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. In addition, since machine learning (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 and 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.

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

“…RIS is an electromagnetic (EM) material surface which has the ability to electronically control the electromagnetic wave propagation [31]. This technology is able to address the spectrum scarcity caused by the obstacles the high-frequency EM signals face.…”

“…ML is adopted in channel estimation, beamforming development, resource management, detection and security-based operations. In [31], several ML based approaches and their adaptation in RISbased networks have been surveyed. An RL-based scheme is used to maximize system throughput under the consideration of precise and imperfect CSI [32].…”

“…Beamforming strategies with DL based implementations can help in adjusting phase shifts and phase optimization in RIS and minimizing RIS-based network model complications. Resource management, symbol detection and security concerned RIS-based networks use DL, DRL and FL based algorithms, which enhance the probability of effective deployment of such networks [31].…”

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

“…For instance, in [21], the authors presented the FL idea and investigated its important system elements, i.e., information distribution, ML model, confidentiality mechanism, and network architecture. Particularly, deep learning (DL) provides a new method to construct beyond 5G (B5G) air interface by optimizing the smart radio environment (SRE), wireless communication techniques, computer hardware, and IoT services in a unified manner [22]. These have stimulated the current applications of DL to information theory (e.g., source-channel coding), and semantic communication.…”

IRS-User Association in IRS-Aided MISO Wireless Networks: Convex Optimization and Machine Learning Approaches

“…2) ML-based WCNs: Recently machine learning (ML) approach is investigated for the non-trivial optimization problems that involve extremely high dimensional optimizations such as large-scale multiple-input multiple-output (MIMO) systems with a massive number of array elements [11], [12], resource allocation of the relay-aided communication with coupled parameters [13], etc. Likewise, when the number of interactions between the user and the infrastructure increases rapidly due to the large-scale deployment of the IRS in WCNs, the trained ML can be used for significantly reduced complexity and computational time [14]. The application of ML approaches in IRS-aided WCNs has been studied in some works, such as channel estimation [15], [16], beamforming [17], energy efficiency [18], and security [19].…”