Multiagent UAV-Aided URLLC Mobile Edge Computing Systems: A Joint Communication and Computation Optimization Approach
Bibliographic record
Abstract
In this article, we consider a multiagent unmanned aerial vehicle (UAV)-aided system employing mobile edge computing (MEC) servers to satisfy the requirement of ultrareliable low latency communications (URLLCs) in intelligent autonomous transport applications. Our MEC architecture aims to guarantee quality-of-service (QoS) by investigating task offloading and caching implemented in the nearby UAVs. To enhance system performance, we propose to minimize the network energy consumption by jointly optimizing communication and computation parameters. This includes decisions on task offloading, edge caching policies, uplink transmission power, and the processing rates of users. Given the nonconvex nature and high computational complexity of this optimization problem, an alternating optimization algorithm is proposed, where the three subproblems of caching, offloading, and power allocation are solved in an alternating manner. Our simulation results demonstrate the efficacy of the proposed method, showcasing significant reductions in user energy consumption and optimal resource allocation. This work serves as an initial exploration of the transformative potential of cutting-edge technologies, such as UAVs, URLLC, and MEC, in shaping the future landscape of intelligent autonomous transport systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".