Learning-Based Reliable and Secure Transmission for UAV-RIS-Assisted Communication Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, combines the benefits of these two techniques, which can further improve the communication performance. However, high-quality air-ground channel links are more vulnerable to both the adversarial eavesdropping and the malicious jamming. Therefore, this paper proposes a reliable and secure communication approach assisted by the UAV-RIS to maximize the secrecy rate, while ensuring the quality of service (QoS) requirement of the legitimate user against both the eavesdroppers and the jammer. Specifically, with the imperfect channel state information and behaviors of mixed attacks, we try to maximize the achievable worst-case secrecy rate by jointly designing the transmit beamforming, artificial noise, UAV-RIS placement, and RIS’s passive beamforming. As the optimization problem is non-convex and the environment is highly dynamic, a post-decision state deep Q-network combined with Fourier feature mapping algorithm (called PDS-DQN-FFM) is further designed to effectively achieve the robust anti-attack transmission strategy. Simulation results demonstrate that our proposed learning based reliable and secure transmission approach significantly enhances both the secrecy rate and QoS satisfaction level as compared with existing approaches.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it