A Cooperative Recharging-Transmission Strategy in Powered UAV-Aided Terahertz Downlink Networks
Bibliographic record
Abstract
Considering that unmanned aerial vehicles (UAVs) with the advantages of flexible deployment and high maneuverability have the limited energy to provide long-term seamless coverage, a cooperative recharging-transmission strategy is studied in this paper for the powered UAV-aided terahertz (THz) downlink networks, where a UAV-to-user THz sub-band association scheme is proposed for the THz frequency transmission to eliminate interference. Based on this, the minimum requirements of the wireless charging window and THz-transmitting window are derived. A minimization problem of minimum timeslot requirement is formulated to jointly optimize UAV placement and UAV-to-user THz sub-band association. The optimization problem is non-convex, and we propose an alternating optimization solution via iteratively solving placement subproblem and association subproblem. Specifically, we resort to the coalescent method of interior point and gradient descent algorithms to optimize UAV placement. The UAV-to-user THz sub-band association is solved as a knapsack problem by the transformation of association matrix. Simulation results validate the solution and demonstrate that the proposed strategy can achieve quasi-optimal performance compared with other contrasting strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".