RIS驱动的多样化网络环境资源分配策略：综合综述
RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review
AI总结：
本文综述RIS在SIMO、MISO、MIMO、HetNets、NOMA及THz、VC、UAV等网络中的资源分配方法，并讨论AI协同、方案比较与未来挑战。
摘要：
本综述全面考察了可重构智能表面（RIS）如何革新各类网络框架中的资源分配。文章首先通过概述RIS技术奠定理论基础，包括无源RIS、有源RIS以及同时透射与反射RIS（STAR-RIS）。综述核心聚焦于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。文中进一步探讨了RIS在复杂网络环境中的集成，例如异构无线网络（HetNets）和非正交多址（NOMA）框架。此外，综述研究了RIS在太赫兹（THz）网络、车辆通信（VC）和无人机（UAV）通信等先进通信领域的应用，强调RIS与人工智能（AI）之间的协同作用以提升网络效率。汇总表提供了对各种方案的比较性见解。综述最后总结了经验教训、未来研究方向和挑战，并强调了关键的开放性问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
