Deep Reinforcement Learning-Based Beam Training for Spatially Consistent Millimeter Wave Channels

Abstract

The fifth generation wireless systems are starting to exploit the large bandwidths available in the millimeter-wave (mmWave) spectrum to provide high data rates. The exploitation of mmWave requires the use of compact antenna arrays with hundreds of antenna elements, which leads to very directional beam patterns. The beams at both the transmitter and the receiver are trained periodically to maintain accurate beam alignments. The trade-off between the training overhead and the achievable data rate must be considered. In this paper, we propose an adaptive beam training algorithm using deep reinforcement learning for tracking dynamic mmWave channels. Based on the patterns learnt from historical data, the proposed algorithm can sense the changes in the environment and switch between different beam training methods so that a high data rate can be achieved with a minimum amount of beam training.