A MAPPO Based Scheme for Joint Resource Allocation in UAV Assisted MEC Networks
Bibliographic record
Abstract
Low latency has become a critical demand of smart mobile devices and many efforts have been dedicated to the mobile edge computing (MEC) technology. Due to the high mobility and low cost, unmanned aerial vehicles (UAVs) have been extensively employed in MEC systems to provide flexible computing service. It is still challenging to achieve low latency in multiple UAVs assisted MEC systems. This paper investigates the joint optimization of UAV association, channel selection, power allocation and computation resource allocation to minimize the system latency. The non-trivial joint optimization problem is formulated into a partially observable markov decision process. Then, an multi-agent proximal policy optimization (MAPPO) based scheme is proposed to solve it. Both the ground equipment and the UAVs are agents and they determine the joint strategy in a collaborative way. Numerical results validate the MAPPO based scheme and show its superiority to benchmarks.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".