Improving Dual-UAV Aided Ground-UAV Bi-Directional Communication Security: Joint UAV Trajectory and Transmit Power Optimization
Bibliographic record
Abstract
This paper investigates a dual-unmanned aerial vehicle (UAV) aided communication system to improve the security of the communication between ground devices and UAVs. Different from the existing works which ignored ground devices mobility and just considered one-way communication security between ground devices and UAVs, we allow the devices to be mobile and consider bi-directional ground-UAV communication security. Specifically, one UAV server communicates with mobile ground devices, and the other UAV jammer is invoked to confuse eavesdroppers. Our objective is to maximize the worst-case average secrecy rate by the joint optimization of UAV trajectory and sender transmit power. To achieve it, we first formulate the worst-case average secrecy rate maximization problem as a constrained Markov decision process (CMDP) under the constraints of UAV flight space, flight speed, energy capacity, anti-collision, and peak transmit power. Then, we design a Deep Deterministic Policy Gradient (DDPG) based algorithm to solve the CMDP. Experiment results demonstrate that our joint optimization scheme can enhance the communication security in terms of the secrecy rate in both UAV-to-ground (U2G) case and ground-to-UAV (G2U) case. Besides, it is observed that UAV trajectory and sender transmit power have different impacts on the communication security in U2G case and G2U case.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".