Broadcast Secrecy Rate Maximization in UAV-Empowered IRS Backscatter Communications
Bibliographic record
Abstract
The backscatter communications (BackCom) and physical layer security are respected to realize extremely low-power secure communications in the imminent sixth generation (6G). In a BackCom system, the backscatter device without radio frequency components sends messages to users by reflecting the external signals. However, the double-fading effect limits BackCom’s performance and the commonly used broadcast mode is vulnerable to eavesdropping. Two promising technologies, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV), show excellent potential in handling these problems. In this paper, we propose a UAV-empowered IRS-BackCom network, where an IRS acts as the backscatter device and uses the received signals from a UAV for BackCom. We aim to guarantee secure transmission and maximize the broadcast secrecy rate by jointly optimizing the UAV’s beamformer and trajectory and the IRS’s reflection coefficient. To tackle the non-convex problem, we leverage the block coordinate descent method to decompose it into three subproblems. Specifically, the UAV’s beamformer and trajectory and the IRS’s reflection matrix are optimized alternatively. Further, we adopt reinforcement learning to facilitate the intractable UAV’s trajectory optimization. Simulation results verify the feasibility and effectiveness of the proposed system model and the optimization scheme.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".