Journal
JOURNAL OF LIGHTWAVE TECHNOLOGY
Volume 38, Issue 6, Pages 1250-1257Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JLT.2020.2971768
Keywords
Fiber nonlinearity compensation; manakov equations; machine learning; nonlinear signal distortions; optical communication system; perturbation-based detection technique
Funding
- Russian Science Foundation [17-72-30006]
- EPSRC Programme [TRANSNET EP/R035342/1]
- Russian Science Foundation [17-72-30006] Funding Source: Russian Science Foundation
- EPSRC [EP/R035342/1] Funding Source: UKRI
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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 x 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.
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