Machine Learning-Assisted Beam Alignment for mmWave Systems

Abstract

Beam alignment is a challenging and time-consuming process for millimeter wave (mmWave) initial access (IA). We propose a beam training method that is assisted by machine learning (ML), where we train ML models to predict the optimal Access Point (AP) and optimal beam for a user equipment (UE) given its Global Positioning System (GPS) coordinates. After a (possibly offline) training phase during which exhaustive or hierarchical beam training is performed, our beam training method predicts a few candidate APs and beams knowing only the location of the UE. We train the models and evaluate the performance with realistic mmWave beamforming (BF) data generated from state-of-the- art ray tracing software. We show that even with dynamic scatterers and imperfect knowledge of the UE locations, our beam training method can reliably find the optimal AP and the optimal beam for a UE while reducing the search time by 4x for AP selection and over 10x for beam selection.