English

Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems

Signal Processing 2021-06-25 v1
Authors: Pedro J. Freire , Daniel Abode , Jaroslaw E. Prilepsky , Sergei K. Turitsyn
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Abstract

Transfer learning is proposed to adapt an NN-based nonlinear equalizer across different launch powers and modulation formats using a 450km TWC-fiber transmission. The result shows up to 92% reduction in epochs or 90% in the training dataset.

Keywords

wireless communication wireless channel characterization

Cite

@article{arxiv.2106.13144,
  title  = {Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems},
  author = {Pedro J. Freire and Daniel Abode and Jaroslaw E. Prilepsky and Sergei K. Turitsyn},
  journal= {arXiv preprint arXiv:2106.13144},
  year   = {2021}
}

Comments

OSA Advanced Photonics Congress 2021 - Oral Presentation

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