Efficient Power Amplifier Identification Using Modified Parallel Cascade Hammerstein Models

The automatic generation of behavioral amplifier models based on the measurement of the input and output signals imply demanding requirements for the identification algorithm. In addition to the computational efficiency it is very important that such an algorithm needs only a low number of predefined parameters for the fitting process. To fulfil these requirements we suggest the use of a modified parallel cascade Hammerstein model (MPCHM). To verify the performance of the presented algorithm the behavior of a 3-stage RF power amplifier (PA) was identified. The resulting model was validated using different measurements with similar statistical properties. Furthermore the static nonlinear contributions were deembedded from both the measured and the modeled output signals to visualize the capability of the MPCHM in predicting the amplifier memory effects