Impact of sampling domain and number of samples on the accuracy of large-signal multisine measurement-based behavioral model

Abstract

This article evaluates two alternative ways to execute the sampling of modulated data for large-signal behavioral modeling. We show that the nonlinear metric domain uniform sampling outperforms the time-domain uniform approach, and examine how the level of improvement depends on the number of samples used for model generation.