Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications

Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
7/21/2025
cs.LGcs.ITeess.SP

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Abstract

We present an autoregressive end-to-end learning approach for probabilistic shaping on nonlinear fiber channels. Our proposed scheme learns the joint symbol distribution and provides a 0.3-bits/2D achievable information rate gain over an optimized marginal distribution for dual-polarized 64-QAM transmission over a single-span 205 km link.

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Cite this paper

@article{askari2025neural,
  title  = {Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications},
  author = {Mohammad Taha Askari and Lutz Lampe and Amirhossein Ghazisaeidi},
  year   = {2025},
  eprint = {2507.16012},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.16012v1}
}

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