Abstract: The behavioral model and predistorter for modeling and reducing nonlinear effects in power amplifiers addresses the model size estimation problem. The GMP model is replaced by the hybrid memory polynomial/envelope memory polynomial (HMEM) model within a twin nonlinear two-box structure to reduce the number of variables involved in the model size estimation problem, without compromising model accuracy and digital predistorter performance. A sequential approach is presented to efficiently estimate the model size. Experimental validation is carried out to evaluate the performance of the size estimation and the accuracy of the HMEM-based twin-nonlinear two-box model with respect to that of the GMP-based twin-nonlinear two-box model.

Abstract: The behavioral model and predistorter for modeling and reducing nonlinear effects in power amplifiers addresses the model size estimation problem. The GMP model is replaced by the hybrid memory polynomial/envelope memory polynomial (HMEM) model within a twin nonlinear two-box structure to reduce the number of variables involved in the model size estimation problem, without compromising model accuracy and digital predistorter performance. A sequential approach is presented to efficiently estimate the model size. Experimental validation is carried out to evaluate the performance of the size estimation and the accuracy of the HMEM-based twin-nonlinear two-box model with respect to that of the GMP-based twin-nonlinear two-box model.