Power Amplifier Behavioral Modeling Through Convolutional Neural Networks

Abstract

In this document we present an approach to characteristic modeling of power amplifiers using convolutional neural networks. Radio Frequency Power Amplifiers suffer from distortions when they approach high efficiency levels, and by having a model of how the amplifier is going to distort radiofrequency signals, we can pre-distort the signal before hand with the inverse characteristic to mitigate the distortion the amplifier applies. To do so, we propose a convolutional neural networks. Neural network behave like universal function approximators given enough data. We present that this architecture is fairly successful when modeling nonlinearities on three different amplifiers while keeping its complexity and number of parameters to reasonable levels. Lastly, we replicate results on similar research showing that feeding certain transformations of the inputs to the network leads to better modeling of the amplifier without raising the number of parameters much. Because of the inherent performance of convolutional neural networks, we can augment the input data both memory-wise and order-wise without adding many parameters to the model.