Task-Driven Relay Assignment in Distributed UAV Communication Networks
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Bibliographic record
Abstract
In this paper, we study the distributed relay assignment problem in multi-channel multi-radio unmanned aerial vehicle (UAV) communication networks. Multi-UAVs are driven by the overall task and fly in certain formation, where UAVs with different tasks have various transmission requirements. Source UAVs equipped with multi-radio can select more than one relay radios to achieve high data rate, and each relay radio can be shared by multiple source UAVs. We construct distributed game models to promote the global transmission performance by self-organizing coordination among UAVs. Specifically, the channel competition relationship between relay UAVs is modeled as a congestion game model, while the task-driven relay selection among UAVs is modeled as a many-to-many matching market without substitutability. With the proposed game models, the optimizing of local optimized process will lead to the improvement of global transmission results. After that, we design algorithms for the stable and changeable topology structures, respectively. Based on the given formation shape of UAVs, a learning matching algorithm is proposed to reach the optimum result with a large probability. A fast potential matching algorithm is propose to deal with the topological change of UAV networks. We prove that two proposed algorithms can both achieve the stable matching results. Simulation results show that the proposed relay assignment approaches yield good performances in terms of the global transmission satisfaction and fairness. Particularly, the result of the learning algorithm is close to the global optimum and the fast potential matching approach is robust to the perturbation of UAV networks.
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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 | 0.001 |
| 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 it