Effects of power amplifier memory on adaptive feedforward linearizers

Abstract

Feedforward linearization is one of the most well-known and widely-applied methods for linearizing power amplifiers (PA). In order to prevent performance degradation due to implementation inaccuracies, adaptive or self-designing structures utilizing e.g. gradient-descent type methods have been developed. Although the basic feedforward structure as such is insensitive to PA memory, the effects of memory on the adaptation behavior can be significant. In this paper, we present an analysis on the convergence of feedforward linearizer coefficients and the resulting reduction of inter-modulation distortion (IMD) when gradient-descent type adaptations are used with a PA that exhibits memory. A Hammerstein model is used for PA modeling, and computer simulations are used to demonstrate the validity and accuracy of the analysis.