Tejas Networks Limited · 9.1.2

Tejas Networks proposes specific parameter sets for sample-based measurements, including Nt values of {32, 64, 128} and Nt' as a fraction of Nt, to balance positioning accuracy and reporting overhead. They require redefining the LoS/NLoS indication for Case-3a to reflect the likelihood that the reported timing corresponds to the direct path, rather than the legacy probability of link existence, arguing that legacy indicators do not improve accuracy in NLoS terrains. They support reusing existing Release-17/18 positioning frameworks for training data collection and assistance data, asserting that current IEs are sufficient with minor enhancements like validity area context. For Model Performance Monitoring, they propose consolidating metric calculation and decision-making at a single entity (LMF or UE/TRP) to minimize specification impact, using range error or positioning error as the primary metrics. They also support implicit reporting of TRP geographical coordinates to protect sensitive location data during the inference phase.

Tejas Networks proposes redefining model outputs for AI/ML positioning by reporting the timing of arrival of the LoS path whether real or virtually inferred, and introducing a new LoS/NLoS indication that characterizes the reported measurement itself rather than the link probability. They propose computing a unique associated ID as a function of TRP-ID, Frequency layer ID, and PRS configurations to ensure consistency between training and inference phases without exposing proprietary spatial filter details. For Model Performance Monitoring, they propose that both metric calculation and decision-making should be centralized at one entity (LMF or UE/TRP) to minimize specification impact, using range error or positioning error as the primary metrics. They propose reusing existing Release-17 frameworks for path-based measurements and existing sampling factors for sample-based measurements to maintain backward compatibility.