dc.creator | Crespo Cadenas, Carlos | es |
dc.creator | Madero Ayora, María José | es |
dc.creator | Becerra González, Juan Antonio | es |
dc.creator | Cruces Álvarez, Sergio Antonio | es |
dc.date.accessioned | 2022-07-01T18:32:21Z | |
dc.date.available | 2022-07-01T18:32:21Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Crespo Cadenas, C., Madero Ayora, M.J., Becerra, J.A. y Cruces, S.A. (2022). A Sparse-Bayesian Approach for the Design of Robust Digital Predistorters Under Power-Varying Operation. IEEE Transactions on Microwave Theory and Techniques, 70 (9) | |
dc.identifier.issn | 0018-9480 | es |
dc.identifier.issn | 1557-9670 | es |
dc.identifier.uri | https://hdl.handle.net/11441/134935 | |
dc.description | "Early access" | es |
dc.description.abstract | In this article, a sparse-Bayesian treatment is
proposed to solve the crucial questions posed by power amplifier
(PA) and digital predistorter (DPD) modeling. To learn a
model, the advanced Bayesian framework includes a group of
specific processes that maximize the likelihood of the measured
data: regressor pursuit and identification, coefficient estimation,
stopping criterion, and regressor deselection. The relevance
vector machine (RVM) method is reformulated theoretically to
be implemented in complex-valued linear regression. In essence,
given an initial set of candidate regressors, the result of this
sparse-Bayesian learning approach is the most likely model.
Experimental results are provided for the linearization of class
AB and class J PAs driven by a 30-MHz fifth-generation new
radio signal for a fixed average power, where the evolution of
the figures of merit versus the number of active coefficients
is examined for the proposed sparse-Bayesian pursuit (SBP)
algorithm in comparison to other greedy algorithms. The SBP
presents a good performance in terms of linearization capabilities
and computational cost. Furthermore, the proposed Bayesian
framework enabled the design of a DPD model structure,
deselect regressors, and readjust coefficients in a direct learning
architecture, demonstrating the robustness to changes in the
power level over a 10-dB range. | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación 10.13039/501100011033 | es |
dc.description.sponsorship | Junta de Andalucía - Fondos FEDER US-1264994 | es |
dc.format | application/pdf | es |
dc.format.extent | 13 p. | es |
dc.language.iso | eng | es |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | es |
dc.relation.ispartof | IEEE Transactions on Microwave Theory and Techniques, 70 (9) | |
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 | es |
dc.subject | Nonlinear model identification | es |
dc.subject | Power amplifier (PA) | es |
dc.subject | Volterra series | es |
dc.title | A Sparse-Bayesian Approach for the Design of Robust Digital Predistorters Under Power-Varying Operation | 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 | TEC2017-82807-P | es |
dc.relation.projectID | 10.13039/501100011033 | es |
dc.relation.projectID | US-1264994 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9738849 | es |
dc.identifier.doi | 10.1109/TMTT.2022.3157586 | es |
dc.journaltitle | IEEE Transactions on Microwave Theory and Techniques | es |
dc.publication.volumen | 70 | |
dc.publication.issue | 9 | |