Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

TL;DR

This paper reviews optimization and AI-based methods for designing phase shifts in RIS-enabled wireless networks, addressing the challenges of large-scale, constrained problems to improve system performance.

Contribution

It provides a comprehensive comparison of current optimization and AI methods for RIS phase shift design, highlighting future challenges and potential solutions.

Findings

Optimization methods vary in solution quality and complexity.

AI-based methods offer promising alternatives for large-scale problems.

Future research should focus on addressing constraints and scalability.

Abstract

Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources, such as beamformers, transmit power, and computation time. However, due to the unique constraints on the phase shift, and massive numbers of reflecting units and users in large-scale networks, the resulting optimization problems are challenging to solve. This paper provides a review of current optimization methods and artificial intelligence-based methods for handling the constraints imposed by RIS and compares them in terms of solution quality and computational complexity. Future challenges in phase shift optimization involving RISs are also described and potential solutions are discussed.