Review of deep learning applications on reconfigurable intelligent surfaces.

This survey comprehensively explores the incorporation of Reconfigurable Intelligent Surfaces (RIS) and Deep Learning (DL) in wireless networks, carefully analyzing their combined ability to enhance network performance and significantly shift efficiency and effectiveness in wireless communication systems. Remote Intelligent Sensing (RIS) can alter the propagation of electromagnetic waves in real-time, resulting in enhanced signal receipt and transmission efficiency. Simultaneously, deep learning (DL) can enhance these modifications by utilizing predictive analytics and intelligent decision-making. The collaboration between RIS (Radio Interface System) and DL (Deep Learning) has significantly improved important metrics such as signal strength, network capacity, and energy efficiency. Although there have been positive results, there are still obstacles to overcome, such as the intricate nature of RIS settings and the requirement for real-time DL models that can adjust. The survey also delineates prospective avenues for research, with a particular emphasis on developing sophisticated algorithms and streamlined hardware designs, as well as assessing security ramifications in networks strengthened by RIS technology. [ABSTRACT FROM AUTHOR]