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.

翻译：本全面综述探讨了可重构智能表面（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（包括无源RIS、有源RIS以及同时透射反射RIS（STAR-RIS））建立理论基础。综述的核心聚焦于RIS在单输入多输出（SIMO）、多输入单输出（MISO）与多输入多输出（MIMO）系统内优化资源分配的作用，并进一步探究RIS在异构无线网络（HetNets）与非正交多址接入（NOMA）框架等复杂网络环境中的集成应用。此外，综述还考察了RIS在太赫兹（THz）网络、车联网（VC）及无人机（UAV）通信等先进通信领域的应用，着重阐明了RIS与人工智能（AI）协同提升网络效能的潜力。总结性表格提供了对不同方案的对比分析。最后，综述归纳了经验教训、未来研究方向与挑战，并强调了亟待解决的关键开放性问题。