Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion

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Autores: López Bueno, David|||0000-0002-9005-0532, Montoro López, Gabriel|||0000-0002-1328-4175, Gilabert Pinal, Pere Lluís|||0000-0001-6183-6977
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/376515
Acesso em linha:https://hdl.handle.net/2117/376515
https://dx.doi.org/10.1109/TMTT.2022.3209214
Access Level:acceso abierto
Palavra-chave: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
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repository_id_str
spelling Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortionLópez Bueno, David|||0000-0002-9005-0532Montoro López, Gabriel|||0000-0002-1328-4175Gilabert Pinal, Pere Lluís|||0000-0001-6183-6977Neural networks (Computer science)Machine learningArtificial neural networks (ANNs)Digital predistortion (DPD)Multiple-input multiple-output (MIMO)Power amplifier (PA)Xarxes neuronals (Informàtica)Aprenentage automàticÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyalÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic© 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.In 5G and beyond radios, the increased bandwidth, the fast-changing waveform scenarios, and the operation of large array multiple-input multiple-output (MIMO) transmitter architectures have challenged both the polynomial and the artificial neural network (ANN) MIMO adaptive digital predistortion (DPD) schemes. This article proposes training data selection methods and dimensionality reduction techniques that can be combined to enable relevant reductions of the DPD training time and the implementation complexity for MIMO transmitter architectures. In this work, the combination of an efficient uncorrelated equation selection (UES) mechanism together with orthogonal least squares (OLS) is proposed to reduce the training data length and the number of basis functions at every behavioral modeling matrix in the polynomial MIMO DPD scheme. For ANN MIMO DPD architectures, applying UES and principal component analysis (PCA) is proposed to reduce the input dataset length and features, respectively. The UES-OLS and the UES-PCA techniques are experimentally validated for a 2×2 MIMO test setup with strong power amplifier (PA) input and output crosstalk.This work was supported in part by the MCIN/AEI/10.13039/501100011033 under Project PID2020-113832RB-C22 and Project PID2020-113832RB-C21; and in part by the European Union-NextGenerationEU through the Spanish Recovery, Transformation and Resilience Plan, under Project TSI-063000-2021-121 (MINECO UNICO Programme).Peer ReviewedIEEE Microwave Theory and Techniques Society20222022-11-0120222022-11-17journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/376515https://dx.doi.org/10.1109/TMTT.2022.3209214reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-113832RB-C21 DISPOSITIVOS ASISTIDOS POR TECNICAS DE DEEP Y MACHINE LEARNING PARA TRANSCEPTORES DE RADIOFRECUENCIA ALTAMENTE EFICIENTESopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3765152026-05-27T15:37:01Z
dc.title.none.fl_str_mv Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion
title Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion
spellingShingle Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion
López Bueno, David|||0000-0002-9005-0532
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
title_short Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion
title_full Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion
title_fullStr Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion
title_full_unstemmed Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion
title_sort Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion
dc.creator.none.fl_str_mv López Bueno, David|||0000-0002-9005-0532
Montoro López, Gabriel|||0000-0002-1328-4175
Gilabert Pinal, Pere Lluís|||0000-0001-6183-6977
author López Bueno, David|||0000-0002-9005-0532
author_facet López Bueno, David|||0000-0002-9005-0532
Montoro López, Gabriel|||0000-0002-1328-4175
Gilabert Pinal, Pere Lluís|||0000-0001-6183-6977
author_role author
author2 Montoro López, Gabriel|||0000-0002-1328-4175
Gilabert Pinal, Pere Lluís|||0000-0001-6183-6977
author2_role author
author
dc.subject.none.fl_str_mv 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
topic 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
description © 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-11-01
2022
2022-11-17
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/376515
https://dx.doi.org/10.1109/TMTT.2022.3209214
url https://hdl.handle.net/2117/376515
https://dx.doi.org/10.1109/TMTT.2022.3209214
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-113832RB-C21 DISPOSITIVOS ASISTIDOS POR TECNICAS DE DEEP Y MACHINE LEARNING PARA TRANSCEPTORES DE RADIOFRECUENCIA ALTAMENTE EFICIENTES
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE Microwave Theory and Techniques Society
publisher.none.fl_str_mv IEEE Microwave Theory and Techniques Society
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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