Offline training adaptive predistortion method based on BP inverse model

In order to further improve the linearity of nonlinear power amplifier system,this paper proposed an adaptive predistortion method that was off-line training based on BP neural network inverse modeling.Firstly,it used BP neural network for inverse modeling of the amplifier,and took parameters of inverse model which had established as the initial of the predistortion model,then,in order to improve the effect of linearization of predistortion in the initial predistortion system,and accelerate the adaptation process of predistortion system,before establishing adaptive predistortion system,predistorter was off-line trained by BP inverse model.Finally,using direct structure and the LMS algorithm to adjust the weights of neural network predistorter,so as to eliminate nonlinear perturbations of amplifier.Simulation results show that this scheme can make adjacent channel intermodulation power reduce by about 18 dB,while the classical direct-indirect structure only reduced 8 dB.It indicates that this predistortion scheme can improve the linearity of the power amplifier better.