Design of Deep Learning-Based Beamforming for mm-Wave Massive MIMO Systems

Abstract

Millimeter-wave (mm-Wave) massive MIMO systems enable high data rates but face significant challenges due to the limited number of radio-frequency (RF) chains and imperfect channel state information (CSI). This paper proposes a deep learning-based beamforming (DLBF) framework that directly learns analog beamforming vectors under strict hardware constraints. Unlike conventional optimization methods, the proposed approach employs an unsupervised learning strategy to maximize spectral efficiency while satisfying constant modulus constraints. The network model is explicitly designed to be robust against imperfect CSI, hardware phase noise, and varying channel conditions. Simulation results demonstrate that the proposed DLBF framework significantly outperforms traditional hybrid beamforming methods in terms of spectral efficiency, particularly in low-quality channel estimation and low signal-to-noise ratio (SNR) scenarios.