Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems

Sasipim Srivallapanondh, Pedro J. Freire, Ashraful Alam, Nelson Costa, Bernhard Spinnler +4 more
7/4/2023
eess.SPcs.LG

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Abstract

For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems. A "single" NN-based equalizer improves Q-factor by up to 4 dB compared to CDC, without re-training, even with variations in launch power, symbol rate, or transmission distance.

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Cite this paper

@article{srivallapanondh2023multitask,
  title  = {Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems},
  author = {Sasipim Srivallapanondh and Pedro J. Freire and Ashraful Alam and Nelson Costa and Bernhard Spinnler and Antonio Napoli and Egor Sedov and Sergei K. Turitsyn and Jaroslaw E. Prilepsky},
  year   = {2023},
  eprint = {2307.05374},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.05374v3}
}

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