Underwater wireless optical communication utilizing low-complexity sparse pruned-term-based nonlinear decision-feedback equalization
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Bibliographic record
Abstract
The nonlinearity of the light-emitting diode (LED) in underwater wireless optical communication (UWOC) systems is considered the one major limiting factor that degrades the system's performance. Volterra series-based nonlinear equalization is widely employed to mitigate such nonlinearity in communication systems. However, the conventional Volterra series-based model is of high complexity, especially for the nonlinearity of higher-order terms or longer memory lengths. In this paper, by pruning away some negligible beating terms and adaptively picking out some of the dominant terms while discarding the trivial ones, we propose and experimentally demonstrate a sparse pruned-term-based nonlinear decision-feedback equalization (SPT-NDFE) scheme for the LED-based UWOC system with an inappreciable performance degradation as compared to systems without the pruning strategy. Meanwhile, by replacing the self/cross beating terms with the terms formed by the absolute operation of a sum of two input samples instead of the product operation terms, a sparse pruned-term-based absolute operation nonlinear decision-feedback equalization (SPT-ANDFE) scheme is also introduced to further reduce complexity. The experimental results show that the SPT-NDFE scheme exhibits comparable performance as compared to the conventional NDFE (nonlinear decision-feedback equalization) scheme with lower complexity (the nonlinear coefficients are reduced by 63.63% as compared to the conventional NDFE scheme). While the SPT-ANDFE scheme yields suboptimal performance with further reduced complexity at the expense of a slight performance degradation, the robustness of the proposed schemes in different turbidity waters is experimentally verified. The proposed channel equalization schemes with low complexity and high performance are promising for power/energy-sensitive UWOC systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it