Jamming-Enhanced Secure UAV Communications With Propulsion Energy and Curvature Radius Constraints
Bibliographic record
Abstract
A multi-unmanned aerial vehicles (UAVs)-aided secure communication network is studied, where multiple information UAVs carrying temporary aerial base stations transmit confidential information to multiple authorized receivers (ARs), and a jammer UAV is employed to send artificial noise to multiple unauthorized receivers (URs) for mitigating information leakage. Some practical constrains including the inter-UAV interference among the information UAVs and the jammer UAV, the maximal available propulsion energy at UAVs, and the curvature radius limitations of UAVs' trajectories are concurrently taken into account. In order to maximize the minimum secrecy rate among the ARs, the AR scheduling, the UAVs' power allocation and the trajectories of UAVs are jointly optimized by formulating a multi-variable optimization problem. To solve the non-convex problem efficiently, we present a block coordinate descent (BCD)-based approach, where the penalty dual decomposition (PDD)-based algorithm is designed to optimize the AR scheduling, and successive convex approximation (SCA)-based algorithm is proposed to optimize the UAVs' power allocation and the trajectories of UAVs. The complexity of the presented BCD-based approach is analyzed, which achieves polynomial complexity. Simulation results show that imposing curvature radius limitations on the UAVs' trajectory design is effective to avoid sharp turning of UAVs. Moreover, as curvature radius increases, the system max-min secrecy rate decreases. Besides, the secrecy rate performance of the system can be enhanced by exploiting one jammer UAV to interfere with the URs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".