CMCC · 9.1.2

CMCC slightly prefers sample-based measurements for AI/ML positioning to avoid the potential errors introduced by intermediate path-based processing at the UE. They propose that the entity deriving the AI model should provide the recommended number of samples (Nt') and that selection rules, such as strongest power samples or thresholds, should be specified to reduce ambiguity. For data collection, CMCC argues that offline training with marginal specification impact is feasible, but questions the feasibility of obtaining ground-truth labels via PRUs due to large dataset sizes. They propose reinterpreting the existing 'Timing Measurement Quality' IE to report performance metrics for gNB-side models and suggest that legacy LOS/NLOS reporting formats be reused. Regarding consistency between training and inference, CMCC deprioritizes options 1 and 2, preferring further discussion on options 3 and 4, and emphasizes that the granularity of validity areas needs further study. For second-priority use cases, they require that reported timing information for Case 2a be based on legacy reporting formats to minimize LMF modifications.