A survey on reconfigurable intelligent surfaces assisted multi-access edge computing networks: State of the art and future challenges
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".