Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems


In this work, we address the question of the adaptability of artificial neural networks (NNs) used for impairments mitigation in optical transmission systems. We demonstrate that by using well-developed techniques based on the concept of transfer learning, we can efficaciously retrain NN-based equalizers to adapt to the changes in the transmission system, using just a fraction (down to 1%) of the initial training data or epochs. We evaluate the capability of transfer learning to adapt the NN to changes in the launch power, modulation format, symbol rate, or even fiber plants (different fiber types and lengths). The numerical examples utilize the recently introduced NN equalizer consisting of a convolutional layer coupled with bi-directional long-short term memory (biLSTM) recurrent NN element. Our analysis focuses on long-haul coherent optical transmission systems for two types of fibers: the standard single-mode fiber (SSMF) and the TrueWave Classic (TWC) fiber. We underline the specific peculiarities that occur when transferring the learning in coherent optical communication systems and draw the limits for the transfer learning efficiency. Our results demonstrate the effectiveness of transfer learning for the fast adaptation of NN architectures to different transmission regimes and scenarios, paving the way for engineering flexible and universal solutions for nonlinearity mitigation.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Additional Information: 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). YO acknowledges the support of the SMARTNET EMJMD programme (Project number - 586686-EPP-1-2017-1-UK-EPPKA1-JMD-MOB). JEP is supported by Leverhulme Trust, Grant No. RP-2018-063. SKT acknowledges support of the EPSRC project TRANSNET.
Uncontrolled Keywords: Neural network,coherent detection,flexible operation,nonlinear equalizer,transfer learning,Atomic and Molecular Physics, and Optics
Publication ISSN: 0733-8724
Last Modified: 09 Apr 2024 07:25
Date Deposited: 03 Sep 2021 10:23
Full Text Link: https://ieeexpl ... cument/9523752/
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-11-01
Published Online Date: 2021-08-26
Accepted Date: 2021-08-01
Authors: Freire de Carvalho Souza, Pedro Jorge (ORCID Profile 0000-0003-3145-1018)
Abode, Daniel
Prilepsky, Jaroslaw E. (ORCID Profile 0000-0002-3035-4112)
Costa, Nelson
Spinnler, Bernhard
Napoli, Antonio
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)



Version: Accepted Version

License: Creative Commons Attribution

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