# Learned Digital Back-Propagation for Dual-Polarization Dispersion Managed Systems

arXiv: https://arxiv.org/abs/2205.11376 · HTML: https://mlsanity.com/paper/2205.11376/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 3 of 3)

- [Nonlinear Interference Mitigation via Deep Neural Networks](https://mlsanity.com/paper/1710.06234/) (2017)
- [Physics-Based Deep Learning for Fiber-Optic Communication Systems](https://mlsanity.com/paper/2010.14258/) (2020)
- [Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation](https://mlsanity.com/paper/2001.09277/) (2020)

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

- [Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation](https://mlsanity.com/paper/2307.06821/) (2023)

## Abstract (arXiv)

Digital back-propagation (DBP) and learned DBP (LDBP) are proposed for nonlinearity mitigation in WDM dual-polarization dispersion-managed systems. LDBP achieves Q-factor improvement of 1.8 dB and 1.2 dB, respectively, over linear equalization and a variant of DBP adapted to DM systems.