Hybrid Beamforming for MU-MIMO in mmWave Communication using CNN Algorithm

The rapidly increasing demand for higher data rates and efficient spectrum utilization has spurred interest in millimeter-wave (mmWave) communication systems. This paper presents a hybrid beamforming approach for Multi - User Multiple - Input Multiple - Output (MU-MIMO) systems in the mmWave spectrum, utilizing a Convolutional Neural Network (CNN) to optimize beamforming strategies. Hybrid beamforming integrates digital and analog beamforming techniques, reducing hardware complexity while maintaining high spectral efficiency. The proposed method significantly enhances spectral efficiency, achieving up to 67% improvement over conventional hybrid beamforming and up to 50% improvement over hybrid beamforming with Maximum Ratio Transmission (MRT). These gains are attributed to CNN-based optimization, which effectively mitigates path loss in mmWave communication. Simulation results highlight that the hybrid beamforming model integrated with the MRT algorithm outperforms conventional methods by offering enhanced spectral efficiency, reduced hardware requirements, and lower computational complexity.