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

no code implementations • 23 Oct 2020 • Rick M. Bütler, Christian Häger, Henry D. Pfister, Gabriele Liga, Alex Alvarado

In this paper, we propose a model-based machine-learning approach for dual-polarization systems by parameterizing the split-step Fourier method for the Manakov-PMD equation.

BIG-bench Machine Learning

Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration

no code implementations • 20 Jul 2020 • Vinícius Oliari, Sebastiaan Goossens, Christian Häger, Gabriele Liga, Rick M. Bütler, Menno van den Hout, Sjoerd van der Heide, Henry D. Pfister, Chigo Okonkwo, Alex Alvarado

One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems.

BIG-bench Machine Learning

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

no code implementations • 25 Jan 2020 • Christian Häger, Henry D. Pfister, Rick M. Bütler, Gabriele Liga, Alex Alvarado

We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation.

BIG-bench Machine Learning

Revisiting Multi-Step Nonlinearity Compensation with Machine Learning

no code implementations • 22 Apr 2019 • Christian Häger, Henry D. Pfister, Rick M. Bütler, Gabriele Liga, Alex Alvarado

For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: fewer steps are better and more efficient.

BIG-bench Machine Learning