Use of Gaussian beam divergence to compensate for misalignment of underwater wireless optical communication links
Bibliographic record
Abstract
The vast majority of underwater wireless optical communication systems use collimated blue/green laser beams to deliver high‐speed data over a transmission range of a few metres to tens of metres. However, such systems are extremely susceptible to misalignment of the transmitter and the receiver. The mitigation techniques for misalignment reported in the literature are complex and costly at times. In this study, the authors consider the simple approach of increasing the divergence angle of the transmitted Gaussian beam to mitigate misalignment. Both plane and spherical beams are considered as the limitation cases. Using Monte Carlo simulations, the authors show that an optimum divergence angle for the maximum acceptable lateral offset exists with respect to the receiver sensitivity in clear waters while this is not an efficient method in harbour waters. Results demonstrate that there is a design trade‐off between acceptable lateral offset, power loss and channel bandwidth. Furthermore, the authors show how the proposed scheme of beam divergence affects the maximum allowed link span as well as the channel bandwidth for a given distance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".