On the Capacity of Buoy-Based MIMO Systems for Underwater Optical Wireless Links with Turbulence
Bibliographic record
Abstract
Absorption and scattering are traditionally considered as the most important factors to affect the performance of underwater optical wireless communications (UOWC). Recently, the theoretical models from free space optical (FSO) communications are applied to model the underwater turbulence, and the turbulence-induced fading may introduce fluctuations to the light intensity. However, the effect of turbulence on UOWC channels might be different from FSO channels due to the interference from absorption and scattering. In this work, we first introduce the log-normal distribution to represent the weak turbulence. After that, we deduce the average capacity of turbulent buoy- based multiple-input multiple-output (MIMO) systems. Numerical results demonstrate that turbulence will boost the average capacity under low transmitted signal-to-noise ratio (SNR) and reduce the average capacity when the transmitted SNR which is defined as the transmitted power divided by the noise at the receiver is sufficiently high enough. Besides, stronger turbulence exerts more influence on the capacity, and the increasing attenuation length will eliminate the effect of turbulence. Moreover, MIMO could offset the impact of turbulence-induced fading, which indicates that it cannot improve the capacity performance under low SNR but could bring positive effects when SNR becomes high.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".