Behavioral Power Amplifier Modeling Using the LASSO

In this paper we use regularization, in the form of the IASSO, as identification procedure in order to find the parameters of the parallel Hammerstein model for behavioral power amplifier modeling. It is shown that the IASSO chooses a subset of the parameters of the parallel Hammerstein model in a systematic way and thereby reduces the number of model parameters while maintaining the performance. The values of the parameters are also smaller than when the ordinary least-squares algorithm is used for the parameter extraction since the IASSO imposes a limit on the Il-norm of the parameters. Thus, the problem with large, and sometimes very large, parameters that is often encountered in behavioral power amplifier modeling is avoided. Experimental results from measurements on a power amplifier intended for the 3G WCDMA system is provided to support the theory.