Energy Efficient Resource Allocation and Trajectory Design for Multi-UAV-Enabled Wireless Networks
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have been a provision for future wireless networks. However, limited backhaul capacity and power are bottlenecks for deployment and control of UAVs. To tackle these challenges, we propose a cache-enabled UAV networks to store popular files proactively to serve ground users and alleviate the backhaul burden. Taking account of the limited battery capacity, we propose an energy-efficient resource allocation and trajectory design algorithm to maximize the minimum achievable throughput among users. The formulated problem is a non-convex and mixed-integer optimization problem. To facilitate dealing with it, we decouple it into three subproblems and alternately solve them by jointly optimizing cache placement, transmit power, bandwidth allocation, and trajectory using successive convex approximation and block coordinate decent. The algorithm is proved to converge after finite steps of iterations. Numerical results reveal that our algorithm outperforms several baselines in terms of achievable throughput.
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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 | 0.000 |
| 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 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".