An Efficient Hybrid Beamforming Design for Massive MIMO Receive Systems via SINR Maximization Based on an Improved Bat Algorithm

Abstract

Hybrid analog and digital (HAD) beamforming has been recently receiving considerable deserved attention for practical implementation on the large-scale antenna systems. Compared to fully-digital beamforming, partially-connected HAD beamforming can significantly reduce the hardware cost, complexity, and power consumption. This paper aims to mitigate interference and increase robustness against direction-of-arrival (DOA) mismatch along with lowering hardware complexity, cost, and power dissipation. To achieve these goals, a novel robust HAD beamforming receiver with partially-connected structure is proposed. It is based on methods of an improved bat algorithm (I-BA) and robust adaptive beamformers (RABs) in the digital domain. Since most of the RAB methods are sensitive to the DOA mismatch and depending on the complex weights which lead to an expensive receiver, the I-BA is proposed. In the analog part, analog phase alignment by linear searching (APALS) with sufficiently fine grid points is employed to optimize the analog beamforming matrix. The performance of the proposed I-BA is assessed using MATLAB simulation and compared with BA, and particle swarm optimization (PSO) algorithms. It shows better performance in terms of convergence speed, stability, and convergence rate. Besides, the proposed hybrid I-BA-APALS approach showed better performance compared to other proposed robust hybrid techniques, i.e., diagonal loading (DL) APALS (DL-APALS) and DL spatial matched filter (SMF) APALS (DL-SMF-APALS).