Self-supervised Deep Learning for mmWave Beam Steering Exploiting Sub-6 GHz Channels

Abstract : mmWave communication requires accurate and continuous beam steering to overcome the severe propagation loss and user mobility. In this paper, we leverage a self-supervised deep learning approach to exploit sub-6 GHz channels and propose a novel method to predict beamforming vectors in the mmWave band for a single access point-user link. This complex channel-beam mapping is learned via data