Physically Inspired Neural Network Model for RF Power Amplifier Behavioral Modeling and Digital Predistortion

Abstract

In this paper, a novel two hidden layers artificial neural network (2HLANN) model is proposed to predict the dynamic nonlinear behavior of wideband RF power amplifiers (PAs). Starting with a generic low-pass equivalent circuit of the PA, several circuit transformations are applied in order to build an appropriate artificial neural network structure and improve the modeling accuracy. This approach culminates in the development of a real-valued and feed-forward 2HLANN-based model. The parameters (number of neurons, memory depth, etc.) of the proposed model and the back propagation learning algorithm (learning rate, momentum term, etc.) used for its training were carefully studied and thoughtfully chosen to ensure the generality of the constructed model. The validation of the proposed models in mimicking the behavior of a 250-W Doherty amplifier driven with a 20-MHz bandwidth signal is carried out in terms of its accuracy in predicting its output spectrum, dynamic AM/AM and AM/PM characteristics, and in minimizing the normalized mean square error. In addition, the linearization of the Doherty PA using the 2HLANN enabled attaining an output power of up to 46.5 dBm and an average efficiency of up to 40% coupled with an adjacent channel power ratio higher than 50 dBc.