标题： 基于RIS的多样化网络环境资源分配策略：全面综述 显示英文标题

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

摘要： 这项全面的综述探讨了可重构智能表面（RIS）如何在各种网络框架中革新资源分配。它首先通过概述RIS技术建立理论基础，包括无源RIS、有源RIS和同时发射和反射RIS（STAR-RIS）。综述的核心部分聚焦于RIS在单输入多输出（SIMO）、多输入单输出（MISO）和多输入多输出（MIMO）系统中优化资源分配的作用。它进一步探讨了RIS在复杂网络环境中的集成，如异构无线网络（HetNets）和非正交多址接入（NOMA）框架。此外，综述还研究了RIS在先进通信领域中的应用，如太赫兹（THz）网络、车联网（VC）和无人机（UAV）通信，突出了RIS与人工智能（AI）之间的协同作用，以提高网络效率。总结表格提供了各种方案的比较见解。综述最后总结了经验教训、未来研究方向和挑战，强调了关键的开放问题。 显示英文摘要

摘要： 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.