Joint Channel Allocation and Data Delivery for UAV-Assisted Cooperative Transportation Communications in Post-Disaster Networks
Bibliographic record
Abstract
As the natural disasters may destroy the ground communication infrastructures for the transportation systems, the communication relief in post-disaster networks is more crucial to reduce risk loss. The growing application of unmanned aerial vehicles (UAVs) holds great potential for disaster communication relief due to its flexibility and functionalities. In this paper, we investigate the channel allocation and data delivery problems for UAV-assisted cooperative transportation communications in post-disaster networks to provide communication and data delivery services for affected users. Specifically, we first introduce the UAV-assisted communication relief system, in which UAVs equipped with the communication and caching functionalities are deployed as the aerial base stations in post-disaster regions. Then, we propose the channel allocation scheme between UAVs and users by taking the interferences into consideration, and obtain the channel allocation strategy to improve the network throughput. Based on the optimal channel allocation strategy, users can deliver their data to UAVs for backup. Next, we propose the data delivery scheme to cope with the pricing problem for UAVs and the data delivery strategy for users to improve the efficiency of data delivery, with the objective of maximizing the utilities of both UAVs and users. The optimal strategy for both UAVs and users are derived according to the analysis of Stackelberg game. Finally, we conduct simulations to evaluate the performance of the proposed channel allocation and data delivery scheme, and the numerical results demonstrate that the proposed scheme can significantly improve the efficiency and effectiveness of channel allocation and data delivery in post-disaster networks, compared with benchmark schemes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".