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A survey about deep learning for constellation design in communications

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Comunicacioneses
dc.contributor.authorLópez Morales, Manuel José
dc.contributor.authorChen Hu, Kun
dc.contributor.authorGarcía-Armada, Ana
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)en
dc.date.accessioned2020-06-15T09:06:38Z
dc.date.available2021-03-17T11:48:40Z
dc.date.issued2020-11-10es
dc.descriptionProceeding of: 12th IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing, (CSNDSP), 20-22, July 2020, (Online).en
dc.description.abstractThe performance of communication systems dependson the choice of constellations, designed in an end-to-endmanner. In case of a mathematical intractability, either becauseof complexity or even lack of channel model only sub-optimal solutionscan be provided with an analytical approach. We presentend-to-end learning, a recent technique in communications tolearn optimal transmitter and receiver architectures based ondeep neural networks (DNNs) architectures. We discuss cases inwhich this technique has been used to design constellations inwhich channel model intractability repressed from a mathematicalanalysis.en
dc.description.sponsorshipThis work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie ETN TeamUp5G, grant agreement No. 813391, and from the Spanish National Project TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE).en
dc.format.extent4
dc.identifier.bibliographicCitation12th IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing, (CSNDSP), 20-22, July 2020, Porto, Portugal (Online), 4 pp.en
dc.identifier.doihttps://doi.org/10.1109/CSNDSP49049.2020.9249528
dc.identifier.isbn978-1-7281-6743-5
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage4
dc.identifier.urihttps://hdl.handle.net/10016/30612
dc.identifier.uxxiCC/0000030585
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate20-22 July, 2020en
dc.relation.eventnumber12
dc.relation.eventtitleIEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing, (CSNDSP).en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/813391/TeamUp5Gen
dc.relation.projectIDGobierno de España. TEC2017-90093-C3-2-R/TERESA-ADAes
dc.rights© 2020 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherMachine learningen
dc.subject.otherEnd-to-end learningen
dc.subject.otherChannel imperfectionsen
dc.subject.otherConstellation designen
dc.subject.otherDeep neural networks architecturesen
dc.titleA survey about deep learning for constellation design in communicationsen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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