Comparative Analysis of Greedy Pursuits for the Order Reduction of Wideband Digital Predistorters
This paper provides a review of greedy pursuits for optimizing Volterra-based behavioral models structure and estimating its parameters. An experimental comparison of the digital predistortion (DPD) linearization performance achieved by these approaches for model-order reduction, such as compressive...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/129890 |
| Acceso en línea: | https://hdl.handle.net/11441/129890 https://doi.org/10.1109/TMTT.2019.2928290 |
| Access Level: | acceso abierto |
| Palabra clave: | Adaptive algorithms Greedy algorithms Modeling Nonlinear systems Power amplifiers (PAs) Predistortion Volterra series |
| Sumario: | This paper provides a review of greedy pursuits for optimizing Volterra-based behavioral models structure and estimating its parameters. An experimental comparison of the digital predistortion (DPD) linearization performance achieved by these approaches for model-order reduction, such as compressive sampling matching pursuit (CoSaMP), subspace pursuit (SP), orthogonal matching pursuit (OMP), and the novel doubly OMP (DOMP), is presented. A benchmark of the techniques in the DPD of a commercial class AB power amplifier (PA) and a class J PA operating over a 15-MHz Long-Term Evolution (LTE) signal is presented, giving a clear overview of their pruning characteristics in terms of linearization indicators and regressor selection capabilities. In addition, the benchmark is run in a cross-validation scheme by identifying the DPD with a 30-MHz 5G-new radio (NR) signal and validating with the same signal and a 20-MHz multicarrier wideband code division multiple access (WCDMA) signal. The DOMP is shown to be a promising technique since it achieves an enhanced model-order reduction for a similar linearization performance and precision. |
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