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.
@article{arxiv.2501.03075,
title = {RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review},
author = {Manzoor Ahmed and Fang Xu and Yuanlin Lyu and Aized Amin Soofi and Yongxiao Li and Feroz Khan and Wali Ullah Khan and Muhammad Sheraz and Teong Chee Chuah and Min Deng},
journal= {arXiv preprint arXiv:2501.03075},
year = {2025}
}