Artículo
A Sparse-Bayesian Approach for the Design of Robust Digital Predistorters Under Power-Varying Operation
Autor/es | Crespo Cadenas, Carlos
Madero Ayora, María José Becerra González, Juan Antonio Cruces Álvarez, Sergio Antonio |
Departamento | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones |
Fecha de publicación | 2022 |
Fecha de depósito | 2022-07-01 |
Publicado en |
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Resumen | 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 ... 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. |
Identificador del proyecto | TEC2017-82807-P
10.13039/501100011033 US-1264994 |
Cita | 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) |
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