Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain

Agastya Raj, Zehao Wang, Frank Slyne, Tingjun Chen, Dan Kilper +1 more
8/4/2023
cs.NIcs.LG

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Abstract

We present a novel ML framework for modeling the wavelength-dependent gain of multiple EDFAs, based on semi-supervised, self-normalizing neural networks, enabling one-shot transfer learning. Our experiments on 22 EDFAs in Open Ireland and COSMOS testbeds show high-accuracy transfer-learning even when operated across different amplifier types.

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Cite this paper

@article{raj2023selfnormalizing,
  title  = {Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain},
  author = {Agastya Raj and Zehao Wang and Frank Slyne and Tingjun Chen and Dan Kilper and Marco Ruffini},
  year   = {2023},
  eprint = {2308.02233},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.02233v2}
}

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