Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866917538673197056 |
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| author | Askari, Mohammad Taha Lampe, Lutz Ghazisaeidi, Amirhossein |
| author_facet | Askari, Mohammad Taha Lampe, Lutz Ghazisaeidi, Amirhossein |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28143 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language Askari, Mohammad Taha Lampe, Lutz Ghazisaeidi, Amirhossein Machine Learning Information Theory Signal Processing 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. |
| title | Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language |
| topic | Machine Learning Information Theory Signal Processing |
| url | https://arxiv.org/abs/2605.28143 |