Inverse regular perturbation with ML-assisted phasor correction for fiber nonlinearity compensation

We improve an inverse regular perturbation (RP) model using a machine learning (ML) technique. The proposed learned RP (LRP) model jointly optimizes step-size, gain and phase rotation for individual RP branches. We demonstrate that the proposed LRP can outperform the corresponding learned digital back-propagation (DBP) method based on a split-step Fourier method (SSFM), with up to 0.75 dB gain in a 800 km standard single mode fiber link. Our LRP also allows a fractional step-per-span (SPS) modeling to reduce complexity while maintaining superior performance over a 1-SPS SSFM-DBP. (C) 2022 Optica Publishing Group

Automated summary

In this study, an inverse regular perturbation (RP) model is improved using a machine learning (ML) technique. The proposed learned RP (LRP) model optimizes step-size, gain, and phase rotation for individual RP branches. The results show that the proposed LRP outperforms the corresponding learned digital back-propagation (DBP) method based on the split-step Fourier method (SSFM), achieving up to 0.75 dB gain in an 800 km standard single mode fiber link. Additionally, the LRP allows for fractional step-per-span (SPS) modeling to reduce complexity while maintaining superior performance over the 1-SPS SSFM-DBP.