Applications of feedforward neural networks to WCDMA power amplifier model

In this paper, we propose a feedforward neural network model (FFNN) of the class B power amplifier (PA) for wideband code division multiple access (WCDMA) communication systems. The amplifier operation frequency is 1920-1980MHz, and 1dB compression output power is 30dBm. According to the measured data, we get the AM/AM and AM/PM neural network model which uses back propagation (BP) training algorithm. The simulation result shows that the characteristic of the NN model matches that of the power amplifier well. The model is very effective for characterizing the power amplifiers. One application of the power amplifiers model is linear compensation techniques such as predistortion methods, which are used to improve the severe nonlinearity of AM/AM and AM/PM characteristics.