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.