Secure UAV Relay under Jamming and Eavesdropping via Trajectory-Power Optimization
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are emerging as key enablers for adaptive connectivity in future sixth-generation (6G) networks, offering high mobility, flexible deployment, and enhanced line-of-sight coverage. However, the open nature of wireless communication and the elevated positioning of UAVs as a relay expose them to severe security threats, including jamming and eavesdropping. In this work, we propose a secure downlink communication framework where a UAV relay flying at a fixed altitude serves multiple ground users while contending with an active jammer and a passive eavesdropper. To enhance physical layer security, we jointly optimize the UAV’s trajectory and transmit power over a discrete time horizon to maximize the cumulative secrecy rate. The resulting non-convex optimization problem is solved using both a successive convex approximation (SCA) method and deep reinforcement learning (DRL). Simulation results demonstrate the optimized trajectories in various user topologies and shed light on the relationship between secrecy energy efficiency and maximal transmit power of the UAV relay as well as the number of users.
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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 | 0.002 |
| 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".