NARX Neural Network Bandwidth Generalization Capability in Power Amplifier Modeling

Nonlinear AutoRegressive with eXogenous input Neural Network (NARXNN) can exhibit strong generalization capabilities and rapid convergence by combining all features of a recurrent neural network (RNN), and the ability of training as a purely feedforward architecture,. As a result, it can be used to simulate the dynamic nonlinear behavior of radio frequency (RF) power amplifiers (PAs). In this paper, a comparative study of NARXNN and real-valued focused time-delay neural networks (RVFTDNN) has been executed to demonstrate that NARXNN is a more suitable option for describing the functions of PAs in terms of bandwidth generalization. The validation of NARXNN is carried out in two scenarios: i) single bandwidth and ii) bandwidth generalization using MATLAB-provided measured signals. By evaluating the accuracy in the predicting output spectrum, the normalized mean square error, and the complexity of the models, NARXNN performs a significantly reduced number of coefficients and a dominant precision compared to RVFTDNN.