Reinforcement Learning-Assisting Secure Reliable Underwater Acoustic Communications
Bibliographic record
Abstract
In recent times, there has been an increasing deployment of autonomous underwater vehicles (AUVs) for tactical acoustic communications. This necessitates the establishment of secure and reliable AUV-links to safeguard sensitive information. However, existing methods such as cryptography and channel coding introduce extra overheads and computational complexity. This is primarily due to the inherent challenges posed by acoustic communications, such as limited bandwidth and low energy efficiency. To overcome these challenges, intelligent reflecting surfaces (IRSs) in conjunction with reinforcement learning (RL) techniques is proposed. This to facilitate simultaneous secure and reliable communications between AUVs and buoyed nodes, resulting in what is termed as RL-based Buoyed-IRS-AUV (RL-BIA) links. The RL-BIA link is engineered to dynamically adjust its beam width and IRS's depth in response to seawater turbulence induced by wind and tide speeds. In this paper, we introduce a comprehensive link model that includes; pointing errors, path loss, interference, and noise. Furthermore, an RL-based system model that integrates the RL technology into the BIA link is proposed. A Max-Min optimization problem is also formulated in this work which integrates the channel secrecy and outage probability, and solves it iteratively using the Q-learning and SARSA algorithms. Our numerical results exhibit the efficacy of the proposed approach, with the RL-BIA link achieving an impressive 500% increase in channel secrecy compared to an RL-based buoyed-AUV (RL-BA) 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".