Memory polynomial based support vector regression dynamic behavioral model for Doherty power amplifiers

Abstract In this article, a novel behavior modeling methodology for radio frequency (RF) power amplifiers (PAs) is introduced. The model is targeted towards strongly nonlinear Doherty power amplifiers (DPAs), combining the memory polynomial (MP) topology with the existing support vector regression (SVR) algorithm. The resulting novel model, is termed the memory polynomial support vector regression (MP‐SVR) model. Experimental validation proceeds by applying the proposed modeling method to both two standard gallium nitride (GaN) DPA with different nonlinearity, as well as a multi‐transistor GaN DPA. Compared with traditional Volterra based models, the standard SVR model, the augmented SVR (ASVR) model and the new augmented SVR (NASVR) model, the proposed MP‐SVR model gives superior prediction accuracy in both cases. This shows the efficacy of the proposed modeling method.