Open AccessDeep learning based digital backpropagation demonstrating SNR gain at low complexity in a 1200 km transmission link
Author(s) -
Bertold Ian Bitachon,
Amirhossein Ghazisaeidi,
Marco Eppenberger,
Benedikt Baeurle,
Masafumi Ayata,
Juerg Leuthold
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.401667
Subject(s) - nonlinear system , computer science , backpropagation , quadrature amplitude modulation , offset (computer science) , transmission (telecommunications) , optics , bit error rate , algorithm , electronic engineering , artificial neural network , physics , artificial intelligence , telecommunications , decoding methods , engineering , quantum mechanics , programming language
A deep learning (DL) based digital backpropagation (DBP) method with a 1 dB SNR gain over a conventional 1 step per span DBP is demonstrated in a 32 GBd 16QAM transmission across 1200 km. The new DL-DPB is shown to require 6 times less computational power over the conventional DBP scheme. The achievement is possible due to a novel training method in which the DL-DBP is blind to timing error, state of polarization rotation, frequency offset and phase offset. An analysis of the underlying mechanism is given. The applied method first undoes the dispersion, compensates for nonlinear effects in a distributed fashion and reduces the out of band nonlinear modulation due to compensation of the nonlinearities by having a low pass characteristic. We also show that it is sufficient to update the elements of the DL network using a signal with high nonlinearity when dispersion or nonlinearity conditions changes. Lastly, simulation results indicate that the proposed scheme is suitable to deal with impairments from transmission over longer distances.