RF power amplifiers behavioral modeling based on extended neural network with additional dynamic fuzzy weights

This paper presents an extended neural network (NN)-based behavioral model of RF power amplifiers (PAs) with memory effects. PA behavioral models are traditionally constructed based on the relationship between the input and output of PA, without regard to the physical mechanisms of distortions in PA however. To more accurately predict the dynamic nonlinear behavior of PA, the novel proposed NN-based behavioral model takes into account the characteristics of the amplitude modulation to amplitude modulation and amplitude modulation to phase modulation distortions that are incorporated into the behavioral model by means of additional dynamic fuzzy weights. In addition, the NN-based behavioral model employs orthonormal Hermite polynomial functions rather than commonly used sigmoid function, as nonlinear activation functions resulting in significant improvement in accuracy and convergence. Experimental results on a class-AB PA driven with the wideband Mobile Multimedia Broadcasting signals show a superior performance of the proposed extended NN-based model over the traditional NN-based ones.