Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity

Alexey Redyuk, Evgeny Averyanov, Oleg Sidelnikov, Mikhail Fedoruk, Sergei Turitsyn

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Article number8984221
Pages (from-to)1250-1257
Number of pages8
JournalJournal of Lightwave Technology
Issue number6
Publication statusPublished - 15 Mar 2020


  • Fiber nonlinearity compensation
  • machine learning
  • manakov equations
  • nonlinear signal distortions
  • optical communication system
  • perturbation-based detection technique


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