Adaptive predistortion of Hammerstein systems based on indirect learning architecture and prediction error method

This paper considers the problem of predistortion of nonlinear systems which are described using IIR Hammerstein models by connecting two adaptive IIR Wiener systems. The first adaptive Wiener system is a training filter connected in parallel with the nonlinear system and its coefficients are estimated recursively using the Recursive Prediction Error Method (RPEM) algorithm. The second adaptive Wiener system is a predistorter connected tandemly with the nonlinear system and its coefficients are a copy from the training Wiener system. Simulation results show that the suggested RPEM algorithm effectively reduces spectral regrowth due to nonlinear distortion.