Lenovo · 9.1.1

Lenovo proposes supporting UE-initiated beam management procedures for data collection to enable UE-side model training. They require combining an associated ID with performance monitoring to ensure consistency between training and inference, arguing that the associated ID alone is insufficient due to signaling overhead. For BM-Case 2, they propose specific overhead reduction techniques, including differential RSRP quantification relative to the global maximum and reporting unique beams with time-stamp indicators. They introduce the concept of AI Process Units (APUs) to manage UE hardware resources and propose refining CSI computation time to account for AI inference latency. For performance monitoring, they support Alt 2 and Alt 3 metrics, which rely on L1-RSRP differences, and propose event-triggered beam reports for hybrid monitoring scenarios.

Lenovo advocates for a comprehensive AI/ML beam management framework that supports both UE-side and NW-side inference with strong emphasis on: 1) UE autonomy in data collection and model training initiation, 2) unified configuration approaches supporting both BM-Case1 and BM-Case2 through common frameworks, 3) robust performance monitoring with multiple metric alternatives (Alt 1, 2, 3), and 4) practical overhead reduction through variable-size beam reports and differential RSRP quantization. They push for flexible associated ID mechanisms combined with performance monitoring rather than rigid pre-defined conditions, and emphasize hardware resource management through AI process units.