Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity

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.

Publication DOI: https://doi.org/10.1109/JLT.2020.2971768
Divisions: Engineering & Applied Sciences > Electrical, Electronic & Power Engineering
Engineering & Applied Sciences > Aston Institute of Photonics Technology
Engineering & Applied Sciences
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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
Full Text Link:
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. ( 0000-0003-0101-3834)

Download

[img]

Version: Accepted Version

| Preview

Export / Share Citation


Statistics

Additional statistics for this record