Reliable comparison for power amplifiers nonlinear behavioral modeling based on regression trees and random forest

被引：2

作者：

Aguila-Torres, Daniel Santiago ^{[1
]}

Alejandro Galaviz-Aguilar, Jose ^{[2
]}

Ricardo Cardenas-Valdez, Jose ^{[1
]}

机构：

[1] IT Tijuana, Tecnol Nacl Mexico, Tijuana, Mexico

[2] Tecnol Monterrey, Sch Sci & Engn, Monterrey, Mexico

来源：

2022 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS 22) | 2022年

关键词：

regression tree; random forest; digital predistortion; power amplifier; linearization;

D O I：

10.1109/ISCAS48785.2022.9937863

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This work evaluates the construction of feature extraction nonlinear behavioral models based on Regression Trees and Random Forest techniques. A framework to evaluate the effectiveness with enough-accuracy regressor models are evaluated to aid in the design of a digital predistorter (DPD) for the power amplifier (PA) linearization. The comparison with a conventional memory polynomial model (MPM) and two ensemble learning models is performed to reveal the ability in decision and region identification without overfitting for the Regression Tree and a Random Forest algorithms.

引用

页码：1527 / 1530

页数：4

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