Federated Learning-Aided Beam Prediction for Multi-User Millimeter Wave Communications

Abstract

Seamless and high-rate millimeter wave (mmWave) communications hinge on precise alignment between transmit and receive beams. While traditional methods like beam sweeping incur high pilot overhead, beam tracking is sensitive to movement patterns. In this study, we leverage received pilot signals collected over a time window and treat them as a time series to predict optimal beams of mmWave users. Inspired by the feature extraction capabilities of machine learning (ML) models, we propose a deep neural network (DNN) trained on a sequence of received pilot signals. Since each user’s dataset reflects only their movement pattern, we adopt federated learning (FL). Here, users train local models and transmit trained parameters to the base station (BS) for aggregation, ensuring model versatility. We assess the performance of our FL-aided beam prediction model using a popular mmWave channel dataset and study the impact of numerous key parameters. We also compare our method against state-of-the-art beam alignment approaches.