Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to se...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/376515 |
| Acceso en línea: | https://hdl.handle.net/2117/376515 https://dx.doi.org/10.1109/TMTT.2022.3209214 |
| Access Level: | acceso abierto |
| Palabra clave: | Neural networks (Computer science) Machine learning Artificial neural networks (ANNs) Digital predistortion (DPD) Multiple-input multiple-output (MIMO) Power amplifier (PA) Xarxes neuronals (Informàtica) Aprenentage automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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