A Reinforcement-Learning-Based Beam Adaptation for Underwater Optical Wireless Communications
Bibliographic record
Abstract
Underwater optical wireless communications (UOWCs) have recently appeared as an attractive solution for many applications, such as remote controlling and sensing due to its advantages, such as high transmission rate, ultrawide bandwidth, and low latency. However, due to the harsh underwater conditions, UOWC faces challenges, such as water absorption, scattering, and pointing-acquisition-and-tracking (PAT) problems. This is mainly due to the dynamicity existing underwater. Consequently, this leads to packet loss and hence deteriorates the reliability and link quality of such networks. Such a problem can affect the degree of connectivity and end-to-end (E2E) performance of the communication system. The existing solutions in the literature are based on predefined models, assuming full knowledge of the environment. However, such models do not optimally treat the dynamicity existing underwater. This article proposes novel beam adaptation methods based on reinforcement learning (RL) for point-to-point UOWC. The first method aims to optimize the light beamwidth; the second method focuses on adapting the beam orientation, whereas the last one optimizes both the light’s beamwidth and beam orientation. Our proposed RL-based solutions yield optimal positioning and beamwidth of the light source and improve the considered communication link’s success rate. They also guarantee better link quality in terms of signal-to-noise ratio (SNR) compared to the uncertainty disk static method for four different underwater environments, including pure seawater, clean ocean, coastal ocean, and turbid harbor.
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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 | 0.000 | 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".