Joint Placement and Precoding Design for Aerial IRS Aided Secure Communication Networks
Bibliographic record
Abstract
In this paper, we propose a secure transmission scheme for aerial intelligent reflecting surface (IRS) assisted wireless networks. A multi-antenna access point (AP) serves multiple legitimate users in the presence of multiple eavesdroppers, whose precise positions are unknown. An IRS is carried by the unmanned aerial vehicle (UAV) to help establish virtual line-of-sight links between the AP and ground users, as well as ensuring the secure transmission. The hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS are jointly optimized to maximize the worst-case sum secrecy rate, subject to the minimum rate requirement of legitimate users. The non-convex optimization problem is decomposed into three subproblems, each of which is transformed into a convex one by utilizing successive convex approximation. An alternating optimization algorithm is applied to tackle the subproblems iteratively. Simulation results validate the effectiveness of the proposed scheme and the security enhancement by the joint optimization.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".