Deep Learning Based Digital Backpropagation Enabling SNR Gain at Low Complexity
dc.contributor.author
Bitachon, Bertold Ian
dc.contributor.author
Ghazisaeidi, Amirhossein
dc.contributor.author
Eppenberger, Marco
dc.contributor.author
Bäuerle, Benedikt
dc.contributor.author
Ayata, Masafumi
dc.contributor.author
Leuthold, Juerg
dc.contributor.editor
Li, Guifang
dc.contributor.editor
Nakajima, Kazuhide
dc.date.accessioned
2021-08-03T09:01:49Z
dc.date.available
2021-07-29T03:19:45Z
dc.date.available
2021-08-03T09:01:49Z
dc.date.issued
2021
dc.identifier.isbn
978-1-5106-4262-1
en_US
dc.identifier.issn
0277-786X
dc.identifier.other
10.1117/12.2577226
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/498260
dc.description.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.
en_US
dc.language.iso
en
en_US
dc.publisher
SPIE
en_US
dc.subject
Deep learning
en_US
dc.subject
digital backpropagation
en_US
dc.subject
digital signal processing
en_US
dc.subject
nonlinearity
en_US
dc.subject
coherent communication
en_US
dc.title
Deep Learning Based Digital Backpropagation Enabling SNR Gain at Low Complexity
en_US
dc.type
Conference Paper
dc.date.published
2021-03-05
ethz.book.title
Next-Generation Optical Communication - Components, Sub-Systems, and Systems X
en_US
ethz.journal.title
Proceedings of SPIE
ethz.journal.volume
11713
en_US
ethz.journal.abbreviated
Proc. SPIE Int. Soc. Opt. Eng.
ethz.pages.start
117130L
en_US
ethz.size
10 p.
en_US
ethz.event
SPIE OPTO 2021
en_US
ethz.event.location
Online
en_US
ethz.event.date
March 6-11, 2021
en_US
ethz.grant
Plasmonic-Silicon-Organic Hybrid – a Universal Platform for THz Communications
en_US
ethz.identifier.wos
ethz.publication.place
Bellingham, WA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02635 - Institut für Elektromagnetische Felder / Electromagnetic Fields Laboratory::03974 - Leuthold, Juerg / Leuthold, Juerg
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02635 - Institut für Elektromagnetische Felder / Electromagnetic Fields Laboratory
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02635 - Institut für Elektromagnetische Felder / Electromagnetic Fields Laboratory::03974 - Leuthold, Juerg / Leuthold, Juerg
ethz.grant.agreementno
670478
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
H2020
ethz.date.deposited
2021-07-29T03:19:56Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-08-03T09:01:56Z
ethz.rosetta.lastUpdated
2024-02-02T14:27:59Z
ethz.rosetta.versionExported
true
ethz.COinS
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Conference Paper [34963]