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

2025-01-06

AI Summary

To address the lack of unified modeling and cross-layer optimization frameworks for reconfigurable intelligent surface (RIS) resource allocation in heterogeneous multi-network environments, this paper systematically surveys passive, active, and simultaneously transmitting-and-reflecting (STAR) RIS technologies. It proposes, for the first time, an AI-empowered cross-layer resource allocation paradigm applicable to twelve communication scenarios—including SIMO/MISO/MIMO, heterogeneous networks (HetNets), non-orthogonal multiple access (NOMA), terahertz (THz), vehicle-to-vehicle (V2V), and unmanned aerial vehicle (UAV) communications. The methodology integrates channel estimation, joint beamforming with reflection/transmission coefficient optimization, and AI-driven dynamic scheduling into a unified framework. Five fundamental optimization principles are distilled, and seven critical open challenges are identified. The work provides theoretical foundations and a taxonomy-based evaluation framework for RIS standardization and 6G intelligent air interface design.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Security and Privacy: Security and privacy of machine learning and AI applications

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.

Problem

• Reconfigurable Intelligent Surfaces (RIS)
• Resource Allocation Optimization
• Advanced Communication Networks

Innovation

• RIS Technology
• Resource Allocation Strategies
• AI Integration in Networking