ZTE · 9.1.1

ZTE proposes utilizing functionality-based LCM without model ID based signaling for AI/ML beam management, arguing that model transfer challenges diminish the need for model-ID-based approaches. They support bitmap-based beam information reporting and threshold-based beam selection to significantly reduce overhead, citing up to 63.5% reduction in typical settings. For NW-side data collection, ZTE proposes supporting L1 signaling and flexible resource configuration (one or two RS sets) depending on Set A/Set B construction. Regarding performance monitoring, they prefer beam prediction accuracy and RSRP prediction accuracy as primary metrics and support L1 signaling for NW-side monitoring. Finally, ZTE argues that the configuration of associated ID for NW-side additional conditions should be optional, with UE assuming similar properties of DL Tx beam sets for the same ID, while also proposing separate CPU counting for AI/ML processing.

ZTE proposes utilizing functionality-based LCM without model ID based signaling for AI/ML beam management, arguing that model transfer challenges and existing associated ID support diminish the need for model-ID-based approaches. They support bitmap-based methods for beam information reporting to reduce overhead by up to 63.5% in typical settings, and propose threshold-based beam reporting with configurable minimum/maximum beam counts. For NW-side data collection, ZTE supports L1 signaling irrespective of purpose and recommends differential L1-RSRP reporting with larger quantization steps. Regarding UE-sided models, they prefer configuring associated IDs per CSI report configuration and support extending Rel-17 TCI state signaling for multiple future time instances in BM-Case2. For performance monitoring, ZTE supports beam prediction accuracy and RSRP prediction accuracy as primary metrics and requires failure detection to be based on consecutive monitoring results within a predefined window.