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

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 po...

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Bibliographic Details
Published in2022 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 1527 - 1530
Main Authors Aguila-Torres, Daniel Santiago, Galaviz-Aguilar, Jose Alejandro, Cardenas-Valdez, Jose Ricardo
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.05.2022
Subjects
Behavioral sciences
Data models
digital predistortion
Feature extraction
linearization
power amplifier
Power amplifiers
Prediction algorithms
Predictive models
random forest
regression tree
Reliability
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Summary: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.
ISSN:2158-1525
DOI:10.1109/ISCAS48785.2022.9937863