Aerial Reconfigurable Intelligent Surfaces-Enabled Secured Wireless Communications: Performance Analysis and Optimization
Bibliographic record
Abstract
Integrating aerial reconfigurable intelligent surfaces (ARIS) with unmanned aerial vehicles (UAVs) presents a significant opportunity to enhance the performance of wireless networks. This integration allows ARIS to be mounted on UAVs, providing greater configuration flexibility, establishing reliable air-ground connections, and enabling three-dimensional signal reflections. However, this integration also introduces unique challenges related to physical layer security (PLS). Addressing these security considerations is essential, given the significance of secure and reliable communication. In this paper, we investigate the PLS for ARIS to assist wireless communication systems. Our objective is to select the ARIS that maximizes the secrecy capacity of the proposed system model. Two selection approaches are considered, namely, optimal and sub-optimal ARIS selection, and analytical expressions for the secrecy outage probability and probability of non-zero secrecy capacity over Nakagami-m fading channels are derived. Additionally, we examine the impact of varying the number of UAVs and the locations of the eavesdropper in practical scenarios. Moreover, the collaborative scenario is investigated, where all UAVs cooperate to improve secrecy transmission. Each ARIS reflects identical copies of the transmitted signal on the same time-frequency channel without mutual interference. The optimization problem of UAV locations and RIS phase shifts to maximize the secrecy capacity under specific constraints is formulated and addressed using an improved particle swarm optimization technique. These scenarios highlight the potential of ARIS in achieving secure and efficient wireless communications. Simulation results verify the analytical derivations, highlighting the critical role of selecting the ARIS in enhancing secrecy performance. As revealed by simulations, doubling the number of UAVs leads to a notable improvement in the average secrecy rate by approximately 77.78%. The obtained results highlight the significance of the proposed ARIS-assisted system in enhancing the PLS for wireless communications.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 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".