Publication: A survey about deep learning for constellation design in communications
dc.affiliation.dpto | UC3M. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Comunicaciones | es |
dc.contributor.author | López Morales, Manuel José | |
dc.contributor.author | Chen Hu, Kun | |
dc.contributor.author | García-Armada, Ana | |
dc.contributor.funder | European Commission | en |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | en |
dc.date.accessioned | 2020-06-15T09:06:38Z | |
dc.date.available | 2021-03-17T11:48:40Z | |
dc.date.issued | 2020-11-10 | es |
dc.description | Proceeding of: 12th IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing, (CSNDSP), 20-22, July 2020, (Online). | en |
dc.description.abstract | The 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.sponsorship | This 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.extent | 4 | |
dc.identifier.bibliographicCitation | 12th 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.doi | https://doi.org/10.1109/CSNDSP49049.2020.9249528 | |
dc.identifier.isbn | 978-1-7281-6743-5 | |
dc.identifier.publicationfirstpage | 1 | |
dc.identifier.publicationlastpage | 4 | |
dc.identifier.uri | https://hdl.handle.net/10016/30612 | |
dc.identifier.uxxi | CC/0000030585 | |
dc.language.iso | eng | en |
dc.publisher | IEEE | en |
dc.relation.eventdate | 20-22 July, 2020 | en |
dc.relation.eventnumber | 12 | |
dc.relation.eventtitle | IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing, (CSNDSP). | en |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/813391/TeamUp5G | en |
dc.relation.projectID | Gobierno de España. TEC2017-90093-C3-2-R/TERESA-ADA | es |
dc.rights | © 2020 IEEE. | en |
dc.rights.accessRights | open access | en |
dc.subject.eciencia | Telecomunicaciones | es |
dc.subject.other | Machine learning | en |
dc.subject.other | End-to-end learning | en |
dc.subject.other | Channel imperfections | en |
dc.subject.other | Constellation design | en |
dc.subject.other | Deep neural networks architectures | en |
dc.title | A survey about deep learning for constellation design in communications | en |
dc.type | conference paper | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
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