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dc.contributor.authorPham, Thi Quynh Anh
dc.contributor.authorLópez Bueno, David
dc.contributor.authorMontoro López, Gabriel
dc.contributor.authorGilabert Pinal, Pere Lluís
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.identifier.citationPham, T., López, D., Montoro, G., Gilabert, P. L. Adaptive principal component analysis for online reduced order parameter extraction in PA behavioral modeling and DPD linearization. A: IEEE MTT-S International Microwave Symposium. "Proceedings of the 2018 IEEE/MTT-S International Microwave Symposium : 10-15 June 2018, Philadelphia, Pennsylvania, USA". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 160-163.
dc.description©2018 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.
dc.description.abstractThis paper presents a new method, based on the adaptive principal component analysis (APCA) technique, that iteratively creates and updates an orthogonal data matrix used to estimate the parameters of power amplifier (PA) behavioral models or digital predistortion (DPD) linearizers. Unlike the conventional PCA, the proposed block deflacted APCA (BD-APCA) is an iterative and online method that can be easily implemented in embedded processors. The proposed BD-APCA is designed by properly modifying the well-known complex domain generalized Hebbian algorithm (CGHA). This adaptation method enhances the robustness of the parameter estimation, simplifies the adaptation by reducing the number of estimated coefficients and due to the orthogonality of the new basis, these parameters can be estimated independently, thus allowing for scalability. Experimental results show that the proposed BD-APCA method is a worthy solution for adaptive, online, reduced-order and robust parameter estimation for PA modeling and DPD.
dc.format.extent4 p.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject.lcshPower amplifiers
dc.subject.otherDigital predistortion
dc.subject.otherPower amplifier
dc.subject.otherPrincipal component analysis
dc.subject.otherBehavioral research
dc.subject.otherIterative methods
dc.subject.otherParameter estimation
dc.subject.otherPower amplifiers
dc.subject.otherAdaptation methods
dc.subject.otherAdaptive principal component analysis
dc.subject.otherBehavioral model
dc.subject.otherComplex domains
dc.subject.otherDigital predistortion
dc.subject.otherEmbedded processors
dc.subject.otherGeneralized Hebbian algorithm
dc.subject.otherRobust parameter estimation
dc.subject.otherPrincipal component analysis
dc.titleAdaptive principal component analysis for online reduced order parameter extraction in PA behavioral modeling and DPD linearization
dc.typeConference report
dc.subject.lemacAmplificadors de potència
dc.contributor.groupUniversitat Politècnica de Catalunya. CSC - Components and Systems for Communications Research Group
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (published version)
upcommons.citation.authorPham, T.; López, D.; Montoro, G.; Gilabert, Pere L.
upcommons.citation.contributorIEEE MTT-S International Microwave Symposium
upcommons.citation.publicationNameProceedings of the 2018 IEEE/MTT-S International Microwave Symposium : 10-15 June 2018, Philadelphia, Pennsylvania, USA

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