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Nonlinear Interference Mitigation via Deep Neural Networks

Information Theory 2017-10-18 v1 math.IT Machine Learning
Authors: Christian Häger , Henry D. Pfister
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Abstract

A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP). For a 32x100 km fiber-optic link, the resulting "learned" DBP significantly reduces the complexity compared to conventional DBP implementations.

Keywords

neural network training deep neural network deep learning

Cite

@article{arxiv.1710.06234,
  title  = {Nonlinear Interference Mitigation via Deep Neural Networks},
  author = {Christian Häger and Henry D. Pfister},
  journal= {arXiv preprint arXiv:1710.06234},
  year   = {2017}
}

Comments

3 pages, 2 figures

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