Queue-Aware Computation Efficient Optimization for MEC-Assisted Aerial-Terrestrial Network
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy, where a UAV equipped with a MEC server is deployed to serve on ground terminal devices. This paper considers a multi-UAV-assisted network in which multiple UAVs are deployed to provide MEC services to terrestrial users. The objective is to maximize the queue-aware computation efficiency of an aerial-terrestrial network by jointly optimizing task splitting, task offloading and MEC server selection, UAV trajectory, and CPU frequency allocation. The work utilizes Dinkelbach’s method and Lyapunov optimization to reformulate the defined problem. Moreover, an alternating iterative approach based on the block descent method is proposed to solve this mixed-integer problem. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.
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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 | 0.001 |
| 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".