R1-2409502 discussion

Discussion on AI/ML for CSI compression

From CMCC
Status: not treated
WI: NR_AIML_air
Agenda: 9.1.4.1
Release: Rel-19
Source: 3gpp.org ↗

Summary

CMCC presents evaluation results for AI/ML-based temporal domain CSI compression (Cases 2 and 3), demonstrating significant SGCS gains over Rel-16 and Rel-18 benchmarks. The document proposes prioritizing inter-vendor collaboration Directions A and B, specifies requirements for dataset/parameter exchange, and recommends NW-side monitoring and enhanced codebook structures for ground-truth data collection.

Position

CMCC prioritizes inter-vendor collaboration Directions A and B for further study, arguing they offer better performance and lower specification effort than Direction C. They propose that for Direction A, the NW-side must share dataset information and performance targets to enable UE-side offline engineering, while acknowledging that over-the-air parameter exchange introduces extra overhead. For Direction B, they assert there is no proprietary information disclosure concern and that common encoders are feasible for UEs with similar conditions within a cell. Regarding performance monitoring, CMCC prioritizes NW-side monitoring based on ground-truth CSI reports and UE-side monitoring based on recovery CSI indication. Finally, they propose using Rel-16 eType-II and Rel-18 Doppler codebooks as starting points for ground-truth CSI data collection, with enhanced parameter values to ensure high resolution.

Key proposals

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