Deep Reinforcement Learning Based Blind mmWave MIMO Beam Alignment

Directional beamforming is a crucial component for realizing robust wireless millimeter wave (mmWave) communication systems. Beam alignment using brute-force search introduces time overhead, and the location aided blind beam adds additional hardware requirements to system. In this paper, we propose method based on radio frequency (RF) fingerprints of user equipment obtained from base stations. The proposed system performs deep reinforcement learning multiple-base station cellular environment with multiple mobile users. We present novel neural network architecture that can handle mix both continuous discrete actions use policy gradient methods train model. Our results show achieve data rate up four times traditional without any overheads.