CMCC · 9.1.3

CMCC prefers not to specify consistency of training/inference regarding NW-side additional conditions (such as tilt angle and TXRU mapping) unless sufficient simulation results demonstrate non-negligible degradation on model generalization performance, citing current observations of negligible impact. For performance monitoring, CMCC proposes using intermediate KPIs (e.g., SGCS, NMSE) as the starting point for Type 3 monitoring, arguing that eventual KPIs are inappropriate due to dependency on external factors like scheduling. For Type 1 monitoring, CMCC presents two alternatives for performance output: calculation/comparison outcomes based on thresholds or recommended LCM decisions reported to the NW. CMCC supports reusing the data collection mechanism from AI/ML-based beam management for CSI prediction to minimize specification overhead. Finally, CMCC proposes reusing Rel-18 CSI parameters (e.g., measurement and reporting windows) with necessary modifications to adapt to AI/ML-enabled CSI prediction, rather than defining entirely new parameters.

CMCC proposes studying two options for ensuring consistency of NW-side additional conditions across training and inference for AI-based CSI prediction: one based on associated ID and another based on performance monitoring. They propose combining these solutions, where the associated ID preliminarily guarantees consistency without exposing proprietary information, while performance monitoring handles residual issues. CMCC requires that if associated ID is supported, the UE assumes consistency of NW-side additional conditions with the same ID at least within a cell, and that the ID is configured within the CSI framework. For performance monitoring, they propose using intermediate KPIs as a starting point for Type 3 monitoring and present two alternatives for Type 1 monitoring involving UE-calculated metrics or UE-recommended LCM decisions. Finally, CMCC proposes reusing data collection mechanisms from AI/ML beam management and adapting Rel-18 CSI parameters, such as measurement and reporting windows, for AI/ML-enabled CSI prediction.