Improving Beam Alignment Accuracy in mmWave Communication Systems With Auxiliary Tasks.

Beam alignment is essential for high-quality data transmission in millimeter wave (mmWave) communication systems. Recent studies have revealed that the beam alignment method can train a small-scale probing codebook that is customized to specific sites, and leverage the codebook measurements to determine the optimal transmit beam. However, existing approaches still necessitate a certain number of probing beams in order to achieve high-accuracy alignment. In this work, we propose a multi-task learning-based beam alignment method, leveraging channel reconstruction and contrastive representation as auxiliary tasks, to improve the primary task of joint probing codebook design and optimal beam selection. Specifically, we offer a channel reconstruction module that estimates the wireless channel with the probing measurements, to improve the channel sensing efficiency of the probing codebook. Likewise, we also expose a contrastive representation module to improve the beam selector's representation robustness against the noisy channel. Results obtained from simulations using realistic public datasets indicate that the proposed method surpasses current state-of-the-art beam alignment techniques. The proposed method demonstrates superior performance in terms of alignment accuracy, achieved throughput, and beam sweeping complexity.