Resource Allocation, Trajectory Optimization, and Admission Control in UAV-Based Wireless Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this letter, we study the resource allocation and trajectory optimization for multi-UAV based wireless networks. Our design maximizes the number of admitted users while satisfying their data transmission demands, which formulates a mixed-integer nonlinear problem. To tackle its difficulty, we first introduce soft admission variables and propose an iterative algorithm to solve this admission maximization problem. Each iteration comprises two steps, namely soft admission maximization and user removal. Our method guarantees that the number of admitted users increases over iterations. Numerical results show that our algorithm outperforms the conventional approach based on block coordinate ascent and mixed-integer linear programming.
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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 | 0.000 |
| 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 it