Bibliographic record
Abstract
Abstract This paper examines artificial intelligence (AI) methods for compensating the distortion experienced in optical communication systems resulting from fiber nonlinearity. To identify the resulting degree of improvement afforded by machine learning methods, the procedures are applied to a model of a typical single frequency optical communication system with, a 3200km fiber length, double polarization, and a 16-QAM modulation format. The performance of transmitters and receivers that incorporate Neural Networks (NNs) are in particular examined for different values of the nonlinear coefficient \(\left(\gamma \right)\). Both of these are found to improve the system Q-factor for all values of\(\gamma\) although the degree of enhancement is dependent on the signal to noise ratio. The structures studied include Siamese neural networks (SNN) implemented at the receiver end and two-stage architectures that employ NNs at the transmitter together with a classifier at the receiver side. Here classifiers ranging from simple decision tree structures to boosting, forests, extra trees, and Multi-layer perceptron (MLP) were examined and found to provide significant enhancement for \(\gamma >\) \(4{W}^{-1}k{m}^{-1}\). The optimal performance for highly nonlinear systems was achieved with two-stage systems with random forest or extra tree AI methods at the receiver. Empirical equations are also presented for each AI technique that relates the Q-factor enhancement to \(\gamma\) and the computational resource requirements (the number of included triplet terms).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".