Opportunistic Utilization of Dynamic Multi-UAV in Device-to-Device Communication Networks
Bibliographic record
Abstract
In this paper, we investigate the problem of opportunistic UAV transmission in D2D communication networks. UAVs are supposed to help transmissions of D2D users when they are employed to perform flying missions with given trajectories. On one hand, users can select appropriate UAVs as real-time relays according to the topology in the sky at different moments. On the other hand, due to flight characteristics, UAVs can receive the uploading data when they are approaching transmitters, and then offload the data to corresponding receivers in the appropriate later time. Users need to select and adjust transmission modes dynamically, including multi-UAV selection, time allocation of data loading and offloading, as well as the competition of channel access. We design a hierarchical game model to analyze the complicated relationship among devices. Specifically, a predictable dynamic matching market is constructed to address the issue of UAV selection and time allocation, while the problem of channel access is studied by the congestion game. After that, distributed algorithms are proposed and the properties of convergence are discussed. Simulation results confirm that the effective opportunistic UAV transmission approach can improve the global network 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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".