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End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping

Information Theory 2021-12-10 v1 Artificial Intelligence Signal Processing math.IT
Authors: Vahid Aref , Mathieu Chagnon
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Abstract

We present a novel autoencoder-based learning of joint geometric and probabilistic constellation shaping for coded-modulation systems. It can maximize either the mutual information (for symbol-metric decoding) or the generalized mutual information (for bit-metric decoding).

Keywords

constellation shaping autoencoder coded-modulation mutual information generalized mutual information

Cite

@article{arxiv.2112.05050,
  title  = {End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping},
  author = {Vahid Aref and Mathieu Chagnon},
  journal= {arXiv preprint arXiv:2112.05050},
  year   = {2021}
}

Comments

Will be presented at OFC 2022 (invited talk)

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