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

Abstract

Analog beamforming (ABF) architectures for both large-scale antennas at base station (BS) and small-scale antennas at 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 ABF network that accepts input as 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 neutral network (DNN) enabled beamforming (BF) design of mmWave massive MIMO system compared with the conventional BF algorithms while lessening the computational complexity.

Initial Access & Beam Alignment for mmWave and Terahertz Communications

“…The analog beamforming is constructed by employing a small number of quantized phase shifts to connect antennas over a network of digitally managed phase shifters to a single RF chain [40]. It should be taken into consideration that the Analog-to-Digital (AD) converter and RF frontend can get overloaded; thus, the self-interference has to be adequately reduced in the analog domain.…”

“…However, having a dedicated RF chain for each antenna is a hardware limitation, particularly in mmWave communication. As a result, fully digital beamforming in mmWave is costly and difficult to implement [40].…”