# Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications

arXiv: https://arxiv.org/abs/2507.16012 · HTML: https://mlsanity.com/paper/2507.16012/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [End-to-end Deep Learning of Optical Fiber Communications](https://mlsanity.com/paper/1804.04097/) (2018)
- [Joint Learning of Geometric and Probabilistic Constellation Shaping](https://mlsanity.com/paper/1906.07748/) (2019)
- [Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems](https://mlsanity.com/paper/2004.05062/) (2020)
- [Analysis of Nonlinear Fiber Interactions for Finite-Length Constant-Composition Sequences](https://mlsanity.com/paper/2006.07039/) (2020)
- [New Lower Bounds on the Capacity of Optical Fiber Channels via Optimized Shaping and Detection](https://mlsanity.com/paper/2112.03796/) (2021)
- [Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication](https://mlsanity.com/paper/2204.07457/) (2022)
- [Probabilistic Amplitude Shaping and Nonlinearity Tolerance: Analysis and Sequence Selection Method](https://mlsanity.com/paper/2208.03449/) (2022)
- [End-to-end Optimization of Constellation Shaping for Wiener Phase Noise Channels with a Differentiable Blind Phase Search](https://mlsanity.com/paper/2212.03839/) (2022)
- [Sequence-Selection-Based Constellation Shaping for Nonlinear Channels](https://mlsanity.com/paper/2307.13292/) (2023)
- [Perturbation-based Sequence Selection for Probabilistic Amplitude Shaping](https://mlsanity.com/paper/2407.09006/) (2024)
- [Cost-Gain Analysis of Sequence Selection for Nonlinearity Mitigation](https://mlsanity.com/paper/2411.02004/) (2024)
- [Probabilistic Shaping for Nonlinearity Tolerance](https://mlsanity.com/paper/2412.09581/) (2024)
- [Kurtosis-limited Sphere Shaping for Nonlinear Interference Noise Reduction in Optical Channels](https://mlsanity.com/paper/2105.14794/) (2021)

## Built on by (most cited first, 1 of 1)

- [Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language](https://mlsanity.com/paper/2605.28143/) (2026)

## Abstract (arXiv)

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.