An Energy-Efficient UAV Recharging and Reshuffling Strategy for Seamless Coverage
Bibliographic record
Abstract
Due to the easy deployment, low cost and high maneuverability, unmanned aerial vehicles (UAVs) serving as aerial base stations can be efficiently deployed according to realtime situations for providing high-quality coverage, which can improve the communication efficiency and meet the requirements of green communications. However, due to the finite flight energy, a single UAV has limited capability of providing seamless long-term service to ground users. Therefore, the cooperation of multiple drones relying on sophisticated recharging and reshuffling schemes is necessary. In this paper, we investigate an energy- efficient cooperation strategy of multi-UAVs for providing seamless long-term coverage, where the positioning and the flight strategy are jointly considered. We first introduce a novel UAV power model, based on which we derive the cyclic UAV recharging and reshuffling constraint in order to satisfy the seamless long-term coverage requirement. For maximizing the energy-efficiency, we introduce a two-stage joint optimization algorithm for solving both the optimal UAV deployment as well as the cyclic UAV recharging and reshuffling strategy (CRRS). Finally, the efficiency of our proposed algorithm is shown by the simulation results.
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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".