CMCC · 9.1.1

CMCC proposes supporting Type 1 and Type 3 data collection contents for NW-side training, emphasizing flexibility for classification and regression models. They require that RS associated with TCI state indication must be measured at least once before application, ensuring QCL parameter validity. For UE-side inference, they prefer Option 1 for reference time determination in BM-Case 2 and support both predicted and measured RSRP reporting options for BM-Case 1. CMCC argues that applicable functionality determination should be up to UE implementation rather than strictly dependent on associated ID matching, allowing for model generalization. They propose separate CPU counting for legacy vs. AI/ML CSI reporting to prevent performance degradation of legacy features. For monitoring, they prefer statistical beam prediction accuracy over N instances and support monitoring sets as down-sampled subsets of Set A to reduce overhead.

CMCC proposes that L1 signaling be supported for NW-sided training data collection and that Top-K beam sweeping be supported for NW-side inference to enhance prediction accuracy. They require that for UE-side models, the overall CPU be separately counted between legacy CSI reporting and AI/ML-based CSI reporting, while being shared among AI/ML features. CMCC prefers Option 1 for BM-Case 2 differential RSRP reporting, which includes CRI of top-K predicted beams per instance. They support dedicated resource sets and report configurations for UE-side model monitoring (Type 1 Option 2), linking the monitoring configuration to an inference report configuration via CSI-ReportConfig ID. Additionally, they propose that the granularity of UE capability reporting for AI/ML be at the sub-use case level, including details on Set A/B size, RS type, and model outputs.