Low Complexity Adaptive Model for Digital Predistortion of RF Power Amplifiers in Time-Varying Configurations

Abstract

A novel behavioral modeling approach called adaptive model tree (AMT) is proposed for digital predistortion (DPD) of RF power amplifiers (PAs) in fixed and time-varying configurations. The AMT model is piecewise based on the decision tree and the reduced-complexity full basis-propagating selection (RC-FBPS) model. A novel two-step joint iterative algorithm is proposed to achieve a good match between the decision tree and the submodels obtained from the RC-FBPS model. The AMT model inherits and enhances the respective advantages of the decision tree and RC-FBPS model to have a powerful adaptive capability potentially. The experimental tests on a Doherty PA confirm that the AMT model can achieve a better trade-off between linearization performance and complexity than the state-of-the-art model in the fixed configuration. Furthermore, to characterize and compensate for the complex dynamic nonlinear distortions of PAs in time-varying configurations, the piecewise modeling technique in time-varying configurations is proposed and applied to the AMT model in this article. The experimental results confirm that the AMT model achieves excellent linearization performance with very low complexity in time-varying configurations and good generalization performance for new configuration combinations that are not used for training.