Determining a Digital Predistorter Model Structure for Wideband Power Amplifiers through Random Forest

Abstract

This work presents the application of the Random Forest technique to behavioral modeling component selection. Several digital predistorters (DPDs) with a random structure are attained to obtain a sequence of the regressors importance with respect to their impact in the linearization error of a power amplifier (PA). The approach has been validated in the DPD of a commercial PA operating under a 5G-NR signal, showcasing the ability of the algorithm to sort and prune the components.