Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration
Journal article, 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.

Complexity theory

Machine learning

deep learning

Optical attenuators

Optical propagation

Optical polarization

Artificial neural networks

Nonlinear optics

digital signal processing

subband processing

low complexity digital backpropagation

polarization mode

Author

Vinícius Oliari

Eindhoven University of Technology

Sebastiaan Goossens

Eindhoven University of Technology

Christian Häger

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Gabriele Liga

Eindhoven University of Technology

Rick M. Bütler

Eindhoven University of Technology

Menno van den Hout

Eindhoven University of Technology

Sjoerd van der Heide

Eindhoven University of Technology

Henry D. Pfister

Duke University

Chigo M. Okonkwo

Eindhoven University of Technology

Alex Alvarado

Eindhoven University of Technology

Journal of Lightwave Technology

0733-8724 (ISSN)

Vol. 38 12 3114-3124

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

European Commission (EC), 2017-01-01 -- 2019-12-31.

Areas of Advance

Information and Communication Technology

Subject Categories

Telecommunications

Communication Systems

Electrical Engineering, Electronic Engineering, Information Engineering

Signal Processing

DOI

10.1109/JLT.2020.2994220

More information

Latest update

9/17/2020