Time-Domain Neural Network Receiver for Nonlinear Frequency Division Multiplexed Systems

Rasmus Thomas Jones*, Simone Gaiarin, Metodi Plamenov Yankov, Darko Zibar

*Corresponding author for this work

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The nonlinear Fourier transform is a new approach of addressing the capacity limiting Kerr nonlinearities in optical communication systems. It exploits the property of integrability of the lossless nonlinear Schrödinger equation and thus incorporates nonlinearities as an element of the transmission. However, practical links employing erbium-doped fiber amplifiers include losses/gains and introduce noise which breaks the integrability of the nonlinear Schrödinger equation. Although the lossless path average approximation proposes an integrable model, its imprecision still leads to unintended distortions and thus performance degradation. We propose an alternative receiver for nonlinear frequency division multiplexing optical communication systems using techniques from machine learning. It is highly adaptive as it learns from previously transmitted pulses and thus holds no assumptions on the system and noise distribution. The detection method presented is fully applied in time-domain and omits the nonlinear Fourier transform. The numerical results provide a benchmark for nonlinear Fourier transform based detection of high order solitons for fiber links with losses and noise present.
Original languageEnglish
JournalI E E E Photonics Technology Letters
Issue number12
Pages (from-to)1079 - 1082
Publication statusPublished - 2018


  • Optical fiber communication
  • Coherent communication
  • Machine Learning
  • (inverse) Nonlinear Fourier transform


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