Parameter Optimization for an Underwater Optical Wireless Vertical Link Subject to Link Misalignments
Bibliographic record
Abstract
We consider transmitter–receiver (Tx–Rx) parameter optimization for a vertical underwater wireless optical communication link under misalignment conditions. The Tx is assumed to use a light-emitting diode, whereas the Rx uses a silicon photomultiplier that allows a high Rx sensitivity. We consider two typical application scenarios and investigate optimization of the Tx and Rx parameters to optimize the link performance in terms of outage probability. We derive accurate analytical expressions for the distribution of the channel attenuation accounting for link misalignments, which allows for the evaluation of the link performance in terms of bit-error-rate and outage probability without resorting to costly Monte Carlo simulations. Through numerical results, we show and discuss the impact of parameter optimization on the link performance in different link configurations and for various misalignment conditions. In particular, the presented results show the significant performance improvement resulting from optimizing the Tx beam divergence and the Rx field-of-view in the presence of pointing errors.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".