# Probabilistic Amplitude Shaping and Nonlinearity Tolerance: Analysis and Sequence Selection Method

arXiv: https://arxiv.org/abs/2208.03449 · HTML: https://mlsanity.com/paper/2208.03449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2022.

## Built on by (most cited first, 2 of 2)

- [Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications](https://mlsanity.com/paper/2507.16012/) (2025)
- [Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language](https://mlsanity.com/paper/2605.28143/) (2026)

## Abstract (arXiv)

Probabilistic amplitude shaping (PAS) is a practical means to achieve a shaping gain in optical fiber communication. However, PAS and shaping in general also affect the signal-dependent generation of nonlinear interference. This provides an opportunity for nonlinearity mitigation through PAS, which is also referred to as a nonlinear shaping gain. In this paper, we introduce a linear lowpass filter model that relates transmitted symbol-energy sequences and nonlinear distortion experienced in an optical fiber channel. Based on this model, we conduct a nonlinearity analysis of PAS with respect to shaping blocklength and mapping strategy. Our model explains results and relationships found in literature and can be used as a design tool for PAS with improved nonlinearity tolerance. We use the model to introduce a new metric for PAS with sequence selection. We perform simulations of selection-based PAS with various amplitude shapers and mapping strategies to demonstrate the effectiveness of the new metric in different optical fiber system scenarios.