Deep Learning based Low-Rank Channel Recovery for Hybrid Beamforming in Millimeter-Wave Massive MIMO

Massive Multiple Input Multiple Output (MIMO) at millimeter wave bands is able to boost the system throughput. A key challenge for the hybrid beamforming design in massive MIMO systems is the acquisition of the full channel state information, since the number of radio frequency chains is much smaller than that of the antennas. Conventional methods require a longer measurement time, a large overhead, or costly signal processing efforts. Therefore, we propose an efficient and adaptable deep neural network based low-rank channel recovery scheme for a hybrid array based massive MIMO system. The proposed neural network architecture includes a common feature extraction module and the adaptable recovery module. The feature extraction, built on the convolutional neural network with residual learning functionality, can efficiently learn the essential features from the low-rank measurements. The adaptable key recovery module maps the essential features to the full channel information. The proposed architecture enables an efficient learning procedure and can be easily adapted to different cases. Simulation results are carried out and compared with existing solutions, showing the potential of applying deep learning concepts in millimeter wave massive MIMO systems.