A survey on reconfigurable intelligent surfaces assisted multi-access edge computing networks: State of the art and future challenges

Abstract

This survey provides a comprehensive analysis of the integration of Reconfigurable Intelligent Surfaces (RIS) with edge computing, underscoring RIS’s critical role in advancing wireless communication networks. The examination begins by demystifying edge computing, contrasting it with traditional cloud computing, and categorizing it into several types. It further delves into advanced edge computing models like Multi-Access Edge Computing (MEC), Vehicle Fog Computing (VFC), and Vehicle Edge Computing (VEC) and challenges. Progressing deeper, the survey explores RIS technology, categorizing it into passive, active, and hybrid RIS, and offers an in-depth analysis of Beyond Diagonal RIS (BD-RIS), including reflective, transmissive, and Simultaneous Transmit and Reflect (STAR) modes. Subsequently, the study assesses RIS’s applications within edge computing, revealing its diverse use cases and strategies for performance analysis. The discussion comprises how RIS-driven computation can elevate rates, reduce latency, and contribute to an eco-friendly edge computing approach through better Energy Efficiency (EE). The survey also scrutinizes RIS’s role in bolstering security within edge computing. To aid comprehension, each subsection is complemented by summary tables that meticulously elaborate on, compare, and evaluate the literature, focusing on aspects like system models, scenarios, RIS details, Channel State Information (CSI), offloading types, employed schemes, methodologies, and proposed solutions. This organized approach ensures a cohesive and thorough exploration of the survey’s diverse topics. By illustrating the synergy between RIS and edge computing, the study provides valuable insights or lessons learned for enhancing wireless networks, paving the way for future breakthroughs in communication technologies. Before conclusion, the survey also identifies ongoing challenges and future research directions in RIS-assisted edge computing, emphasizing the vast potential of this field.