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

Abstract

This paper performs a detailed, multi-faceted analysis of key challenges and common design caveats related to the development of efficient neural networks (NN) nonlinear channel equalizers in coherent optical communication systems. Our study aims 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.

Publication DOI: https://doi.org/10.1109/JSTQE.2022.3174268
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 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)
Uncontrolled Keywords: Artificial neural networks,Equalizers,Optical fibers,Symbols,Training,Fiber nonlinear optics,Optical fiber amplifiers
Publication ISSN: 1558-4542
Full Text Link:
Related URLs: https://ieeexpl ... cument/9773034/ (Publisher URL)
PURE Output Type: Article
Published Date: 2022-05-11
Published Online Date: 2022-05-11
Accepted Date: 2022-05-01
Authors: Freire, Pedro Jorge (ORCID Profile 0000-0003-3145-1018)
Napoli, Antonio
Spinnler, Bernhard
Costa, Nelson Manuel Simoes da
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)
Prilepsky, Jaroslaw (ORCID Profile 0000-0002-3035-4112)

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