Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems

This paper proposes a deep learning approach to channel sensing and downlink hybrid beamforming for massive multiple-input multiple-output systems operating in the time division duplex mode and employing either single-carrier or multi-carrier transmission. The conventional precoding design involves estimating the high dimensional channel and designing the precoders based on such estimate. This two-step process is, however, not necessarily optimal. This paper shows that by training the analog sensing and designing the hybrid downlink precoders directly from the received pilots without the intermediate high-dimensional channel estimation, the overall system performance can be significantly improved. However, the direct approach that simultaneously designs the hybrid precoders is difficult to train and only works for a fixed number of users. In this paper, we develop a simplified semi-direct approach that enjoys most of the advantages of the direct design while eliminating its drawbacks. Specifically, the proposed approach learns the uplink sensing stage and downlink analog precoder using deep learning and designs the digital precoder based on an estimate of the low-dimensional equivalent channel. Numerical comparisons show that the proposed methodology requires significantly less training overhead than the conventional strategy and further demonstrate its generalizability to various different system settings.