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dc.creatoramelec, viloria
dc.creatorLizardo Zelaya, Nelson Alberto
dc.creatorMercado Caruso, Nohora Nubia
dc.date.accessioned2021-01-15T14:14:56Z
dc.date.available2021-01-15T14:14:56Z
dc.date.issued2020
dc.identifier.issn1877-0509
dc.identifier.urihttps://hdl.handle.net/11323/7693
dc.description.abstractA rapid evolution in electronic systems has been experienced in recent years, and one of the fields where this development has been notorious is the telecommunication systems in which users demand more and better services and with higher data transfer speeds. This has generated the need to develop new devices, algorithms and systems that manage to satisfy the requirements demanded y new technologies. An example of the above is the front-end of telecommunication systems. Systems need to be more efficient, but some elements of the systems, as the power amplifier, present nonlinearity when operating in its most efficient region, causing that it has to make a commitment between efficiency and linearity. This paper presents a comparison of different artificial neural network architectures, as a behavioral modeling method, to perform digital predistortion of power amplifiers.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceProcedia Computer Sciencespa
dc.subjectComparative studyspa
dc.subjectNeural networksspa
dc.subjectDigital pre-distortionspa
dc.subjectRF amplifiersspa
dc.titleArtificial techniques applied to the improvement of the previous signals in the power amplifiersspa
dc.typearticlespa
dcterms.references[1] Liu, T., Ye, Y., Yin, S., Chen, H., Xu, G., Lu, Y., & Chen, Y. (2019, May). Digital Predistortion Linearization with Deep Neural Networks for 5G Power Amplifiers. In 2019 European Microwave Conference in Central Europe (EuMCE) (pp. 216-219). IEEE.spa
dcterms.references[2] Phartiyal, D., & Rawat, M. (2019, February). LSTM-Deep Neural Networks based Predistortion Linearizer for High Power Amplifiers. In 2019 National Conference on Communications (NCC) (pp. 1-5). IEEE.spa
dcterms.references[3] Viloria, A., Hernández Palma, H., Gamboa Suarez, R., Niebles Núẽz, W., & Solórzano Movilla, J. (2020). Intelligent Model for Electric Power Management: Patterns. In Journal of Physics: Conference Series (Vol. 1432). Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/1432/1/012032.spa
dcterms.references[4] Sun, J., Wang, J., Guo, L., Yang, J., & Gui, G. (2020). Adaptive deep learning aided digital predistorter considering dynamic envelope. IEEE Transactions on Vehicular Technology.spa
dcterms.references[5] Tripathi, G. C., Rawat, M., & Rawat, K. (2019, October). Swish Activation Based Deep Neural Network Predistorter for RF-PA. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 1239-1242). IEEE.spa
dcterms.references[6] Tripathi, G. C., Rawat, M., & Rawat, K. (2019, October). Swish Activation Based Deep Neural Network Predistorter for RF-PA. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 1239-1242). IEEE.spa
dcterms.references[7] Cioba, A., Chua, A., Shiu, D. S., Kuo, T. H., & Peng, C. S. (2020). Efficient attention guided 5G power amplifier digital predistortion. arXiv preprint arXiv:2003.13361.spa
dcterms.references[8] Rawat, M., Rawat, K., & Ghannouchi, F. M. (2009). Adaptive digital predistortion of wireless power amplifiers/transmitters using dynamic real-valued focused time-delay line neural networks. IEEE Transactions on Microwave Theory and Techniques, 58(1), 95-104.spa
dcterms.references[9] Isaksson, M. (2007). Radio Frequency Power Amplifiers: Behavioral Modeling, Parameter-Reduction, and Digital Predistortion (Doctoral dissertation, Royal Institute of Technology).spa
dcterms.references[10] Xiang, T. and Wang, G. Doherty power amplifier with feedforward linearization, 2009 Asia Pacific Microwave Conference, Singapore, 2009, pp. 1621-1624spa
dcterms.references[11] Watkins, B. E., North, R., & Tummala, M. (1995, November). Neural network based adaptive predistortion for the linearization of nonlinear RF amplifiers. In Proceedings of MILCOM'95 (Vol. 1, pp. 145-149). IEEE.spa
dcterms.references[12] Watkins, B. E., & North, R. (1996, October). Predistortion of nonlinear amplifiers using neural networks. In Proceedings of MILCOM'96 IEEE Military Communications Conference (Vol. 1, pp. 316-320). IEEE.spa
dcterms.references[13] Viloria, A., Senior Naveda, A., Hernández Palma, H., Niebles Núẽz, W., & Niebles Núẽz, L. (2020). Electrical Consumption Patterns through Machine Learning. In Journal of Physics: Conference Series (Vol. 1432). Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/1432/1/012093.spa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920317919spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.07.091
dc.type.hasversioninfo:eu-repo/semantics/publishedVersionspa


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