Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment
This addresses the need for efficient beam alignment in wireless communication systems, offering a solution that avoids time and hardware overheads, though it appears incremental as it builds on existing reinforcement learning techniques.
The paper tackles the problem of beam alignment in millimeter wave MIMO systems by proposing a deep reinforcement learning method that uses RF fingerprints, achieving up to four times the data rate of traditional methods without overheads.
Directional beamforming is a crucial component for realizing robust wireless communication systems using millimeter wave (mmWave) technology. Beam alignment using brute-force search of the space introduces time overhead while location aided blind beam alignment adds additional hardware requirements to the system. In this paper, we introduce a method for blind beam alignment based on the RF fingerprints of user equipment obtained by the base stations. The proposed system performs blind beam alignment on a multiple base station cellular environment with multiple mobile users using deep reinforcement learning. 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 traditional method without any overheads.