Improvements and analysis of nonlinear parallel behavioral models

Abstract

This article performs an analysis of current limitations regarding the extraction of parallel behavioral models to reproduce the power amplifier (PA) nonlinear behavior and its dynamics. To overcome these limitations, a general preprocessing block that clearly improves the identification capabilities shown by classical parallel structures is proposed. It follows the principle of separating both static and dynamic nonlinear behavior of the PA to obtain a better identification performance. A comparison with common parallel configurations using linear estimation is performed, to highlight the benefits of using the preprocessing structure. Furthermore, a new nonlinear parallel structure using sub-band filtering techniques is also proposed. For the models extraction and comparison, four types of noise-free simulated data presenting different levels of nonlinearities and memory, as well as a measured signal obtained from a laboratory amplifier have been considered.