Opportunistic Data Ferrying in UAV-Assisted D2D Networks: A Dynamic Hierarchical Game
Bibliographic record
Abstract
In this paper, we investigate the problem of distributed ferrying transmission in UAV-assisted device-to-device (D2D) communication networks. When drones are performing tasks with given trajectories, terrestrial communication devices can select them for loading data opportunistically, and then drones will offload the data to corresponding receivers in the appropriate later time. For the dynamic multi-device network, there are composite optimization problems including competition of drone selection, time allocation of data loading and offloading, as well as limited channel access. Due to the distributed feature, devices share resources through independent perception and decision making. Therefore, a dynamic hierarchical game is designed for the problem of joint UAV allocation and channel access. Specifically, a predictable dynamic matching market is constructed to address the problem of UAV selection and time allocation, while the problem of channel access is studied by the congestion game. Based on the game model, a distributed hierarchical algorithm is proposed and the property of convergence is discussed. Simulation results confirm that the effective selection of data ferrying approach can improve the transmission performance significantly, while unreasonable optimization approaches may lead to the decline of the transmission performance.
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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".