English

Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems

Signal Processing 2022-02-28 v1 Machine Learning
Authors: Pedro J. Freire , Bernhard Spinnler , Daniel Abode , Jaroslaw E. Prilepsky , Abdallah A. I. Ali , Nelson Costa , Wolfgang Schairer , Antonio Napoli , Andrew D. Ellis , Sergei K. Turitsyn
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Abstract

We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up to 99\% training process reduction, which we demonstrate in three experimental setups.

Keywords

domain adaptation neural network equalizer coherent optical system synthetic data training reduction domain randomization

Cite

@article{arxiv.2202.12689,
  title  = {Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems},
  author = {Pedro J. Freire and Bernhard Spinnler and Daniel Abode and Jaroslaw E. Prilepsky and Abdallah A. I. Ali and Nelson Costa and Wolfgang Schairer and Antonio Napoli and Andrew D. Ellis and Sergei K. Turitsyn},
  journal= {arXiv preprint arXiv:2202.12689},
  year   = {2022}
}

Comments

Paper Accepted at OFC 2022

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