Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
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Uncontrolled Keywords: Fiber nonlinearity compensation,machine learning,manakov equations,nonlinear signal distortions,optical communication system,perturbation-based detection technique,Atomic and Molecular Physics, and Optics
Publication ISSN: 1558-2213
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Related URLs: https://ieeexpl ... cument/8984221/ (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-03-15
Published Online Date: 2020-02-05
Accepted Date: 2020-02-01
Authors: Redyuk, Alexey
Averyanov, Evgeny
Sidelnikov, Oleg
Fedoruk, Mikhail
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)



Version: Accepted Version

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