Deep-Learning-Based Phase-Only Robust Massive MU-MIMO Hybrid Beamforming

Abstract

Conventional hybrid beamforming (BF) techniques encounter high computational complexity (CC) and performance loss due to array steering vector mismatches. Therefore, in this letter, a joint robust adaptive BF (RAB) method based on the diagonal loading technique along with phase-only digital beamformer design is proposed. In addition, with the aim of reducing the CC of the system, a novel deep-learning model is proposed to estimate the digital weights. Simulations demonstrated that the proposed deep neural network (DNN) model can have similar performance for digital BF weights estimation as a metaheuristic-based one with significantly lower CC.