# Cost-Gain Analysis of Sequence Selection for Nonlinearity Mitigation

arXiv: https://arxiv.org/abs/2411.02004 · HTML: https://mlsanity.com/paper/2411.02004/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2024.

## 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)

We propose a low-complexity sign-dependent metric for sequence selection and study the nonlinear shaping gain achievable for a given computational cost, establishing a benchmark for future research. Small gains are obtained with feasible complexity. Higher gains are achievable in principle, but with high complexity or a more sophisticated metric.