Aerial IRS-Enabled Secure Mobile Communications: Joint 3-D Trajectory and Beamforming Design
Bibliographic record
Abstract
This letter investigates a novel aerial intelligent reflecting surface (IRS)-assisted secure mobile communication system. In particular, the IRS is mounted on a unmanned aerial vehicle (UAV) to help a source transmit its confidential messages to a legitimate mobile user in the presence of a mobile eavesdropper. The aerial IRS can adjust its trajectory and phase-shift to track the moving user and provide safer communication services. Furthermore, due to the mobility of the UAV, user and eavesdropper, the effect of Doppler shifts is also taken into consideration in the channel model. Under such a setup, we formulate an average secrecy rate maximization problem to jointly optimize the 3D trajectory of the UAV and the phase-shift matrix of the aerial IRS. To deal with this non-convex optimization problem, we decompose the original problem into two subproblems and propose an iterative algorithm to determine its suboptimal solution. Numerical results show that the proposed aerial IRS-assisted 3D joint design can significantly improve the secrecy rate compared to the benchmark schemes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".