From Nodes to Roads: Surveying DRL Applications in MEC-Enhanced Terrestrial Wireless Networks
Bibliographic record
Abstract
The rapid evolution of mobile communication technologies has propelled mobile edge computing (MEC) as a pivotal paradigm bringing cloud capabilities and storage resources to the network edges, thereby, enabling the execution of computation-intensive, latency-sensitive applications at the network edge, and addressing limited device resources. However, efficient operation in MEC-assisted systems necessitates proper task executions onto MEC servers. Meanwhile, deep reinforcement learning (DRL) can substantially enhance the performance of MEC-enhanced networks by incorporating decision-making capabilities into individual network entities and edge servers. This paper presents a comprehensive survey of the applications of DRL in MEC ecosystems. More specifically, it explores the applications of DRL in MEC-enabled terrestrial wireless networks (TWNs) including Internet-of-things (IoT) and vehicular networks (VNs). The article provides a comprehensive roadmap for researchers navigating the complexities of intelligent systems and MEC-enabled networks, offering a meticulous understanding of the continuously evolving landscape in this domain. Beginning with foundational DRL principles, the survey scrutinizes the integration of DRL in MEC-enabled TWNs, showcasing its efficacy in optimizing modern TWNs. In the context of MEC-empowered IoT, the paper highlights the role of DRL in enhancing resource allocation, data management, and scalability enhancements. Extending beyond, the paper discusses MEC-enabled VNs, where DRL transforms its role in traffic signal control, and route optimization, ultimately improving efficiency and safety. Additionally, we highlight significant challenges and outline future research directions in applying DRL in terrestrial networks (TWNs) empowered by the MEC paradigm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".