Research on Channel Modeling and Performance Optimization of RIS-assisted Massive MIMO Systems

With the rapid development of wireless communication technologies, Massive MIMO (Multiple Input Multiple Output) technology has attracted considerable attention because of its potential to enhance system capacity and spectrum efficiency. However, challenges such as signal coverage in non-line-of-sight (NLOS) scenarios and high-frequency channel fading limit its application in complex environments. This study focuses on integrating Reconfigurable Intelligent Surface (RIS) technology with Massive MIMO systems to enhance performance in such scenarios. The research constructs a channel model for a RIS-assisted Massive MIMO system and investigates the impact of RIS element configuration on system capacity and energy efficiency. Numerical simulations are conducted to evaluate the effectiveness of RIS in improving overall system performance. Convex optimization and deep learning algorithms are utilized to optimize RIS reflection matrices, adjusting the phase and amplitude of reflection elements to maximize channel gain. The results demonstrate that RIS significantly enhances system capacity, spectrum efficiency, and energy savings in both line-of-sight (LOS) and NLOS environments, providing practical insights for the deployment of RIS in future 6G networks.