Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language
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Computer Science > Machine Learning
Title:Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language
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.
| Comments: | 4 pages, 2 figures, Submitted to the 52nd European Conference on Optical Communications |
| Subjects: | Machine Learning (cs.LG); Information Theory (cs.IT); Signal Processing (eess.SP) |
| Cite as: | arXiv:2605.28143 [cs.LG] |
| (or arXiv:2605.28143v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.28143
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Mohammad Taha Askari [view email][v1] Wed, 27 May 2026 08:25:18 UTC (298 KB)
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