Abstract
Large signal behavioral modeling for RF power transistor has been developed rapidly in the past decade, this paper compares and contrasts recent nonlinear large-signal behavioral modeling techniques designed for power transistors. These techniques include traditional function based behavioral modeling techniques, e.g. X-parameters, Pade model, Cardiff model, load-pull X-parameters; and artificial neural network (ANN) and machine learning (ML) technique based methods, e.g. real value feed-forward neural network (RVFFNN) model, Bayesian inference model, support vector regression (SVR) based model, in the frequency domains. Besides, approaches to generating the data for modeling from active load-pull measurements are reviewed as well, including closed loop, feed-forward, and open loop active load pull system.
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