ZTE · 9.1.2

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.

ZTE advocates FOR maximizing reuse of existing 3GPP procedures and specifications rather than defining new enhancements, strongly supports CIR with phase information over PDP for better positioning accuracy despite higher overhead, pushes for sample-based measurements over path-based measurements for unified implementation, and argues AGAINST specifying network-side additional conditions or extra assistance information requirements.