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arXiv · 2605.28143

Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language

Mohammad Taha Askari·Lutz Lampe·Amirhossein Ghazisaeidi
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Abstract

We present the first neural probabilistic amplitude shaping that outperforms existing methods while accounting for all implementation losses, using a block-less, easily implementable sequential autoregressive encoder compatible with arithmetic distribution matching, yielding reduced rate loss and higher achievable information rates.

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Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi. 2026-05-27. Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language. https://arxiv.org/abs/2605.28143

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