Аннотация
We propose a modification of the conventional perturbation-based approach of fiber nonlinearity compensation that enables straight-forward implementation at the receiver and meets feasible complexity requirements. We have developed a model based on perturbation analysis of an inverse Manakov problem, where we use the received signal as the initial condition and solve Manakov equations in the reversed direction, effectively implementing a perturbative digital backward propagation enhanced by machine learning techniques. To determine model coefficients we employ machine learning methods using a training set of transmitted symbols. The proposed approach allowed us to achieve 0.5 dB and 0.2 dB Q2-factor improvement for 2000 km transmission of 11 × 256 Gbit/s DP-16QAM signal compared to chromatic dispersion equalization and one step per span two samples per symbol digital back-propagation technique, respectively. We quantify the trade-off between performance and complexity.
Язык оригинала | английский |
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Номер статьи | 8984221 |
Страницы (с-по) | 1250-1257 |
Число страниц | 8 |
Журнал | Journal of Lightwave Technology |
Том | 38 |
Номер выпуска | 6 |
DOI | |
Состояние | Опубликовано - 15 мар 2020 |