Artículo
Sparse identification of volterra models for power amplifiers without pseudoinverse computation
Autor/es | Becerra González, Juan Antonio
Madero Ayora, María José Reina Tosina, Luis Javier Crespo Cadenas, Carlos |
Departamento | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones |
Fecha de publicación | 2021 |
Fecha de depósito | 2021-09-01 |
Publicado en |
|
Resumen | We 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 ... We 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. |
Cita | Becerra González, J.A., Madero Ayora, M.J., Reina Tosina, L.J. y Crespo Cadenas, C. (2021). Sparse identification of volterra models for power amplifiers without pseudoinverse computation. IEEE Transactions on Microwave Theory and Techniques, 68 (11), 4570-4578. |
Ficheros | Tamaño | Formato | Ver | Descripción |
---|---|---|---|---|
Sparse_Identification_of_Volte ... | 1.750Mb | [PDF] | Ver/ | |