Performance Optimization of Hybrid Beamforming in MU-MIMO NOMA Systems with Reconfigurable Intelligent Surfaces

The application of MU-MIMO along with NOMA is an innovative approach for the challenge of next-generation wireless communication systems. This paper explores how Reconfigurable Intelligent Surfaces (RIS) can improve hybrid beamforming in MU-MIMO NOMA systems. In order to achieve these goals, the proposed framework uses the potential benefits of RIS to minimize signal transmission, reduce interference, and refine spectral efficiency. The study provides an analytical framework, which includes closed-form expressions of the Signal-to-Interference-plus-Noise Ratio (SINR), Bit Error Rate (BER), and ergodic capacity for hybrid beamforming. The proposed system performance is evaluated via extensive MATLAB simulations showing significant improvements in terms of data rates and energy efficiency, which are critical for practical deployment in future wireless networks. The results highlight the effectiveness of RIS-assisted hybrid beamforming in satisfying the severe 6G and beyond requirements of large connectivity, ultra-high reliability, and low latency. The proposed model is essential to optimize the resource allocation MU-MIMO NOMA system, leading to improved performance and effectively increasing the overall system capacity and end-user experience; furthermore, the study showcases RIS as a new enabler towards future wireless networks.