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Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities

Information Theory 2018-05-11 v1 math.IT Machine Learning
Authors: Rasmus T. Jones , Tobias A. Eriksson , Metodi P. Yankov , Darko Zibar
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Abstract

A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a simplified fiber channel model.

Keywords

geometric constellation shaping deep learning unsupervised learning fiber nonlinearities optical communications

Cite

@article{arxiv.1805.03785,
  title  = {Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities},
  author = {Rasmus T. Jones and Tobias A. Eriksson and Metodi P. Yankov and Darko Zibar},
  journal= {arXiv preprint arXiv:1805.03785},
  year   = {2018}
}

Comments

3 pages, 6 figures, submitted to ECOC 2018

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