Recurrent neural network technique for behavioral modeling of power amplifier with memory effects

Abstract

A new technique for behavioral modeling of power amplifier PA with short- and long-term memory effects is presented here using recurrent neural networks RNNs. RNN can be trained directly with only the input-output data without having to know the internal details of the circuit. The trained models can reflect the behavior of nonlinear circuits. In our proposed technique, we extract slow-changing signals from the inputs and outputs of the PA and use these signals as extra inputs of RNN model to effectively represent long-term memory effects. The methodology using the proposed RNN for modeling short-term and long-term memory effects is discussed. Examples of behavioral modeling of PAs with short- and long-term memory using both the existing dynamic neural networks and the proposed RNNs techniques are shown. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:289-298, 2015.