Behavioral modeling and digital predistortion of RF power amplifiers based on time-delay kernel ridge regression

Abstract

This paper, investigates and presents the behavioral modeling and digital pre-distortion (DPD) of radio frequency power amplifiers (RFPAs) using a time-delay kernel ridge regression (KRR) algorithm. The KRR is an advanced machine learning algorithm that can be effectively used for modeling the baseband characteristics of the RFPA considering both the effects of memory and transistor non-linearity. Compared to the traditional artificial neural network (ANN) based approach which is computationally intensive, the proposed approach using KRR with radial basis kernel function and min-max normalization method, extracts the PA behavioral and DPD model in a short time and yield consistently better results. The performance of the proposed approach in extracting PA and DPD model is demonstrated experimentally on a GaN based class AB power amplifier. The experimental results illustrates that, when compared to ANN based model, the proposed approach yields more accurate PA and DPD models, with an improvement in modeling performance by 2 dB in terms of normalized mean square error (NMSE). In addition, compared to the ANN based approach, the DPD model developed using the proposed approach exhibit improved linearization performance in suppressing spectral regrowth due to PA non-linearity.