6.6SPJan 25, 2020
Model-Based Machine Learning for Joint Digital Backpropagation and PMD CompensationChristian Häger, Henry D. Pfister, Rick M. Bütler et al.
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.
5.1SPApr 22, 2019
Revisiting Multi-Step Nonlinearity Compensation with Machine LearningChristian Häger, Henry D. Pfister, Rick M. Bütler et al.
For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: fewer steps are better and more efficient. We challenge this assumption and show that carefully designed multi-step approaches can lead to better performance-complexity trade-offs than their few-step counterparts.