Joint Communication and Trajectory Optimization for Multi-UAV Enabled Mobile Internet of Vehicles
Bibliographic record
Abstract
Due to its flexibility and high maneuverability, Unmanned Aerial Vehicle (UAV) is able to quickly provide wireless connections to the ground vehicles in mobile environment. In this paper, a multi-UAV enabled mobile Internet of Vehicles (IoV) model is proposed, where the UAVs track to serve the mobile vehicles and send downlink information to the vehicles during the flight time. Considering the constraints of anti-collision and communication interference between the UAVs, the system throughput is maximized by jointly optimizing vehicle communication scheduling, UAV power allocation and UAV trajectory. The formulated non-convex optimization problem is separated into three subproblems, including communication scheduling optimization, power allocation optimization and UAV trajectory optimization, which can be solved by successive convex approximation (SCA). A joint iterative optimization algorithm of the three subproblems is put forward to get the optimal solution. Then, a fairness optimization problem is proposed to guarantee the fair communications for each vehicle. The numerical results reveal the excellent performance of the multi-UAV enabled mobile IoV by joint communication and trajectory optimization.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".