METHODS AND APPARATUSES FOR POSITIONING CONFIGURATION MANAGEMENT FOR ML TRAINING AND INFERENCE

Abstract

Aspects presented herein may improve the efficiency and accuracy for machine learning (ML), training and inference, where entities associated with the ML, training/inference may be able to identify whether same/similar sets of configurations are used both in the ML training and the ML inference. In one aspect, a UE receives a configuration for a set of positioning measurements from a network entity, where the configuration includes at least one configuration ID associated with an ML data collection or an ML inference for a set of reference signals (RSs). The UE performs the set of positioning measurements based on the configuration and the set of RSs. The UE stores at least one parameter for the ML data collection or load a first neural network (NN) based on the at least one configuration ID.

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