ZTE · 9.1.2

ZTE proposes reusing existing legacy signaling structures, such as 'measurementReferenceTime' and 'NR-TimeStamp', to minimize specification impact for AI/ML positioning timestamps and quality indicators. They argue against introducing a new 'associated ID' for training/inference consistency, preferring explicit provision of legacy assistance data instead. ZTE supports the inclusion of phase information (CIR) as model input, presenting technical evidence that CIR offers superior positioning accuracy compared to PDP with acceptable overhead increases. They oppose reporting the transmit offset from gNB to LMF in Case 3b and argue that power quality indicators are unnecessary for channel measurements. For model monitoring, ZTE supports LMF-side metric calculation (Option B) for Case 1 and proposes that label-free monitoring be handled by implementation transparent to the specification.

ZTE proposes supporting sample-based measurements for Rel-19 AI/ML positioning, arguing that implementation ambiguities in path-based measurements cannot be removed, whereas sample-based ambiguities can be resolved via LMF configuration. They require the starting point of Nt samples to be configurable by the LMF (Option C/D) to ensure consistent positioning performance across different TRP implementations. ZTE supports using Channel Impulse Response (CIR) including phase information for model input, presenting technical evidence that CIR provides significantly better positioning accuracy than Power Delay Profile (PDP) with acceptable overhead increases. They oppose introducing a specific indicator to identify AI/ML-derived measurements, arguing that LMF awareness of the procedure and timestamp suffices. For model monitoring, ZTE proposes that label-free monitoring is implementation-specific and requires no specification discussion, while supporting LMF-centric metric calculation for Case 1. They also argue against introducing an associated ID for AI/ML positioning, stating that UE-side generalization can be handled via mixed dataset training.