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arXiv · 2507.16012

Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications

Mohammad Taha Askari·Lutz Lampe·Amirhossein Ghazisaeidi
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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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Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi. 2025-07-21. Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications. https://arxiv.org/abs/2507.16012

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