Joint Optimization of UAV Trajectory and Radio Resource Allocation for Drive-Thru Vehicular Networks
Bibliographic record
Abstract
In recent years, providing connectivity to fast-moving vehicles on highways has been the focus of the wireless research community. In this paper, in the context of V2X, we propose using unmanned aerial vehicles (UAVs) to serve vehicles on a highway, where a UAV is dispatched in disaster situations (such as floods or earthquakes) to serve these vehicles, or to provide better coverage when vehicles are out of reach of road side units. We consider free flow scenario where vehicles moving between two road-side units and where the infrastructure is partially or totally unavailable. Our goal is to guarantee a certain Quality of Service (QoS) for each vehicle on the highway by jointly optimizing the UAV trajectory and the radio resource allocation. We show that during the UAV flight time, the UAV adapts its velocity to the velocities of the vehicles in the served cluster, to maximize the minimum average rate for each vehicle. Our findings are verified through Monte-Carlo simulation where we demonstrate the effectiveness of our proposed design under different UAVs types.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".