Generative AI-Enhanced Energy-Efficient Multi-UAV Collaborative Mobile Edge Computing
Bibliographic record
Abstract
Mobile edge computing (MEC) has emerged as a key enabler for low-latency and computation-intensive applications. However, conventional terrestrial MEC networks face challenges in coverage, scalability, and dynamic environments. To address these limitations, this paper explores unmanned aerial vehicle (UAV)-assisted MEC, leveraging the mobility and flexibility of UAVs to enhance edge computing performance. However, challenges such as dynamic mobile user association, multi-UAV collaborative trajectory design, and limited communication and computation resources remain significant hurdles. We then formulate a joint optimization problem for user association, trajectory design, and resource allocation for the multi-UAV collaborative MEC network, aiming to minimize system cost considering computation task demands and UAV resource constraints. The problem, modeled as a Mixed Integer NonLinear Program (MINLP), is reformulated as a decentralized partially observable Markov decision process (Dec-POMDP) to capture complex system dynamics. Afterwards, we propose a multi-agent generative-enhanced deep reinforcement learning method that integrates generative diffusion model (GDM) with multi-agent deep reinforcement learning (MADRL) to effectively handle high-dimensional action spaces and enable collaborative multi-UAV decision-making. Extensive simulations using a real-world mobility dataset demonstrate that the proposed method consistently outperforms three baselines in terms of time-average system cost, task completion delay, task satisfactory ratio, and energy consumption ratio when varying the number of UAVs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".