Sparse identification of volterra models for power amplifiers without pseudoinverse computation

Article number 9178996

Detalles Bibliográficos
Autores: Becerra González, Juan Antonio, Madero Ayora, María José, Reina Tosina, Luis Javier, Crespo Cadenas, Carlos
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/125277
Acceso en línea:https://hdl.handle.net/11441/125277
https://doi.org/10.1109/TMTT.2020.3016967
Access Level:acceso abierto
Palabra clave:Behavioral modeling
Digital predistortion (DPD)
Doubly orthogonal matching pursuit (DOMP)
Greedy algorithm
Model identification
Power amplifier (PA)
Sparse regression
Volterra series
id ES_6ca445c8b3b1cd260e6e9e36631f4fd4
oai_identifier_str oai:idus.us.es:11441/125277
network_acronym_str ES
network_name_str España
repository_id_str
spelling Sparse identification of volterra models for power amplifiers without pseudoinverse computationBecerra González, Juan AntonioMadero Ayora, María JoséReina Tosina, Luis JavierCrespo Cadenas, CarlosBehavioral modelingDigital predistortion (DPD)Doubly orthogonal matching pursuit (DOMP)Greedy algorithmModel identificationPower amplifier (PA)Sparse regressionVolterra seriesArticle number 9178996We present a new formulation of the doubly orthogonal matching pursuit (DOMP) algorithm for the sparse recovery of Volterra series models. The proposal works over the covariance matrices by taking advantage of the orthogonal properties of the solution at each iteration and avoids the calculation of the pseudoinverse matrix to obtain the model coefficients. A detailed formulation of the algorithm is provided along with a computational complexity assessment, showing a fixed complexity per iteration compared with its previous versions in which it depends on the iteration number. Moreover, we empirically demonstrate the reduction in computational complexity in terms of runtime and highlight the pruning capabilities through its application to the digital predistortion of a class J power amplifier operating under 5G-NR signals with the bandwidth of 20 and 30 MHz, concluding that this proposal significantly outperforms existing techniques in terms of computational complexity.Institute of Electrical and Electronics Engineers Inc.Teoría de la Señal y Comunicaciones2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/125277https://doi.org/10.1109/TMTT.2020.3016967reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Transactions on Microwave Theory and Techniques, 68 (11), 4570-4578.https://ieeexplore.ieee.org./document/9178996info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1252772026-06-17T12:51:07Z
dc.title.none.fl_str_mv Sparse identification of volterra models for power amplifiers without pseudoinverse computation
title Sparse identification of volterra models for power amplifiers without pseudoinverse computation
spellingShingle Sparse identification of volterra models for power amplifiers without pseudoinverse computation
Becerra González, Juan Antonio
Behavioral modeling
Digital predistortion (DPD)
Doubly orthogonal matching pursuit (DOMP)
Greedy algorithm
Model identification
Power amplifier (PA)
Sparse regression
Volterra series
title_short Sparse identification of volterra models for power amplifiers without pseudoinverse computation
title_full Sparse identification of volterra models for power amplifiers without pseudoinverse computation
title_fullStr Sparse identification of volterra models for power amplifiers without pseudoinverse computation
title_full_unstemmed Sparse identification of volterra models for power amplifiers without pseudoinverse computation
title_sort Sparse identification of volterra models for power amplifiers without pseudoinverse computation
dc.creator.none.fl_str_mv Becerra González, Juan Antonio
Madero Ayora, María José
Reina Tosina, Luis Javier
Crespo Cadenas, Carlos
author Becerra González, Juan Antonio
author_facet Becerra González, Juan Antonio
Madero Ayora, María José
Reina Tosina, Luis Javier
Crespo Cadenas, Carlos
author_role author
author2 Madero Ayora, María José
Reina Tosina, Luis Javier
Crespo Cadenas, Carlos
author2_role author
author
author
dc.contributor.none.fl_str_mv Teoría de la Señal y Comunicaciones
dc.subject.none.fl_str_mv Behavioral modeling
Digital predistortion (DPD)
Doubly orthogonal matching pursuit (DOMP)
Greedy algorithm
Model identification
Power amplifier (PA)
Sparse regression
Volterra series
topic Behavioral modeling
Digital predistortion (DPD)
Doubly orthogonal matching pursuit (DOMP)
Greedy algorithm
Model identification
Power amplifier (PA)
Sparse regression
Volterra series
description Article number 9178996
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/125277
https://doi.org/10.1109/TMTT.2020.3016967
url https://hdl.handle.net/11441/125277
https://doi.org/10.1109/TMTT.2020.3016967
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Microwave Theory and Techniques, 68 (11), 4570-4578.
https://ieeexplore.ieee.org./document/9178996
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869410282489511936
score 15,300724

Similares en LA Referencia