# Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

arXiv: https://arxiv.org/abs/2001.09277 · HTML: https://mlsanity.com/paper/2001.09277/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2020.

## Built on by (most cited first, 7 of 7)

- [Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems](https://mlsanity.com/paper/2206.12180/) (2022)
- [FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training](https://mlsanity.com/paper/2212.03515/) (2022)
- [Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment](https://mlsanity.com/paper/2205.11284/) (2022)
- [Learned Digital Back-Propagation for Dual-Polarization Dispersion Managed Systems](https://mlsanity.com/paper/2205.11376/) (2022)
- [Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission](https://mlsanity.com/paper/2210.05454/) (2022)
- [Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation](https://mlsanity.com/paper/2307.06821/) (2023)
- [Quantization of Neural Network Equalizers in Optical Fiber Transmission Experiments](https://mlsanity.com/paper/2310.05950/) (2023)

## Abstract (arXiv)

We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation. This approach performs hardware-friendly DBP and distributed PMD compensation with performance close to the PMD-free case.