Security Enhancement of ISAC via IRS-UAV
Bibliographic record
Abstract
Despite its advantage of improving the spectrum and hardware efficiency, integrated sensing and communication (ISAC) system is susceptible to eavesdropping due to the open nature of wireless channels. In this paper, we investigate the secure transmission of ISAC aided by an intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV). Moreover, assuming that an aerial target is a potential eavesdropper, the artificial noise is introduced to disrupt the eavesdropping, while enhancing the sensing signal-to-noise ratio and the users’ quality of service. Aiming to maximize the sum secrecy rate, we jointly optimize the UAV deployment, BS transmit beamforming, artificial noise power and passive beamforming. The formulated non-convex problem is decomposed into three subproblems and solved via an iterative alternating optimization algorithm. Specifically, we introduce auxiliary variables to transform the non-convex subproblems into convex ones. For the UAV deployment solution, it can be obtained by successive convex approximation. With the optimal UAV deployment, the BS transmit beamforming, artificial noise power and passive beamforming can be derived by semi-definite relaxation. Finally, we present simulation results to validate the performance improvement of the proposed scheme on the security of ISAC.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".