Machine Learning-Enabled Optimization for Next-Generation Wireless Networks: A Survey of Intelligent Resource Management in RIS-Assisted Systems

Abstract

In recent years, reconfigurable intelligent surfaces (RISs) have been proposed as a promising disruptive technology for future wireless communication systems. RISs enable unprecedented dynamic and programmable control of the electromagnetic waves by integrating software-defined metasurfaces into wireless environments. When smartly configured with the phase shifts of incident signals, RIS systems have the potential to improve spectral efficiency, energy efficiency, coverage, security, and other wireless metrics without the need for additional transmit power or active radio frequency chains. However, optimizing RIS-assisted wireless networks is highly nontrivial, due to the high-dimensional search space, cascaded channel model, coupled design of active and passive beamforming, etc. Machine learning (ML), and in particular deep reinforcement learning (DRL), has shown great promise in addressing these challenges by providing intelligent, adaptive, and real-time resource allocation and control. In this survey, we present a comprehensive overview of the state-of-the-art Machine Learning (ML) empowered RISs, from the fundamentals to the various ML paradigms, including supervised learning, unsupervised learning and the DRL framework. We then discuss in details ML-based solutions for channel estimation, beamforming design, power allocation, and resource management in RIS-aided multiple access systems. We further survey recent advances in ML for mobile edge computing, federated learning, unmanned aerial vehicles (UAVs), and physical layer security with RISs. Finally, we discuss several open challenges and future directions to spur future research on ML-empowered RISs, including scalability, hardware impairments, and integration with future 6G wireless networks. Critically, we provide a substantive technical treatment of Explainable AI (XAI) for RIS scenarios, detailing how SHAP value attribution, Grad-CAM saliency mapping over RIS element indices, and Transformer attention maps can be applied to interpret black-box DRL policies and CNN-based models used for continuous phase-shift and beamforming optimisation, enabling operators to understand, trust, and debug ML-driven RIS control decisions.