dc.creator | Barry, Abdoul | es |
dc.creator | Li, Wantao | es |
dc.creator | Becerra González, Juan Antonio | es |
dc.creator | Gilabert, Pere L | es |
dc.date.accessioned | 2022-09-15T18:33:50Z | |
dc.date.available | 2022-09-15T18:33:50Z | |
dc.date.issued | 2021-09 | |
dc.identifier.citation | Barry, A., Li, W., Becerra González, J.A. y Gilabert, P.L. (2021). Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion Linearization. Sensors, 21 (17), 5772. | |
dc.identifier.issn | ISSN:1424-3210 | es |
dc.identifier.issn | E-ISSN:1424-8220 | es |
dc.identifier.uri | https://hdl.handle.net/11441/137120 | |
dc.description.abstract | The power amplifier (PA) is the most critical subsystem in terms of linearity and power
efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA
operates at levels close to saturation, where the device presents its highest power efficiency. Since the
DPD is generally based on Volterra series models, its number of coefficients is high, producing ill-
conditioned and over-fitted estimations. Recently, a plethora of techniques have been independently
proposed for reducing their dimensionality. This paper is devoted to presenting a fair benchmark of
the most relevant order reduction techniques present in the literature categorized by the following:
(i) greedy pursuits, including Orthogonal Matching Pursuit (OMP), Doubly Orthogonal Matching
Pursuit (DOMP), Subspace Pursuit (SP) and Random Forest (RF); (ii) regularization techniques,
including ridge regression and least absolute shrinkage and selection operator (LASSO); (iii) heuristic
local search methods, including hill climbing (HC) and dynamic model sizing (DMS); and (iv) global
probabilistic optimization algorithms, including simulated annealing (SA), genetic algorithms (GA)
and adaptive Lipschitz optimization (adaLIPO). The comparison is carried out with modeling and
linearization performance and in terms of runtime. The results show that greedy pursuits, particularly
the DOMP, provide the best trade-off between execution time and linearization robustness against
dimensionality reduction. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades TEC2017-83343-C4-2-R | es |
dc.description.sponsorship | Unión Europea TEC2017-83343-C4-2-R | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación PID2020-113832RB-C21 | es |
dc.description.sponsorship | Generalitat de Catalunya 2017-SGR-813 | es |
dc.description.sponsorship | Unión Europea 2017-SGR-813 | es |
dc.description.sponsorship | Generalitat de Catalunya 2021-FI-B-137 | es |
dc.description.sponsorship | Unión Europea 2021-FI-B-137 | es |
dc.format | application/pdf | es |
dc.format.extent | 28 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Sensors, 21 (17), 5772. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Behavioral modeling | es |
dc.subject | Digital predistortion linearization | es |
dc.subject | Dimensionality reduction | es |
dc.subject | Feature selection techniques | es |
dc.subject | Power amplifier | es |
dc.title | Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion Linearization | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.relation.projectID | TEC2017-83343-C4-2-R | es |
dc.relation.projectID | PID2020-113832RB-C21 | es |
dc.relation.projectID | 2017-SGR-813 | es |
dc.relation.projectID | 2021-FI-B-137 | es |
dc.relation.publisherversion | https://doi.org/10.3390/s21175772 | es |
dc.identifier.doi | 10.3390/s21175772 | es |
dc.journaltitle | Sensors | es |
dc.publication.volumen | 21 | es |
dc.publication.issue | 17 | es |
dc.publication.initialPage | 5772 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | Generalitat de Catalunya | es |
dc.contributor.funder | European Commission. Fondo Social Europeo (FSO) | es |