RIS-Empowered Rate-Splitting Multiple Access Toward 6G and Beyond Wireless Communication Networks: A Comprehensive Survey

In light of the revolutionary requirements of the sixth generation (6G) and beyond wireless networks, reconfigurable intelligent surface (RIS) and rate-splitting multiple access (RSMA) have emerged as pivotal technologies due to their potential for improving spectral efficiency, user fairness, and interference management. This survey explores the theoretical foundations, architectural frameworks, and design strategies of RIS-assisted RSMA, emphasizing the combined adaptability of RIS’s wireless propagation control and RSMA’s multi-user flexibility for dynamic spectrum management. The article first discusses the fundamental concepts of RSMA and RIS technologies. Then, we investigate various enabling technologies for RIS-RSMA networks, highlighting key advancements in interference mitigation, energy efficiency, and security for future networks. Subsequently, some optimization techniques crucial for enhancing RIS-RSMA network performance are presented. Additionally, we examine advanced machine learning (ML) approaches that enable RIS configurations to dynamically adapt to changing network requirements. Techniques such as deep reinforcement learning support real-time adjustments, creating more scalable and resilient RIS-RSMA architectures. Finally, we discuss open research directions for advancing RIS-assisted RSMA in emerging 6G applications. We also consider the potential of advanced ML techniques, including quantum-based ML and large language models, to handle the complexities of large-scale network optimization. This comprehensive survey addresses critical challenges and current advancements. It offers a roadmap for future research in RIS-assisted RSMA networks, paving the way for robust, intelligent, and adaptive 6G wireless communication systems.