mlsanityData to 14 Oct 2026

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

arXiv 2605.28143 ↗First posted 27 May 2026cs.LG cs.IT eess.SPcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 27 May 2026.

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13 of 21 references.

References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,079
  3. 2020GLU Variants Improve Transformercited by 1,804
  4. 2020Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systemscited by 5
  5. 2020Analysis of Nonlinear Fiber Interactions for Finite-Length Constant-Composition Sequencescited by 2
  6. 2021New Lower Bounds on the Capacity of Optical Fiber Channels via Optimized Shaping and Detectioncited by 2
  7. 2022Probabilistic Amplitude Shaping and Nonlinearity Tolerance: Analysis and Sequence Selection Methodcited by 2
  8. 2023Sequence-Selection-Based Constellation Shaping for Nonlinear Channelscited by 2
  9. 2024Perturbation-based Sequence Selection for Probabilistic Amplitude Shapingcited by 2
  10. 2024Cost-Gain Analysis of Sequence Selection for Nonlinearity Mitigationcited by 2
  11. 2024Probabilistic Shaping for Nonlinearity Tolerancecited by 2
  12. 2025Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communicationscited by 1
1 more
  1. 2026Neural Probabilistic Amplitude Shaping for Nonlinear Fiber Channelscited by 1

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