IRS-Aided Secure Reliable Underwater Acoustic Communications
Bibliographic record
Abstract
Recently, there has been a growing deployment of buoyed nodes on the sea surface for tactical acoustic communications with autonomous underwater vehicles (AUVs). This development necessitates the establishment of secure and reliable buoyed node-to-AUV links to safeguard sensitive information. However, existing methods such as cryptography and channel coding introduce latency and computational complexity, primarily due to the inherent challenges of acoustic communications, including limited bandwidth and low energy efficiency. To address these challenges, we propose implementing intelligent reflecting surfaces (IRSs) between buoyed nodes and AUVs, creating what we term buoyed-to-IRS-to-AUV (BIA) links. The BIA link enables secure and reliable communications by dynamically adjusting its beam widths and IRS depth in response to variations in wind and sea current speeds. In this paper, we introduce the BIA link topology, develop a comprehensive model for it, and derive a mathematical expression for the link's outage probability. Additionally, we formulate the ratio of channel secrecy rate in the MAX-MIN optimization problem and solve it using an exhaustive search method to ensure optimality. Our numerical results demonstrate the effectiveness of the proposed approach, with the BIA link achieving an impressive 300% increase in the secrecy rate compared to the buoyed-AUV link.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 0.001 |
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".