Aerial-IRS-Assisted Securing Communications Against Eavesdropping: Joint Trajectory and Resource Allocation
Bibliographic record
Abstract
Intelligent reconfigurable surface (IRS) is an innovative and promising technology to achieve intelligent reconfigurable wireless environment, and thus, enables cost-effective and energy-efficient wireless communications. Due to the broadcasting nature of the wireless signals, the reflected signal in IRS-assisted wireless communications networks might suffer from eavesdropping. Thus, it is essential to tackle the secrecy aware problems in IRS-assisted wireless communications networks. In this article, we consider an aerial IRS (AIRS) assisted wireless relay network scenario, where IRS is mounted on the aerial platform. The artificial noise is added to interrupt the eavesdropping. A secrecy rate maximization problem is formulated subject to the total transmit power and reflecting phase shift constraints. To solve this problem, we first divide the secrecy maximization problem into three subproblems, i.e., transmit power allocation, AIRS trajectory design, and reflecting phase shift optimization. These three subproblems are solved alternately until convergence to maximize the secrecy rate. Especially, for the AIRS trajectory design and reflecting phase shift optimization, we employ the successive convex approximation (SCA) and positive semidefinite relaxation (SDR) technologies to convert the nonconvex optimization problems into convex problems, respectively. The intercept probability of the proposed optimal schemes is derived and the theoretical analyses show that the intercept probability can be reduced by increasing the numbers of IRS elements. Simulation results show that the joint optimization of transmit power, AIRS trajectory and reflecting phase shift can effectively improve the secrecy rate.
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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 | 0.000 | 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".