Integration of a GNN and U-Net for Hybrid Beamforming in mm-Wave m-MIMO Systems

Millimeter-Wave (mm-Wave) based Massive Multiple-Input Multiple-Output massive (m-MIMO) systems promise high data rates and high spectral efficiency in next-generation i.e., beyond fifth generation (B5G) wireless networks. Moreover, the fully-digital precoding technique helps further to achieve the highest spectral efficiency in mm-Wave massive MIMO systems but face significant challenges such as hardware complexity and cost. Practically, these systems are infeasible due to complexity and cost of using radio frequency (RF) chain with each antenna in such systems. Hybrid beamforming emerges as a practical solution by combining analog and digital precoding, yet its performance hinges on accurate and low-complexity beamformer design under sparse and dynamic channel conditions. This paper proposes a novel deep learning approach that synergizes Graph Neural Networks (GNNs) and U-Net architectures to efficiently learn optimal hybrid beamforming strategies from channel state information (CSI). For generalizing the system in various configurations and distributions, the GNN is used to capture the spatial and topological relationships between antennas and users. Simultaneously, for extracting the various spatial features for enhanced precoder rebuilding, the U-Net is used to process the structured CSI representations. The proposed approach is trained using a loss function for optimizing the spectral efficiency (SE) and bit error rate (BER). Simulation results show that the integrated GNN and U-Net model achieves superior performance to traditional methods, with significantly reduced computational overhead during inference. The SE percentage improvement with proposed approach is varying from 9–54% as compared to some exiting techniques. Moreover, the proposed approach is able to achieve approximately 92% of the fully digital precoding performance.