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dc.contributor.author
Balatsoukas-Stimming, Alexios
dc.contributor.author
Castañeda Fernández, Oscar
dc.contributor.author
Jacobsson, Sven
dc.contributor.author
Durisi, Giuseppe
dc.contributor.author
Studer, Christoph
dc.date.accessioned
2020-11-02T10:03:59Z
dc.date.available
2020-10-30T16:41:45Z
dc.date.available
2020-11-02T10:03:59Z
dc.date.issued
2019-07
dc.identifier.isbn
978-1-5386-6528-2
en_US
dc.identifier.isbn
978-1-5386-6529-9
en_US
dc.identifier.other
10.1109/spawc.2019.8815519
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/448922
dc.identifier.doi
10.3929/ethz-b-000448922
dc.description.abstract
Base station (BS) architectures for massive multiuser (MU) multiple-input multiple-output (MIMO) wireless systems are equipped with hundreds of antennas to serve tens of users on the same time-frequency channel. The immense number of BS antennas incurs high system costs, power, and interconnect bandwidth. To circumvent these obstacles, sophisticated MU precoding algorithms that enable the use of 1-bit DACs have been proposed. Many of these precoders feature parameters that are, traditionally, tuned manually to optimize their performance. We propose to use deep-learning tools to automatically tune such 1-bit precoders. Specifically, we optimize the biConvex 1-bit PrecOding (C2PO) algorithm using neural networks. Compared to the original C2PO algorithm, our neural-network optimized (NNO-)C2PO achieves the same error-rate performance at 2x lower complexity. Moreover, by training NNO-C2PO for different channel models, we show that 1-bit precoding can be made robust to vastly changing propagation conditions.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.title
Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2019-08-29
ethz.book.title
2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
en_US
ethz.pages.start
1
en_US
ethz.pages.end
5
en_US
ethz.size
5 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2019)
en_US
ethz.event.location
Cannes, France
en_US
ethz.event.date
July 2-5, 2019
en_US
ethz.publication.place
Piscataway, NJ
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.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::09695 - Studer, Christoph / Studer, Christoph
en_US
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.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::09695 - Studer, Christoph / Studer, Christoph
en_US
ethz.date.deposited
2020-10-30T16:41:52Z
ethz.source
FORM
ethz.eth
no
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-11-02T10:04:10Z
ethz.rosetta.lastUpdated
2022-03-29T03:52:23Z
ethz.rosetta.versionExported
true
ethz.COinS
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