End-to-end Learning for GMI Optimized Geometric Constellation Shape

Rasmus T. Jones; Metodi P. Yankov; Darko Zibar

End-to-end Learning for GMI Optimized Geometric Constellation Shape

Information Theory 2019-07-22 v1 Signal Processing math.IT Machine Learning

Authors: Rasmus T. Jones , Metodi P. Yankov , Darko Zibar

Abstract

Autoencoder-based geometric shaping is proposed that includes optimizing bit mappings. Up to 0.2 bits/QAM symbol gain in GMI is achieved for a variety of data rates and in the presence of transceiver impairments. The gains can be harvested with standard binary FEC at no cost w.r.t. conventional BICM.

Keywords

coded modulation

Cite

@article{arxiv.1907.08535,
  title  = {End-to-end Learning for GMI Optimized Geometric Constellation Shape},
  author = {Rasmus T. Jones and Metodi P. Yankov and Darko Zibar},
  journal= {arXiv preprint arXiv:1907.08535},
  year   = {2019}
}

Comments

submitted to ECOC 2019

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