Resource Block-Based Co-Design of Trajectory and Communication in UAV-Assisted Data Collection Networks
Bibliographic record
Abstract
This paper explores the joint optimization problem for trajectory planning and radio resource allocation in unmanned aerial vehicle (UAV) communications with the aim of maximizing data collection. Rather than decomposing the problem into subproblems, as most current approaches do, we express the quantity of data gathered by a UAV-assisted network as a function of both the size of the resource block allocated to all ground devices and their average upload rate. Based on this formula, it can be concluded that the problem of maximizing the average data collection can be reduced to minimizing the flight trajectory if each device communicates with the UAV within the maximum allowable coverage of the UAV. To address this issue, we propose an advanced hierarchical clustering algorithm that divides larger network-scale scenarios into many disjoint subregions to determine the initial hovering positions of the UAV. The non-convex minimization trajectory problem is decomposed into a series of convex optimizations to minimize path segments along the trajectory, based on the traveling salesman problem (TSP). Subsequently, the communication optimization process is modified to assign specific upload times for each device. The effectiveness of the optimization algorithm is demonstrated through extensive simulations, which show its superior performance in terms of average rates of data collection and upload failures.
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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".