Machine learning model selection for beam prediction for wireless networks

Abstract

A method includes beam prediction inference using a first machine learning (ML) model based algorithm that uses a first quantity of reference signal (RS) measurements; transmitting an indication that the first user device is using the first ML model based algorithm that uses the first quantity of RS measurements; receiving, based on beam change dynamics event information received by the network node from one or more other user devices that have one or more corresponding conditions within a threshold to the first user device, a request for the first user device to either: change to a non-ML model based algorithm to perform beam selection or a second ML model based algorithm that uses a second quantity of reference signal measurements to perform beam prediction inference, wherein the second quantity is different than the first quantity, or concurrently perform beam prediction inferences using the first and second ML model based algorithms.