Deep Learning Based Digital Backpropagation Enabling SNR Gain at Low Complexity
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. Show more
Publication status
publishedExternal links
Book title
Next-Generation Optical Communication - Components, Sub-Systems, and Systems XJournal / series
Proceedings of SPIEVolume
Pages / Article No.
Publisher
SPIEEvent
Subject
Deep learning; digital backpropagation; digital signal processing; nonlinearity; coherent communicationOrganisational unit
03974 - Leuthold, Juerg / Leuthold, Juerg
02635 - Institut für Elektromagnetische Felder / Electromagnetic Fields Laboratory
Funding
670478 - Plasmonic-Silicon-Organic Hybrid – a Universal Platform for THz Communications (EC)
More
Show all metadata
ETH Bibliography
yes
Altmetrics