Conclusions
The following aspects have been studied for the general framework of AI/ML over air interface for one-sided models and two-sided models:
- Various Network-UE Collaboration Levels
- Functionality-based LCM and model-ID-based LCM
- Functionality/model selection, activation, deactivation, switching, fallback
- Functionality identification and model identification
- Data collection
- Performance monitoring
- Various model identification Types and their use cases
- Reporting of applicable functionalities/models
- Method(s) to ensure consistency between training and inference regarding NW-side additional conditions (if identified) for inference at UE
- Model delivery/transfer and analysis of various model delivery/transfer Cases
The above studied aspects for General Framework can be considered for developing/specifying AI/ML use cases and common framework (if needed for some aspects) across AI/ML use cases.
CSI feedback enhancement:
CSI compression sub use case:
The performance benefit and potential specification impact were studied for AI/ML based CSI compression sub use case.
Evaluation has been performed to assess AI/ML based CSI compression from various aspects, including performance gain over non-AI/ML benchmark, model input/output type, CSI feedback quantization methods, ground-truth CSI format, monitoring, generalization, training collaboration types, etc. Some aspects were studied but not fully investigated, including the options of CQI/RI calculation, the options of rank>1 solution.
Performance gain over baseline and computational complexity in FLOPs are summarized in clause 6.2.2.8.
Potential specification impact on NW side/UE side data collection, dataset delivery, quantization alignment between CSI generation part at the UE and CSI reconstruction part at the NW, CSI report configuration, CSI report format, pairing information/procedure and monitoring approach were investigated but not all aspects were identified.
The pros and cons are analysed for each training collaboration types, and each training collaboration type has its own benefits and limitations in different aspects. The study has investigated the feasibility of the studied training collaboration types and necessity of corresponding potential RAN1 specification impact. However, not all aspects have been concluded.
Both NW side and UE side performance monitoring were studied, some but not all aspects were concluded.
From RAN1 perspective, there is no consensus on the recommendation of CSI compression for normative work.
At least the following aspects are the reasons for the lack of RAN1 consensus on the recommendation of CSI compression for normative work:
- Trade-off between performance and complexity/overhead.
- Issues related to inter-vendor training collaboration.
Other aspects that require further study/conclusion are captured in the summary above.
CSI prediction sub use case:
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Beam management:
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Positioning accuracy enhancements:
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3GPP TSG-RAN WG1 Meeting #121 R1-2505009
St. Julian’s, Malta 19 – 23 May, 2025
Source: Moderator (OPPO)
Title: TPs capturing the new agreements of other aspects (9.1.4.2)
Agenda Item: 9.1
Document for: Discussion and Decision
TPs capturing the new agreements of other aspects (9.1.4.2)
The original TP1-TP4 in R1-2407520 are agreed in RAN1#118
They are also copied in Section 2 for reference
On top of the original TP1,TP3 and TP4 (the change marks are still kept), new agreements are copied that are highlighted by YELLOW and the associated agreements are also included for reference
Only include the new agreements of 9.1.4.2 made in RAN1#118bis, 119, 120
TP1-1 (on top of TP1)
TP3-1 (on top of TP3)
TP4-1 (on top of TP)
Annex: Agreed TPs (for reference)
The following TP1-TP4 in R1-2407520 are agreed in RAN1#118
TP1
TP2
TP3
TP4
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