UAV-Aided Covert Communication With a Multi-Antenna Jammer
Bibliographic record
Abstract
Owing to the flexible mobility and convenient deployment, unmanned aerial vehicle (UAV) aided networks can facilitate many applications. However, the open accessibility of wireless communication brings a great risk of privacy to UAV-based networks. Thus, in this paper, we propose a UAV-aided covert communication scheme assisted by a multi-antenna jammer to maximize the transmission rate between a ground transmitter and a UAV receiver against several randomly distributed wardens. The transmitter adopts the maximum ratio transmission, while the jammer zero-forces its transmitted signal at the UAV to disturb the monitoring at wardens without interfering the legitimate transmission. First, we analyze the detection performance and derive the optimal threshold for each warden to minimize its detection outage probability (DOP). Then, with the worst situation in which all wardens set their respective optimal thresholds to achieve the minimum global DOP, the location and the transmit power of the jammer are optimized to maximize the DOP. The location of UAV and the transmit power of the ground transmitter are also optimized to maximize the transmission rate with the minimum DOP requirement satisfied. Numerical results are provided to demonstrate the effectiveness of the proposed UAV-aided covert communication scheme.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".