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Linealización digital de transmisores mediante redes neuronales no lineales
dc.contributor.author | López Bueno, David |
dc.contributor.author | Pham, Thi Quynh Anh |
dc.contributor.author | Montoro López, Gabriel |
dc.contributor.author | Gilabert Pinal, Pere Lluís |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2020-05-04T06:01:36Z |
dc.date.issued | 2019 |
dc.identifier.citation | López, D. [et al.]. Linealización digital de transmisores mediante redes neuronales no lineales. A: Simposium Nacional de la Unión Científica Internacional de Radio. "Proceedings URSI". 2019. |
dc.identifier.isbn | 978-84-09-12961-4 |
dc.identifier.other | https://zenodo.org/record/3678089#.XqbANpmxU2y |
dc.identifier.uri | http://hdl.handle.net/2117/186053 |
dc.description.abstract | This paper presents an overview on how the artificial neural networks (ANN) are applied to digitally linearize modern transmitters. The use of nonlinear ANNs is intended to either assist or replace the traditional crest factor reduction (CFR) and digital predistortion (DPD) building blocks, and benefit from their inherently good approximation capabilities and reduced hardware complexity when targeting complex transceiver scenarios such as those present in 5G. There is not a universal procedure to set up the best ANN given a specific application. However, in this paper some design considerations which have been experimentally validated in the literature will be summarized both considering single-antenna and multi-antenna transmitters. Finally, some principles in the selection of ANN parameters for nonlinear modeling will be showcased by using asimulation test bench that employs measured data from a strongly non-linear GaN PA operated with wideband signals. |
dc.description.sponsorship | Agradecemos el soporte y financiación recibidos por el Gobierno de España (MICINN) y FEDER en el marco de los proyectos TEC2017-83343-C4-2-R y RTI2018-099841-B-I00, y por la Generalitat de Catalunya bajo las ayudas 2017 SGR 813 y 2017 SGR 891. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.other | Transmisores |
dc.subject.other | Redes neuronales. |
dc.title | Linealización digital de transmisores mediante redes neuronales no lineales |
dc.type | Conference report |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. CSC - Components and Systems for Communications Research Group |
dc.description.peerreviewed | Peer Reviewed |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 26517618 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/FEDER/TEC2017-88343-C4-2-R |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099841-B-I00/ES/TECNOLOGIAS 5G PARA EFICIENCIA Y RECONFIGURACION DE LA INFRAESTRUCTURA DE ACCESO RADIO Y DISPOSITIVOS MOVILES/ |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/2017SGR813 |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/2017SGR891 |
dc.date.lift | 10000-01-01 |
local.citation.author | López, D.; Pham, T.; Montoro, G.; Gilabert, Pere L. |
local.citation.contributor | Simposium Nacional de la Unión Científica Internacional de Radio |
local.citation.publicationName | Proceedings URSI |