Joint Estimation of Linear and Nonlinear Coherent Optical Fiber Signal-to-Noise Ratio
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Bibliographic record
Abstract
This letter proposes an estimator based on the neural network (NN) to jointly estimate the linear and nonlinear signal-to-noise ratios. The proposed NN-based estimator utilizes new input features based on the entropy extracted from the received signal. Moreover, the computational complexity of the proposed estimator is analyzed. The dataset utilized for training and testing is constructed from dual-polarization 16-ary quadrature amplitude modulation format over different system configurations of the standard single-mode fiber, such as launch power, transmission distances, and the number of wavelength division multiplexed channels. Numerical results reveal the superiority of the proposed NN-based estimator in terms of accuracy and computational complexity compared to the existing NN-based estimators in the literature.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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