Time-domain neural network characterization for dynamic behavioral models of power amplifiers

This paper presents a black-box model that can be applied to characterize the nonlinear dynamic behavior of power amplifiers. We show that time-delay feed-forward neural networks can be used to make a large-signal input-output time-domain characterization, and to provide an analytical form to predict the amplifier response to multitone excitations. Furthermore, a new technique to immediately extract Volterra series models from the neural network parameters has been described. An experiment based on a power amplifier, characterized with a two-tone power swept stimulus to extract the behavioral model, validated with spectra measurements, is demonstrated.