A novel adaptive nonlinear predistorter based on the direct learning algorithm

The nonlinear predistorter is an effective technique to compensate the nonlinear distortions existing in a digital communication system. However, available adaptive nonlinear predistorters are either based on the indirect learning algorithm, or are complicated in structure and computation. In this paper, we propose a novel adaptive nonlinear predistorter based on a direct learning algorithm: the adjoint nonlinear LMS algorithm. Because of the direct learning algorithm, our adaptive predistorter outperforms the other nonlinear predistorters that are based on the indirect learning method in the sense of mean square error (MSE). Moreover, compared with any other adaptive nonlinear predistorter based on the direct learning architecture, our predistorter has a simpler structure and lower computational complexity. Simulation results show the effectiveness of our nonlinear adaptive predistorter.