Boosted Model Tree-Based Behavioral Modeling for Digital Predistortion of RF Power Amplifiers

In this article, we propose a new behavioral modeling approach, called boosted model tree, to characterize and compensate for the complex nonlinear distortions induced by wideband high-efficiency radio frequency power amplifiers. With proposed model, input data are classified into different zones decision trees each zone is assigned separate submodels. We also employ boosting technique build multiple parallel tree structures that jointly desired behavior. By designing dedicated optimization procedures, both submodel coefficients can be efficiently identified. It demonstrated combination of piecewise provides powerful hardware-efficient way memory effect cross terms. Based on experimental results, method achieve improved linearization performance with low hardware complexity under challenging predistortion scenarios.