Priority‐based resource allocation in wireless powered UAV‐assisted networks
Bibliographic record
Abstract
Abstract Unmanned aerial vehicles (UAVs) can be deployed to combine terrestrial and aerial networks to provide flexible connectivity to a large number of devices in three‐dimensional (3D) space. Powering wireless devices through energy harvesting and wireless power transfer have been investigated to provide uninterrupted operations. Resource allocation is critical to improving the overall performance of the network. This paper focusses on a downlink network in which UAVs serve as base stations to provide connectivity to ground users. A priority‐based charging of UAVs from the ground charging stations needs to be efficiently designed for the sustainable operation of the overall network. A binary linear integer programming (BLIP) problem is formulated to minimise the charging cost and maximise the number of UAVs that can be charged by charging stations. First, the BLIP is transformed into a quadratic programming problem to solve it in polynomial time. A sequential quadratic programming algorithm is developed to solve the optimisation problem. Simulation results demonstrate the effectiveness of the proposed work compared to existing solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".