Deep Reinforcement Learning Based Blind mmWave MIMO Beam Alignment

• Published in: IEEE transactions on wireless communications 2022-10, Vol.21 (10), p.8772-8785
• Main Authors: Raj, Vishnu, Nayak, Nancy, Kalyani, Sheetal
• Format: Article
• Language: English
• Subjects: Alignment; Array signal processing; Base stations; beam alignment; Beamforming; Communications systems; Computer architecture; Deep learning; deep reinforcement learning; Millimeter wave communication; Millimeter waves; MIMO; MIMO communication; Neural networks; Radio equipment; Radio frequency; Training; Wireless communication; Wireless communications
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2022.3169900

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 alignment adds additional hardware requirements to the system. In this paper, we propose a blind beam alignment method based on the radio frequency (RF) fingerprints of the user equipment obtained from the base stations. The proposed system performs blind beam alignment using deep reinforcement learning on a multiple-base station cellular environment with multiple mobile users. We present a novel neural network architecture that can handle a mix of both continuous and discrete actions and use policy gradient methods to train the model. Our results show that the proposed method can achieve a data rate of up to four times the data rate of the traditional method without any overheads.