Machine Learning Approaches for Reconfigurable Intelligent Surfaces: A Survey

Abstract

Next-generation wireless networks must handle a growing density of mobile users while accommodating a rapid increase in mobile data traffic flow and a wide variety of services and applications. High-frequency waves will perform an essential role in future networks, but these signals are easily obstructed by objects and diminish over long distances. Reconfigurable intelligent surfaces (RISs) have attracted considerable interest because of their potential to improve wireless network capacity and coverage by intelligently changing the wireless propagation environment. Consequently, RISs possess potential technology for the sixth generation of communication networks. Machine learning (ML) is an effective method for maximizing the possible advantages of RIS-assisted communication systems, particularly when the computational complexity of operating and deploying RIS increases rapidly as the number of interactions between the user and the infrastructure starts to grow. Since ML is a promising strategy for improving a network and its performance, the application of ML in RISs is expected to open new avenues for interdisciplinary studies as well as practical applications. In this paper, we extensively investigate the ML algorithms used in RISs. We provide a brief overview of RISs, a summary of ML methods with RIS architecture, and a comparison of the available methodologies to explain the combination of these two technologies. Moreover, the significance of open research topics is emphasized to provide sound research directions.

Keywords

• Reconfigurable intelligent surface
• machine learning
• deep learning
• federated learning
• reinforcement learning