A PSO Based Memory Polynomial Predistorter With Embedded Dimension Estimation

Power amplifiers are widely used in RF broadcasting applications. However, they tend to exhibit nonlinear behavior that distorts the input signals both in the time and frequency domains, consequently motivating the development of techniques, such as digital predistortion, which can counteract this behavior. Among the challenges facing the identification of an amplifier's digital predistorter and behavioral model is finding the correct model dimensions, as this requires a priori knowledge of multiple parameters. In this paper, a predistorter based on a cluster-based implementation particle swarm optimization technique with embedded model-size estimation capability is presented. The validation of the proposed technique on a Doherty power amplifier prototype demonstrates its ability to efficiently find the dimensions of a memory polynomial based digital predistorter, while accurately estimating its coefficients.