Machine Learning Strategies for Reconfigurable Intelligent Surface: An Overview

Abstract

Reconfigurable Intelligent Surface (RIS)-based Machine Learning (ML) methods have been extensively employed to enhance the performance of wireless communications networks. RIS has garnered significant interest due to its capability to both reflect signals and adjust their propagation directions in a greatly intelligent and efficient way, hence fostering the development of smart radio settings. The performance of the system may be considerably enhanced by investigating the ML-aided RIS in wireless communication networks, which can be a useful substitute to reduce the signal worsening in wireless networks. It is indispensable to identify the most effective solution in practice because there are so many different challenges such as system configurations and channel circumstances. This work provides a comprehensive study of RIS-based ML communications, an overview of several reviews that used RISs and ML to improve the overall performance of wireless communication networks as well as the most important future directions.