Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls

Pedro Jorge Freire, Antonio Napoli, Bernhard Spinnler, Nelson Manuel Simoes da Costa, Sergei K. Turitsyn, Jaroslaw Prilepsky

Research output: Contribution to journalArticlepeer-review


This paper performs a detailed, multi-faceted analysis of key challenges and common design caveats related to the development of efficient neural networks (NN) based nonlinear channel equalizers in coherent optical communication systems. The goal of this study is to guide researchers and engineers working in this field. We start by clarifying the metrics used to evaluate the equalizers' performance, relating them to the loss functions employed in the training of the NN equalizers. The relationships between the channel propagation model's accuracy and the performance of the equalizers are addressed and quantified. Next, we assess the impact of the order of the pseudo-random bit sequence used to generate the-numerical and experimental-data as well as of the DAC memory limitations on the operation of the NN equalizers both during the training and validation phases. Finally, we examine the critical issues of overfitting limitations, the difference between using classification instead of regression, and batch-size-related peculiarities. We conclude by providing analytical expressions for the equalizers' complexity evaluation in the digital signal processing (DSP) terms and relate the metrics to the processing latency.

Original languageEnglish
Article number7600223
Number of pages24
JournalIEEE Journal of Selected Topics in Quantum Electronics
Issue number4
Early online date11 May 2022
Publication statusPublished - 2022

Bibliographical note

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see

Funding: This paper was supported by the EU Horizon 2020 program under the Marie
Sklodowska-Curie grant agreement 813144 (REAL-NET). JEP is supported
by Leverhulme Trust, Grant No. RP-2018-063. SKT acknowledges support of
the EPSRC project TRANSNET. (Corresponding Author: Pedro J. Freire)


  • Artificial neural networks
  • Equalizers
  • Fiber nonlinear optics
  • Neural network
  • Optical fiber amplifiers
  • Optical fibers
  • Symbols
  • Training
  • classification
  • coherent detection
  • nonlinear equalizer
  • optical communications
  • overfitting
  • pitfalls
  • regression


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