Deep learning based digital backpropagation enabling SNR gain at low complexity

Bertold Ian Bitachon; Amirhossein Ghazisaeidi; Marco Eppenberger; Benedikt Baeuerle; Masafumi Ayata; Juerg Leuthold

doi:10.1117/12.2577226

5 March 2021 Deep learning based digital backpropagation enabling SNR gain at low complexity

Bertold Ian Bitachon, Amirhossein Ghazisaeidi, Marco Eppenberger, Benedikt Baeuerle, Masafumi Ayata, Juerg Leuthold

Proceedings Volume 11713, Next-Generation Optical Communication: Components, Sub-Systems, and Systems X; 117130L (2021) https://doi.org/10.1117/12.2577226
Event: SPIE OPTO, 2021, Online Only

Abstract

A computationally efficient deep learning based digital backpropagation (DL-DBP) algorithm providing a 1.9 dB SNR over a conventional linear compensation (chromatic dispersion compensation algorithm) and a 1 dB gain over a conventional back-propagation algorithm of the same complexity is presented. The algorithm has been tested in a 1200km transmission experiment. Also, if the algorithm is tested against a conventional digital backpropagation algorithm with the gain, then the new algorithm requires a factor 6 lower complexity. We discuss its training procedure and its principle. We discuss its training procedure and its principle.

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Citation Download Citation

Bertold Ian Bitachon, Amirhossein Ghazisaeidi, Marco Eppenberger, Benedikt Baeuerle, Masafumi Ayata, and Juerg Leuthold "Deep learning based digital backpropagation enabling SNR gain at low complexity", Proc. SPIE 11713, Next-Generation Optical Communication: Components, Sub-Systems, and Systems X, 117130L (5 March 2021); https://doi.org/10.1117/12.2577226

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