Machine Learning-Enhanced RIS Phase Optimization Using SVDBased Initialization and Momentum Gradient Descent in 6G

Reconfigurable Intelligent Surfaces (RIS) have emerged as a key enabler for 6G wireless communication systems due to their ability to manipulate the wireless propagation environment and enhance signal quality. However, conventional RIS phase optimization methods face critical challenges such as high computational complexity, limited scalability, and performance degradation caused by discrete phase constraints. In particular, alternating optimization with discrete phase search often results in slow convergence and suboptimal configurations, limiting the achievable throughput and energy efficiency. To address these challenges, this paper introduces a Machine Learning (ML)-enhanced RIS phase optimization framework for 6G networks. The proposed method leverages intelligent Singular Value Decomposition (SVD)-based initialization to provide an effective starting point for the optimization process, significantly reducing convergence time. Furthermore, a momentum-based gradient descent algorithm is employed to overcome local minima and accelerate the optimization, enabling near-optimal performance under practical discrete phase conditions. Simulation results demonstrate that the proposed ML-enhanced approach outperforms conventional alternating optimization in terms of convergence speed, spectral efficiency, and energy efficiency, highlighting its potential for next generation RIS-assisted 6G communication systems.