Deep Learning Based Analog Beamforming Design for Millimetre Wave Massive MIMO System

Analog beamforming (ABF) architectures for both large-scale antennas at the base station (BS) and the small-scale antennas at the user side in millimetre wave (mmWave) channel are constructed and investigated in this paper with the aid of deep learning (DL) techniques. Transmit and receive beamformers are selected through offline training of the ABF network that accepts input as the channel. The joint optimization of both beamformers based on DL for maximization of spectral efficiency (SE) for massive multiple-input multiple-output (M-MIMO) system has been employed. This design procedure is carried out under imperfect channel state information (CSI) conditions and the proposed design of precoders and combiners shows robustness to imperfect CSI. The simulation results verify the superiority in terms of SE of deep neural network (DNN) enabled beamforming (BF) design of mmWave M-MIMO system compared with the conventional BF algorithms, while lessening the computational complexity.