Secrecy Performance Analysis of Air-to-Ground Communication With UAV Jitter and Multiple Random Walking Eavesdroppers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Flexible mobility and random jitter are two unique features of UAV communication platforms. Although advantages of mobility have been extensively explored, the random jitter of UAV platforms, caused by airflow and body vibrations, has been rarely studied. This work aims to answer the two fundamental questions: i) how to analyze the secrecy performance when considering UAV jitter and ii) can this inherent characteristic of UAV be exploited to enhance secrecy? Detailed, we study the modeling and analysis of UAV jitter on the secrecy performance in an air-to-ground (A2G) wiretap system with multiple non-colluding eavesdroppers, where a UAV-mounted transmitter equipped with directional antennas illuminates ground terminals in a finite area. Random waypoint model is applied to characterize the mobility of eavesdroppers. To be specific, by modeling UAV jitter in both horizontal azimuth and vertical elevation, distortion and shift of UAV illumination area are analyzed. Further, beam-illumination probability of a randomly located ground terminal is obtained. Following which, a tractable framework for analyzing the secrecy coverage probability (SCP) and ergodic secrecy capacity (ESC) is developed. Expressions for SCP and ESC are derived with characterizations for the signal-to-noise ratio received at legitimate receiver and eavesdroppers. Finally, extensive simulations are provided to validate the theoretical analysis. This is the first work to find that UAV jitter can be exploited to enhanced secrecy performance of A2G wiretap system with appropriate UAV height and beamwidth.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it