Nonlinear Signal-to-Noise Ratio Estimation in Coherent Optical Fiber Transmission Systems Using Artificial Neural Networks
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Bibliographic record
Abstract
For high symbol rate fiber optic networks, the estimation and monitoring of time varying link performance parameters are critical for delivering optimal network performance. In this paper, a method is presented for estimating the nonlinear signal-to-noise ratio (SNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nl</sub> ) using an artificial neural network (ANN). The ANN is trained with the fiber nonlinearity induced amplitude noise covariance and phase noise correlation extracted from received symbols. The data used for training are simulation results for a 34.5 Gbaud dual polarization 16-ary quadrature amplitude modulation signal transmitted over a wide range of link configurations with varying fiber types and number of wavelength division multiplexed channels. Using 734 input-output sets, high accuracy is demonstrated for training, testing, and validation for simulation data with a maximum normalized root-mean-square error of 0.37% for SNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nl</sub> . Validation using experimental data exhibits less than 0.25 dB deviation from the true SNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nl</sub> for estimates obtained with varying fiber length and launch power.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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