Machine Learning Model Training Framework for Nonlinear Signal-to-Noise Ratio Estimation in Heterogeneous Optical Networks
Bibliographic record
Abstract
A computationally efficient framework is presented for calculating features used to train machine learning (ML) models for estimating the nonlinear signal-to-noise ratio ($S\!N\!R_{N\!L}$) in heterogeneous optical networks. Data used for training and testing is obtained from a first-order perturbation analytical model, which is calculated for over 500,000 distinct system configurations, covering a wide range of system and channel compositions. System configurations vary based on span lengths, number of spans, number of wavelength division multiplexed (WDM) channels, channel spacings, modulation formats, shaping rates, and symbol rates. Five ML models have been trained using features extracted from the nonlinear phase noise generated by signal-signal interaction between WDM channels. Model comparison suggests that ensembles of regression trees produce highest estimation accuracy. A robust model building method is presented that aggregates important features from three ensemble models with boosting and shows universal application to all considered training cases. The impact from the extent of heterogeneity and training diversity in terms of number of WDM channels, symbol rate, and total distance covered in training is explored. Estimation results demonstrate the benefit of considering heterogeneous system configurations in training and indicate high accuracy and generalization potential to arbitrary system configurations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".