Dzieciol, Hubert;
Koike-Akino, Toshiaki;
Wang, Ye;
Parsons, Kieran;
(2022)
Inverse regular perturbation with ML-assisted phasor correction for fiber nonlinearity compensation.
Optics Letters
, 47
(14)
pp. 3471-3474.
10.1364/ol.460929.
Preview |
Text
UCL_RPS.pdf - Submitted Version Download (1MB) | Preview |
Abstract
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.
| Type: | Article |
|---|---|
| Title: | Inverse regular perturbation with ML-assisted phasor correction for fiber nonlinearity compensation |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1364/ol.460929 |
| Publisher version: | https://doi.org/10.1364/OL.460929 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Physical Sciences, Optics |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10154631 |
Archive Staff Only
 |
View Item |
