Service-Oriented Topology Reconfiguration of UAV Networks with Deep Reinforcement Learning
Bibliographic record
Abstract
The high mobility of UAVs makes it flexible to provide on-demand service function chains (SFCs) for users in a large geographic area where the terrestrial network is usually not available. Considering sequential and sparsely geographically distributed service requests, the UAV network topology should be dynamically adjusted to provide guaranteed quality of services. This is especially challenging since the UAV movement, data transmission, and virtual function deployment and computing are highly coupled. In this paper, we investigate the topology reconfiguring of UAV networks to construct SFCs by jointly programming multi-UAV trajectories intelligently. Specifically, the dynamic UAV-SFC construction problem is formulated to maximize the net benefit of constructing the SFC by optimizing the UAV trajectory. The net benefit is defined as the delayed benefit deducting energy consumption costs. Then, we propose a deep Q-network (DQN)-based algorithm for real-time decision-making of multi-UAV actions to program multi-UAV trajectories jointly. Simulation results show that our proposed approach of topology reconfiguration can significantly reduce the delay in completing services and save the energy consumption of UAVs.
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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) | 0.001 | 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 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".