Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications

Mohammad Taha Askari; Lutz Lampe; Amirhossein Ghazisaeidi

Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications

Machine Learning 2025-07-23 v1 Information Theory Signal Processing math.IT

Authors: Mohammad Taha Askari , Lutz Lampe , Amirhossein Ghazisaeidi

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.

Keywords

coded modulation molecular communication wireless communication

Cite

@article{arxiv.2507.16012,
  title  = {Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications},
  author = {Mohammad Taha Askari and Lutz Lampe and Amirhossein Ghazisaeidi},
  journal= {arXiv preprint arXiv:2507.16012},
  year   = {2025}
}

Comments

4 pages, 3 figures, Submitted to the 51st European Conference on Optical Communications

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