On Integrated Stochastic Channel Model for Underwater Optical Wireless Communications
Bibliographic record
Abstract
Absorption, scattering and turbulence are the three main characteristics for underwater optical wireless communications (UOWC). Among these three factors, absorption and scattering, respectively, characterize the energy loss and direction change when photons propagate through underwater wireless channels interacting with water molecules or suspended particles. In recent years, several analytical methods originated from free space optical communications are used to model various underwater channels, while only statistical models are employed to consider the effects of absorption and scattering containing all the scattering components. To facilitate in-depth theoretical analysis, we propose an expression to model the spatial probability density function of optical intensity for all scattering components in UOWC links in this paper. After that, we model the photodiode receiving process and give a clear explanation on the related parameters. Numerical results indicate that the bit-error rate performance deteriorates as the turbulence gets stronger, and larger multiple-input multiple-output array can alleviate the negative effect caused by fading. Besides, the effect of turbulence on BER is more important than that of the link geometry, and the increase in transmitted power will weaken the diversity gain from MIMO configuration.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".