Machine-Learning-Aided Method for Optimizing Beam Selection and Update Period in 5G Networks and Beyond

Abstract Finding the optimal beam pair and update time in 5G systems operating at mmWave frequencies is time-intensive and resource-demanding. This intricate procedure calls for the proposal of more intelligent approaches. Therefore, this work proposes a machine learning-based method for optimizing beam pair selection and its update time. The method is structured around three main modules: spatial characterization of beam pair service areas, training of a machine learning model using collected beam pair data, and an algorithm that uses the decision function of the trained model to compute the optimal update time for beam pairs based on the spatial position and velocity of user equipment. When the machine learning model is deployed in the network comprising one single gNB and one single user equipment in an mmWave scenario, improvement in SINR and throughput up to 4% are observed. Improvements are gathered because of a reduction of 87.5% in beam pair selections because of an increase of approximately 2330% in the effective time between successive beam pair searches. This method could offer real-time optimization of the beam pair procedures in 5G networks and beyond.