abstract
- © 2022 IEEE.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.