Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration
Artikel i vetenskaplig tidskrift, 2020

Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the "capacity crunch". One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems. In this paper, we challenge this assumption and show how to carefully design multi-step approaches that provide better performance-complexity trade-offs than their few-step counterparts. We consider the recently proposed learned digital backpropagation (LDBP) approach, where the linear steps in the split-step method are re-interpreted as general linear functions, similar to the weight matrices in a deep neural network. Our main contribution lies in an experimental demonstration of this approach for a 25 Gbaud single-channel optical transmission system. It is shown how LDBP can be integrated into a coherent receiver DSP chain and successfully trained in the presence of various hardware impairments. Our results show that LDBP with limited complexity can achieve better performance than standard DBP by using very short, but jointly optimized, finite-impulse response filters in each step. This paper also provides an overview of recently proposed extensions of LDBP and we comment on potentially interesting avenues for future work.

low complexity digital backpropagation

Optical attenuators

Machine learning

Nonlinear optics

digital signal processing

Complexity theory

deep learning

Optical polarization

subband processing

Optical propagation

Artificial neural networks

polarization mode


Vinícius Oliari

Technische Universiteit Eindhoven

Sebastiaan Goossens

Technische Universiteit Eindhoven

Christian Häger

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Gabriele Liga

Technische Universiteit Eindhoven

Rick M. Bütler

Technische Universiteit Eindhoven

Menno van den Hout

Technische Universiteit Eindhoven

Sjoerd van der Heide

Technische Universiteit Eindhoven

Henry D. Pfister

Duke University

Chigo M. Okonkwo

Technische Universiteit Eindhoven

Alex Alvarado

Technische Universiteit Eindhoven

Journal of Lightwave Technology

0733-8724 (ISSN)

Vol. 38 12 3114-3124 9091867

Coding for terabit-per-second fiber-optical communications (TERA)

Europeiska kommissionen (EU) (EC/H2020/749798), 2017-01-01 -- 2019-12-31.


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