# Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation

arXiv: https://arxiv.org/abs/2307.06821 · HTML: https://mlsanity.com/paper/2307.06821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 4 of 4)

- [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)
- [Learned Digital Back-Propagation for Dual-Polarization Dispersion Managed Systems](https://mlsanity.com/paper/2205.11376/) (2022)

## Abstract (arXiv)

In this paper, we investigate the use of the learned digital back-propagation (LDBP) for equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links. LDBP is a deep neural network that optimizes the parameters of DBP using the stochastic gradient descent. We evaluate DBP and LDBP in a simulated WDM dual-polarization fiber transmission system operating at the bitrate of 256 Gbit/s per channel, with a dispersion map designed for a 2016 km link with 15% residual dispersion. Our results show that in single-channel transmission, LDBP achieves an effective signal-to-noise ratio improvement of 6.3 dB and 2.5 dB, respectively, over linear equalization and DBP. In WDM transmission, the corresponding Q-factor gains are 1.1 dB and 0.4 dB, respectively. Additionally, we conduct a complexity analysis, which reveals that a frequency-domain implementation of LDBP and DBP is more favorable in terms of complexity than the time-domain implementation. These findings demonstrate the effectiveness of LDBP in mitigating the nonlinear effects in DM fiber-optic transmission systems.