Deep Learning Based Digital Back Propagation with Polarization State Rotation & Phase Noise Invariance

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Author
Bitachon, Bertold Iancc
Ghazisaeidi, Amirhossein
Bäuerle, Benediktcc
Eppenberger, Marcocc
Leuthold, Juergcc
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Date
2020
Type
    Conference Paper
Abstract
A new deep learning training method for digital back propagation (DBP) is introduced. It is invariant to polarization state rotation and phase noise. Applying the method one gains more than 1 dB over standard DBP.
Publication status
published
External links
Book title
Optical Fiber Communication Conference (OFC) 2020
Journal / series
OSA Technical Digest
Pages / Article No.
M1G.2
Publisher
Optical Society of America
Event
The Optical Networking and Communication Conference & Exhibition (OFC 2020), San Diego, CA, USA, March 8-12, 2020
Organisational unit
02635 - Institut für Elektromagnetische Felder / Electromagnetic Fields Laboratory
03974 - Leuthold, Juerg / Leuthold, Juerg  
Funding
670478 - Plasmonic-Silicon-Organic Hybrid – a Universal Platform for THz Communications (EC)
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Citations
Cited 4 times in Web of Science
Cited 10 times in Scopus
ETH Bibliography
yes
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