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.
@article{arxiv.1904.09807,
title = {Revisiting Multi-Step Nonlinearity Compensation with Machine Learning},
author = {Christian Häger and Henry D. Pfister and Rick M. Bütler and Gabriele Liga and Alex Alvarado},
journal= {arXiv preprint arXiv:1904.09807},
year = {2019}
}
Comments
4 pages, 3 figures, This is a preprint of a paper submitted to the 2019 European Conference on Optical Communication