Efficient Queue-Aware Communication and Computation Optimization for a MEC-Assisted Satellite–Aerial–Terrestrial Network
Bibliographic record
Abstract
An integrated network combining satellite, aerial, and terrestrial components has generated interest in offering wireless communication services because of its high flexibility, adaptable deployment, and widespread connectivity. Moreover, mobile edge computing (MEC) has positioned itself as one of the promising techniques for enabling next generation mobile networks. Besides, unmanned aerial vehicle (UAV)-assisted MEC systems have evolved the edge computing strategy in the air. This work takes into account a multi-UAV satellite-aerial-terrestrial network where a satellite station and multiple UAVs jointly serve terrestrial mobile users with computing services. By simultaneously optimizing splitting and offloading of a task, remote server selection, transmit power, UAV path control, and CPU computation resource distribution, the goal is to maximize the network’s queue-aware efficiency to compute. A block descent method-based alternating iterative strategy is suggested to address the formulated complex mixed integer problem. To reduce computation time, the proposed solution breaks the whole UAV flight trajectory into shorter periods using a segment-by-segment methodology. The reported simulation results demonstrate that the suggested strategy outperforms many advanced methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".