R1-2500160
discussion
Discussion on AIML for CSI prediction
From Spreadtrum
Spreadtrum's prior position on
9.1.3
at
RAN1#118bis
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Advocates for reusing existing AI beam management conclusions and associated ID mechanisms for CSI prediction to minimize workload. Opposes performance monitoring-based approaches, arguing they would cause significant performance loss due to trial-and-error processes.
Summary
Spreadtrum presents eight proposals for 3GPP RAN1 regarding AI/ML-based CSI prediction in NR, focusing on ensuring consistency between training and inference, defining data collection procedures, and establishing performance monitoring mechanisms. The document argues for reusing existing Rel-18 CSI-RS configurations and prioritizing UE-side data collection to minimize specification impact and signaling overhead.
Position
Spreadtrum proposes using an associated ID, configured within the CSI framework, to ensure consistency between training and inference for UE-sided CSI prediction models, arguing that performance monitoring-based approaches cause unacceptable performance loss due to trial-and-error processes. They prefer UE-side data collection over network-side collection to avoid significant reporting overhead and model transfer complexities, suggesting that NW configuration or UE requests should trigger this collection. For inference, they propose reusing Rel-18 MIMO CSI-RS configurations to reduce specification impact. Regarding monitoring, they support Type 1 and Type 3 monitoring using intermediate KPIs like SGCS, while explicitly deprioritizing Type 2 monitoring due to the high overhead of reporting ground-truth CSI to the gNB. They further suggest that gNB should indicate the association between prediction and ground-truth CSI-RS resources to facilitate accurate metric calculation.
Key proposals
- Proposal 1 (Sec Further study on consistency of training and inference): Use associated ID to ensure consistency between training and inference for CSI prediction.
- Proposal 2 (Sec Further study on consistency of training and inference): Configure the associated ID within the CSI framework.
- Proposal 3 (Sec Data collection for training): Study specification impacts for initiating/triggering UE-side data collection via NW configuration or UE request.
- Proposal 4 (Sec Data collection for inference): Reuse CSI-RS configuration for non-AI-based CSI prediction in Rel-18 MIMO for AI-based model inference.
- Proposal 5 (Sec Data collection for monitoring): gNB should indicate the association between prediction CSI-RS and ground-truth CSI-RS for performance monitoring.
- Proposal 6 (Sec Model monitoring): Support performance monitoring Type 1 and Type 3 with intermediate KPIs, such as SGCS, as performance metrics.
- Proposal 7 (Sec Model monitoring): Deprioritize performance monitoring Type 2 for UE-sided CSI prediction models.
- Proposal 8 (Sec Model monitoring): Consider both periodic trigger and event trigger mechanisms for performance monitoring.