Mitigation of distortions in fiber-optic communication systems using optical neural network-based equalizer
Bibliographic record
Abstract
A feedforward optical neural network (ONN) based on Silicon photonics nanocircuits is proposed for the equalization of linear and nonlinear impairments of fiber optic communication systems. The activation function is realized using a semiconductor saturable absorber mirror (SESAM) or a highly nonlinear waveguide (HNLW). The proposed ONN can be placed at the fiber optic link output just before the coherent receiver. The numerical simulation of a 28 GBaud quadrature phase shift keying (QPSK) signal over a long haul dispersion-managed fiber optic link showed that the performance can be significantly improved by using the ONN based on HNLW. The dominant impairment in the long haul dispersion managed link is the fiber nonlinearity. To illustrate that the proposed ONN can mitigate fiber dispersion, simulations of a short-haul fiber optic link consisting of a standard single mode fiber (SSMF) is carried out and results showed that the ONN based on SESAM is quite effective in mitigating the dispersive impairments of the fiber optic system.
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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 | 0.000 | 0.000 |
| 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".