RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review

TL;DR

This survey reviews how Reconfigurable Intelligent Surfaces (RIS) enhance resource allocation across diverse network types, including MIMO, HetNets, NOMA, and emerging domains like UAV and THz communications, highlighting future challenges.

Contribution

It provides a comprehensive overview of RIS technologies and their applications in optimizing resource allocation in various complex network environments, including integration with AI.

Findings

RIS significantly improves resource efficiency in MIMO systems.

RIS integration benefits heterogeneous and NOMA networks.

Challenges and future directions for RIS in advanced networks are identified.

Abstract

This comprehensive survey examines how Reconfigurable Intelligent Surfaces (RIS) revolutionize resource allocation in various network frameworks. It begins by establishing a theoretical foundation with an overview of RIS technologies, including passive RIS, active RIS, and Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The core of the survey focuses on RIS's role in optimizing resource allocation within Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO), and Multiple-Input Multiple-Output (MIMO) systems. It further explores RIS integration in complex network environments, such as Heterogeneous Wireless Networks (HetNets) and Non-Orthogonal Multiple Access (NOMA) frameworks. Additionally, the survey investigates RIS applications in advanced communication domains like Terahertz (THz) networks, Vehicular Communication (VC), and Unmanned Aerial Vehicle (UAV) communications, highlighting the synergy between RIS and Artificial Intelligence (AI) for enhanced network efficiency. Summary tables provide comparative insights into various schemes. The survey concludes with lessons learned, future research directions, and challenges, emphasizing critical open issues.