Abstract
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 Q 2-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.
| Original language | English |
|---|---|
| Article number | 8984221 |
| Pages (from-to) | 1250-1257 |
| Number of pages | 8 |
| Journal | Journal of Lightwave Technology |
| Volume | 38 |
| Issue number | 6 |
| Early online date | 5 Feb 2020 |
| DOIs | |
| Publication status | Published - 15 Mar 2020 |
Bibliographical note
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- Fiber nonlinearity compensation
- machine learning
- manakov equations
- nonlinear signal distortions
- optical communication system
- perturbation-based detection technique