF009 (ETS 300 543 [8]) as described in subclause 7.3.2
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.2.4 Call Forwarding on mobile subscriber Not Reachable (CFNRc)
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The basic call handling processes SRI_HLR and ICH_VLR interact with the process MAF010 (ETS 300 543 [8]) as described in subclauses 7.2.2 and 7.3.2
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.3 Call wait (GSM 03.83)
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The basic call handling process ICH_VLR interacts with the process MAF013(ETS 300 544 [9]) as described in subclause 7.3.2. Further details of the handling of call waiting are given in subclauses 7.3.1 and 7.3.2. ETSI TS 101 043 V5.6.0 (1998-11) 19 GSM 03.18 version 5.6.0 Release 1996
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.4 Call hold (GSM 03.83)
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Invocation of call hold before a basic call has been established will be rejected.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.5 Multiparty (GSM 03.84)
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Invocation of multiparty before a basic call has been established will be rejected.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.6 Closed user group (GSM 03.85)
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The basic call handling process OCH_VLR interacts with the process CUG_MAF014 (ETS 300 546 [11]) as described in subclause 7.1.2. The basic call handling process SRI_HLR interacts with the process CUG_MAF015 (ETS 300 546 [11]) as described in subclause 7.2.2. The interactions between call forwarding and CUG (ETS 300 546 [11]) are handled as described in subclause 7.2.2.6.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.7 Advice of charge (GSM 03.86)
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The interactions between Advice of Charge (ETS 300 547 [12]) and MO calls are handled as described in subclauses 7.1.1 and 7.1.2. The interactions between Advice of Charge (ETS 300 547 [12]) and MT calls are handled as described in subclauses 7.3.1 and 7.3.2.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.8 Call barring (GSM 03.88)
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.8.1 Barring of outgoing calls
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The basic call handling process OCH_VLR interacts with the processes MAF017, MAF018 and MAF020 (ETS 300 548 [13]) as described in subclause 7.1.2.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.8.2 Barring of incoming calls
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The basic call handling process SRI_HLR interacts with the processes MAF022 and MAF023 (ETS 300 548 [13]) as described in subclause 7.2.2.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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6.9 Explicit Call Transfer (GSM 03.91)
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There is no interaction between Explicit Call Transfer and the basic call handling described in this specification.
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4adb5594a028990749bbb6b19a2cf30e
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101 043
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7 Functional requirements of network entities
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The text in this clause is a supplement to the definition in the SDL diagrams; it does not duplicate the information in the SDL diagrams. The entities described in this clause interwork with other entities over three different types of interface: - The A interface, used to interwork between the MSC and the BSS or the MS; - The C, D & F interfaces, used to interwork between the MSC & HLR (C), VLR & HLR (D) and MSC & EIR (F); - Telephony signalling interfaces, used to interwork between an MSC and another exchange. ETSI TS 101 043 V5.6.0 (1998-11) 20 GSM 03.18 version 5.6.0 Release 1996 The protocols used over the A interface are BSSMAP, which is specified in ETS 300 590 [16], for interworking with the BSS and DTAP, which is specified in ETS 300 557 [14], for interworking with the MS. The protocol used over the C, D & F interfaces is MAP, which is specified in ETS 300 599 [17]. For the purposes of this specification, the protocol used over telephony signalling interfaces is ISUP, which is specified in ETS 300 356-1 [21]; other telephony signalling systems may be used instead. This specification shows the call handling application processes interworking with a protocol handler for each of the protocols listed above. Each protocol defines supervision timers. If a supervision timer expires before a distant entity responds to a signal, the handling is as defined in the appropriate protocol specification. In general, the protocol handler reports timer expiry to the application as an error condition or negative response. Where a timer is shown in this specification, therefore, it is an application timer rather than a protocol timer. Interworking with the protocol handlers uses functional signal names which do not necessarily have a one-to-one correspondence with the names of messages used in the protocols. An MSC which receives an IAM from an originating exchange may react in three different ways: - It acts as a transit exchange, i.e. it relays the IAM to a destination exchange determined by analysis of the called party address, and thereafter relays other telephony signalling between the originating and destination exchange until the connection is released. This behaviour is not specific to GSM; - It acts as a terminating exchange, i.e. it attempts to connect the call to an MS currently registered in the service area of the MSC; - It acts as a GMSC, i.e. it interrogates an HLR for information to route the call. If the HLR returns routeing information, the MSC uses the routeing information from the HLR to construct an IAM, which it sends to a destination exchange determined by analysis of the routeing information from the HLR. Annex A describes the method which the MSC uses to decide how to process the IAM. The SDL diagrams in this clause show the handling for a number of optional features and services. If the handling consists only of a call to a procedure specific to the feature or service, the procedure call is omitted if the entity does not support an optional feature or service. If the handling consists of more than a call to a procedure specific to the feature or service, the text associated with each SDL diagram specifies the handling which applies if the entity does not support an optional feature or service. For simplicity of description, it is assumed that support for Operator Determined Barring and the Call Forwarding and Call Barring supplementary services is mandatory.
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