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