Design and Performance Analysis of Massive MIMO Modeling with Reflected Intelligent Surface to Enhance the Capacity of 6G Networks

Abstract

Reflected Intelligent Surface (RIS) can establish a virtual line-of-sight link to enhance system performance while consuming less power in non-line-of-sight. RIS technique is utilized to minimize the spreading of electromagnetic signals by modifying the surface's electric and magnetic field characteristics. RIS can be adopted with the existing environment to effectively increase efficiency by modifying the radio channel characteristics. The signals are steered to the receiver using an RIS thus improving the link quality. The radio channel is viewed in traditional wireless systems as an uncontrollable entity that typically tampers with transmitted signals. This paper suggests an alternate theoretical model after examining the prevalent models and potential issues. Security of the wireless communication physical layer can be improved with an RIS by just changing the phase of the reflective unit. In this work, the massive Multiple Input Multiple Outputs (MIMO)-based iterative modeling technique is used to build the transmitter, channel, and receiver section. This will simultaneously optimize the RIS passive beamforming pre-coding matrix and thus avoid multi-user interference. At the base station cyclic programming is adopted, it iteratively calculates the active and passive beam-forming RIS matrices. The simulation results demonstrate that the combined pre-coding technique used in this work improves the weighted sum rate, efficiency, and coverage ratio. The results show that MIMO with continuous phase shift RIS achieves a higher Weighted Sum Ratio (WSR) and minimum Channel State Information (CSI) error in comparison with the random phase shift RIS. The performance metrics sum rate, signal-to-interference plus noise ratio (SINR), energy efficiency, and transmit power are analyzed and compared.