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

Millimeter waves (mmV) communication has emerged as a key enabler for wireless networks to the next generation, due to the support of ultra-high data rate with large antenna arrays. However, its practical deployment is hindered by challenges such as limited radio frequency (RF) chains, high hardware complexity and imperfect channel status information (CSI). To overcome these limitations, this paper proposes a novel deep learning -enhanced binding (DLBF) framework for mmwave massive MIMO sys-tem. The proposed method benefits from deep neural networks to learn effective binding strategies that maximize spectral efficiency while complying with strict hard-ware restrictions. Unlike conventional bonding methods, it depends on the CSI and suffers from high computing costs, the DLBF model demonstrates strength against defects in the channel and hardware limitations. The simulation results show that the proposed DLBF method achieved significantly high spectral efficiency compared to traditional algorithms, showing its potential as a practical solution for real -world mmwave massive MIMO deployment.