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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2 AI/ML related activities in TSG SA & CT Working Groups
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.1 AI/ML related terminology
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.1.1 TSG SA WG2
| The following definitions are provided in clause 3 of TS 23.288 [8]:
- Analytics Accuracy Information: Represent a performance measure of an analytics ID provided by an NWDAF containing AnLF, which is composed of the number of correct predictions of the analytics ID out of all predictions and the corresponding number ... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.1.2 TSG SA WG5
| The following definitions are provided in clause 3 of TS 28.105 [9]:
- ML model: a manageable representation of an ML model algorithm.
NOTE 1: An ML model algorithm is a mathematical algorithm through which running a set of input data can generate a set of inference output.
NOTE 2: An ML model algorithm is proprieta... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.1.3 TSG SA WG6
| The following definitions are provided in clause 3 of TR TS 23.482 [34]:
- ML model: According to TS 28.105 [9], mathematical algorithm that can be "trained" by data and human expert input as examples to replicate a decision an expert would make when provided that same information.
- ML model lifecycle: The lifecycle... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2 AI/ML related activities
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.1 Rel-18 SA WG1 WID - AI/ML model transfer in 5GS (AIML_MT)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.1.1 Description
| The objective of this work item is to specify performance requirements (for end-to-end latency, experienced data rate, communication service availability) and service requirements (for AI/ML QoS management, AI/ML model /data distribution/transfer, network performance and resource utilization monitoring/prediction) for ... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.1.2 Activities summary
| The three application AI/ML operations in 5.2.2.1.1 the 5G system can support were specified in clause 6.40.1 of TS 22.261 [6] as follows:
- AI/ML operation splitting between AI/ML endpoints: The AI/ML operation/model is split into multiple parts according to the current task and environment. The intention is to offlo... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.2 Rel-19 SA WG1 SID - AI/ML Model Transfer Phase 2 (FS_AIML_MT_Ph2)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.2.1 Description
| The objective of this study is to explore new use cases and potential service and performance requirements to support efficient AI/ML operations using direct device connections. This includes:
- Distributed AI training and inference based on direct device connections, such as traffic KPIs, various QoS and functional ... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.2.2 Activities summary
| In this study, TR 22.876 [21] described study the use cases with potential functional and performance requirements to support efficient AI/ML operations over the application layer using direct device connection for various applications e.g. auto-driving, robot remote control, video recognition, etc. The agreed activiti... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.3 Rel-19 SA WG1 WID - AI/ML Model Transfer Phase 2 (AIML_MT_Ph2)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.3.1 Description
| The objective of this work item is to specify KPI and functional requirements for 5GS to support the AIML data transfer by leveraging direct device connection under 5G network control. These objectives were derived based on outcome of Rel-19 study in SA WG1 that relates to how the 5GS supports the transmissions of AI/M... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.3.2 Activities summary
| The service requirements and performance requirements for AI/ML model transfer over the application layer in 5GS with direct device connection are specified in clause 6.40.2.2 and in clause 7.10.2 of TS 22.261 [6], respectively.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.4 Rel-18 SA WG2 WID - Enablers for Network Automation for 5G - phase 3 (eNA_Ph3)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.4.1 Description
| The objective of this work item is to further enhance NWDAF, based on what has been specified in the previous releases to allow 5GS to support network automation. This work item focuses on architecture enhancement, new scenarios and the necessary inputs and outputs to the NWDAF based on the conclusions of the study in ... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.4.2 Activities summary
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.4.2.1 AIML related LCM activities
| Data collection/storage/exposure
Analysis of data collection activities as part of eNA, eNA_Ph2 work:
- Data collection in TS 23.288 [8] refers to data collected by the NWDAF and DCCF. The data are collected for the purpose of analytics generation and training of ML models. NWDAF/DCCF/MFAF collects data from NFs/AFs,... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.4.2.2 AI/ML functional entities.
| As part of eNA, eNA_Ph2 and eNA_Ph3 work the following functional entities have been defined:
- NWDAF (Network Data Analytics Function) is defined in TS 23.288 [8] with main function to generate analytics (statistics and/or predictions) for one or more network events. NWDAF is defined by two logical functions.
- AnLF... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.5 Rel-18 SA WG2 WID - System Support for AI/ML-based Services (AIMLsys)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.5.1 Description
| This work item implements the conclusions of the Rel-18 study on the 5GS architectural and functional extensions to enable 5GS to assist the Application AI/ML operations. The normative text is defined based on the agreed conclusions on 6 key issues, ensuring consistency with other 5GS features. The agreed conclusions f... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.5.2 Activities summary
| This work item specifies a list of principles that apply when the 5GS assists the AI/ML operation at the application layer as specified in clause 5.46 of TS 23.501 [22], namely:
- AF requesting 5GS assistance to AI/ML operations in the application layer shall be authorized by the 5GC using the existing mechanisms.
- ... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.5.2.1 AI/ML related LCM activities
| No AI/ML related LCM activities or functional entities were specified as part of this work. Instead, the activities summarized in clause 5.2.2.5.2 specify 5GS support for AI/ML related LCM activities (e.g. AI/ML model training and inference) assumed to be conducted at the application layer.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.6 Rel-19 SA WG2 SID - Core Network Enhanced Support for Artificial Intelligence (AI)/Machine Learning (ML) (FS_AIML_CN)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.6.1 Description
| The aim of this study is to investigate and identify potential architectural and system-level enhancements to support AI/ML enhancements. Specifically, the objectives include:
- AI/ML Cross-Domain Coordination Aspects: Investigate enhancements to support AI-enabled RAN based on the conclusions of the RAN study in TR 3... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.7 Rel-19 SA WG2 WID - Core Network Enhanced Support for Artificial Intelligence (AI)/Machine Learning (ML) (AIML_CN)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.7.1 Description
| The objective of this work item is to specify the following enhancements to 5GS as per the conclusions reached within the Rel-19 study for the following aspects:
- Enhancements to LCS to Support Direct AI/ML-Based Positioning:
- LMF Enhancements: The LMF will be enhanced to perform location calculations based on an M... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.7.2 Activities summary
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.7.2.1 AIML related LCM activities
| As part of the AIML_CN work the following enhancements are supported:
Data collection/exposure
Data collection from Direct AIML positioning. Data is collected to train an ML model for LMF-based AIML positioning and to support inference.
AI/ML model training
- ML model training for LMF-side Direct AIML positioning. ... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.8 Rel-18 SA WG3 WID - Security aspects of enablers for Network Automation for 5G - phase 3 (eNA_SEC_PH3)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.8.1 Description
| The main objective of this work is to produce normative specification based on the conclusions from Rel-18 study. More specifically, the following objectives are expected to be specified:
- Protection of data and analytics exchange in roaming case.
- Security for AI/ML model storage and sharing.
- Authorization of s... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.8.2 Activities summary
| As part of this work, enhancements were specified for:
- The protection of data and analytics exchange in roaming case including authorization and anonymization of data/analytics. Authorization at data and analytics level is enforced by the roaming entry NWDAF producer. The roaming entry NWDAF producer is responsible ... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.9 Rel-19 SA WG3 SID - Security aspects of Core Network Enhanced Support for AIML (FS_AIML_CN_SEC)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.9.1 Description
| The objectives of this study are the following:
- Security Aspects on Enhancements to LCS: Study security aspects on enhancements to LCS to support AI/ML-based positioning, considering the conclusions in TR 38.843 [3] and TR 23.700-84 [7].
- Security Aspects of Cross-Domain Vertical Federated Learning (VFL):
- Autho... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.10 Rel-19 SA WG4 SID - Artificial Intelligence (AI) and Machine Learning (ML) for Media (FS_AI4Media)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.10.1 Description
| The primary objective of this study item is to identify relevant interoperability requirements and implementation constraints of AI/ML in 5G media services. The specific objectives include:
- Use Cases for Media-Based AI/ML Scenarios: List and describe the use cases for media-based AI/ML scenarios, based on those defi... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.11 Rel-18 SA WG5 WID - AI/ML management (AIML_MGT)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.11.1 Description
| The objective of this work is to specify the AI/ML management capabilities, including use cases, requirements and solutions for each phase of the AI/ML operational workflow for managing the AI/ML capabilities in 5GS (i.e. management and orchestration, 5GC and NG-RAN), including:
- Management capabilities for ML traini... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.11.2 Activities summary
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.11.2.1 ML model life cycle management (LCM)
| Rel-18 specification in TS 28.105 [9] addressed the AI/ML LCM management capabilities (including wide range of use cases, corresponding requirements (stage 1) and solutions (stage 2 NRMs & stage 3 OpenAPIs) for the ML model, including ML model training (which also includes validation), ML model testing, AI/ML inference... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.11.2.2 ML model lifecycle management capabilities
| Each step in the ML model lifecycle, defined in TS 28.105 [9] (see clause 6.1) i.e. the ML model training, ML model testing, AI/ML emulation, ML model deployment and AI/ML inference correspond to number of dedicated management capabilities. The specified capabilities are developed based on corresponding use cases and r... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.11.2.3 AI/ML functionalities management scenarios (relation with managed AI/ML features)
| The Rel-18 specification TS 28.105 [9] (see clause 4a.2) also documented AI/ML functionalities management scenarios in relation with managed AI/ML features which describe the possible locations of ML training function and AI/ML inference function involving the various 3GPP system domains.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.12 Rel-19 SA WG5 SID - AI/ML management - phase 2 (FS_AIML_MGT_Ph2)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.12.1 Description
| The objectives of this study item include:
- Continuation of AI/ML Studies: Continue the study on AI/ML emulation, AI/ML inference coordination and ML knowledge transfer that are left over from Rel-18.
- Management Aspects of AI/ML functionalities defined by other 3GPP WGs:
- AI/ML Model Transfer/delivery in RAN: St... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.13 Rel-19 SA WG6 SID - Application layer support for AI/ML services (FS_AIMLAPP)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.13.1 Description
| The objective of this study is to enable support for AI/ML services at the application enablement layer. This includes the following:
- Analysis of Rel-18 and Rel-19 Requirements: Analyse the requirements in TS 22.261 [6] related to AI/ML model distribution, transfer and training. Identify key issues and develop corre... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.13.2 Activities summary
| In this study, TR 23.700-82 [4] described the AI/ML enablement capabilities for supporting vertical use cases. The agreed AIMLE activities which were progressed in normative phase are described in clause 5.2.2.14.2.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.14 Rel-19 SA WG6 WID - Application enablement for AI/ML services (AIML_App)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.14.1 Description
| The objectives of this work include the following:
Develop Stage 2 normative technical specification for AIML enablement service as a new SEAL service, based on the key issues, architecture, solutions and conclusions captured in TR 23.700-82 [4]. The Stage 2 normative technical specification will include the following... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.14.2 Activities summary
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.14.2.1 AI/ML Functional Entities
| In AIML_App, the following logical entities have been introduced within SEAL framework:
- AIMLE server includes of a common set of services for exposure of AIML functionality, including federated and distributed learning (e.g. FL client registration management, FL client discovery and selection) and reference points. ... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.14.2.2 AI/ML related LCM activities
| Model Lifecycle enablement for AI/ML
Some AIMLE capabilities are applicable to ML model lifecycle enablement which provides assistance for use cases where an ASP/VAL layer wants to find and use other application entities to perform some ML operations (e.g. ML model inference) and AIMLE server as a mediator to accompli... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.15.1 Description
| In Rel-18, the UPF offers services to the NEF, AF, SMF, NWDAF, DCCF, MFAF via the Nupf service based interface for data collecting in AI/ML related activities. In Rel-19, CT WG4 is studying "Protocol for AI Data Collection from UPF", which aims at studying UPF data Collection for AI/ML and whether alternative protocols... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.15.2 Activities summary
| Editor's note: Reference to TR 21.919 can be added when the work item summary is made available.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.16 Rel-19 SA WG3 WID - Security aspects of Core Network Enhanced Support for AIML (AIML_CN_SEC)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.16.1 Description
| The following objectives are expected to be specified as a result of this work item:
- Security aspects on enhancements to LCS to support AIML.
- Security aspects on VFL process.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.16.2 Activities summary
| Editor's note: Reference to TR 21.919 can be added when the work item summary is made available.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.17 Rel-19 SA WG5 WID - AI/ML management phase 2 (AIML_MGT_Ph2)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.17.1 Description
| The objectives of AI/ML management phase 2 work item are to specify the management capabilities to support AI/ML functions defined by 3GPP, including:
- NG-RAN AIML-based Coverage and Capacity Optimization, and NG-RAN AIML-based Network Slicing defined by RAN3,
- Model delivery/transfer as defined by RAN1/2,
- ML mo... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.17.2 Activities summary
| Editor's note: Reference to TR 21.919 can be added when the work item summary is made available.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.18 Rel-19 SA WG6 WID - Application Data Analytics Enablement Service (TEI19_ADAES)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.18.1 Description
| The objectives of this work item include the following:
- Clarify metrics related to the analytics inputs/outputs introduced in TS 23.436 [33].
- Enhance the IEs for the request/response of data collection from Data Producer and/or A-ADRF (or via A-DCCF) in TS 23.436 [33].
- Complete the definition of IEs for the da... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.18.2 Activities summary
| Editor's note: Reference to TR 21.919 can be added when the work item summary is made available.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.19 Rel-19 CT WG1/WG3 WID – CT aspects of application enablement for AI/ML services (AIML_App)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.19.1 Description
| The objective of this work item includes providing the stage 3 solutions and protocol support for application enablement for AIML services (AIML_App) based upon the normative technical specification for the functionalities defined in stage 2 requirements under the AIML App WID in the SA WG6 working group.
Stage 3 work... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.19.2 Activities summary
| Editor's note: Reference to TR 21.919 can be added when the work item summary is made available.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.20 Rel-19 CT WG3 WID – Rel-19 Enhancements of Network Automation Enablers (eNetAE19)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.20.1 Description
| The objective of this work item is to specify the technical improvements and enhancements to the network data analytics related services in Release 19 stage 3 level, mainly (but not exhaustively) including:
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 1 Potential completion of the support of Processing Instructions in MFAF and ADRF APIs.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 2 Completion of the analytics transfer procedure.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 3 Completion of the analytics aggregation.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 4 Clarifications for input data information in ML model training procedure.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5 Enhancements of Collective Behaviour of UEs in AF, e.g., to include the average moving speed of the UE.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 6 Enhancements of Nnwdaf_MLModelTraining_Notify service operation to provide the Global ML Model Accuracy information.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 7 Other technical enhancements and corrections for the services related to Network Automation Enablers (i.e., the pure stage 3 protocol and interface enhancements are not included), which are not covered by the other dedicated WIs.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.20.2 Activities summary
| Editor's note: Reference to TR 21.919 can be added when the work item summary is made available.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.21 Rel-19 CT WG3/WG4 WID – CT aspects of Core Network Enhanced Support for Artificial Intelligence (AI) and Machine Learning (ML) (AIML_CN)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.21.1 Description
| The objective of this work is to specify the CT aspects of Core Network Enhanced Support for Artificial Intelligence (AI)/Machine Learning (ML) in order to implement the stage 2 normative work. The stage 3 work shall be started after the applicable normative stage 2 requirements are available, the detail impacts are su... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.2.2.21.2 Activities summary
| Editor's note: Reference to TR 21.919 can be added when the work item summary is made available.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3 AI/ML related activities in TSG RAN Working Groups
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.1 AI/ML related terminology
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.1.1 TSG RAN WG1
| The following definitions are provided in clause 3 of TR 38.843 [3]:
- AI/ML-enabled Feature: refers to a Feature where AI/ML may be used.
- AI/ML Model: A data driven algorithm that applies AI/ML techniques to generate a set of outputs based on a set of inputs.
- AI/ML model delivery: A generic term referring to de... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.1.2 TSG RAN WG3
| The following definitions are provided in clause 16.20 of TS 38.300 [11]:
- AI/ML Model Training follows the definition of the "ML model training" as specified in clause 3.1 of TS 28.105 [9].
- AI/ML Model Inference follows the definition of the "AI/ML inference" as defined in clause 3.1 of TS 28.105 [9].
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2 AI/ML related activities
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.1 Rel-19 RAN WG1/RAN WG4 WID - Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface (NR_AIML_air)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.1.1 Description
| The objective of this work is to provide specification support for the following aspects:
- AI/ML general framework for one-sided AI/ML models within the realm of what has been studied in the FS_NR_AIML_air project (RAN WG2):
- Signalling and protocol aspects of Life Cycle Management (LCM) enabling functionality and ... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.1.2 Activities summary
| Editor's note: Reference to TR 21.919 can be added when the work item summary is made available.
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.2 Rel-19 RAN WG2 SID - AIML for mobility in NR (FS_NR_AIML_Mob)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.2.1 Description
| The study will focus on mobility enhancement in RRC_CONNECTED mode over air interface by following existing mobility framework, i.e. handover decision is always made in network side. Mobility use cases focus on standalone NR PCell change. UE-side and network-side AI/ML model can be both considered, respectively. The in... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.3 Rel-18 RAN WG3 WID - Artificial Intelligence (AI)/Machine Learning (ML) for NG-RAN (NR_AIML_NGRAN-Core)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.3.1 Description
| The objective of this work is to specify data collection enhancements and signalling support within existing NG-RAN interfaces and architecture (including non-split architecture and split architecture) for AI/ML-based Network Energy Saving, Load Balancing and Mobility Optimization.
Support of AI/ML for NG-RAN, as a RA... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.3.2 Activities summary
| Summary is available in clause 11.2 of TR 21.918 [2].
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.4 Rel-19 RAN WG3 SID - Enhancements for Artificial Intelligence (AI)/Machine Learning (ML) for NG-RAN (FS_NR_AIML_NGRAN_enh)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.4.1 Description
| The objective of this study is to further investigate new AI/ML based use cases and identify enhancements to support AI/ML functionality and further discussions on the Rel-18 leftovers. The detailed objectives of the study are listed as follows:
- Study two new AI/ML based use cases, i.e. Network Slicing and CCO, with... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.5 Rel-19 RAN WG1/RAN WG4 SID - Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface (FS_NR_AIML_air_Ph2)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.5.1 Description
| The objective of this work is to provide further study on some outstanding issues identified during the prior study. specifically for the following aspects:
- CSI feedback enhancement [RAN1]:
- For CSI temporal prediction: further update TR 38.843 [3] with additional evaluations.
- For CSI compression (two-sided mod... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.6 Rel-19 RAN WG3 WID - Enhancements for Artificial Intelligence (AI)/Machine Learning (ML) for NG-RAN (NR_AIML_NGRAN_enh-Core)
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fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.6.1 Description
| The aim of this work item is to specify new AI/ML-based use cases and introduce further enhancements to finalize the Rel-18 leftovers based on the conclusions captured in TR 38.743 [69] (FS_NR_AIML_NGRAN_enh).
The objective of this work is to provide specification support for the following aspects:
- Specify data col... |
fba61181a0f9993b18933fec82e25a47 | 22.850 | 5.3.2.6.2 Activities summary
| Editor's note: Reference to TR 21.919 can be added when the work item summary is made available.
|
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