Dual-UAV Aided Secure Dynamic G2U Communication
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV) communication is easily wiretapped by malignant nodes due to the broadcast nature of line-of-sight (LoS) wireless channels. To tackle this problem, this paper investigates a dual-UAV aided secure dynamic ground-to-UAV (G2U) communication system. By dynamic, we mean UAVs communicate with moving ground devices (GDs). Our objective is maximizing the sum secrecy rate by the joint optimization of UAV trajectory and GDs transmit power. To achieve it, we first formulate this nonconvex optimization problem as a Constrained Markov Decision Process (CMDP) under the constraints of UAV flying speed, initial and final locations, limited energy, and average transmit power. Then, a Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning algorithm is designed, named SC-TDPC, to learn the optimal transmit power and UAV trajectory. The experiment results demonstrate that, compared to other benchmark schemes, SC-TDPC can efficiently enhance the UAV communication security in terms of sum secrecy rate.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".