A simplified adaptive nonlinear predistorter for high power amplifiers based on the direct learning algorithm

The adaptive nonlinear predistorter is an effective technique to compensate the nonlinear distortion existing in a digital communication system. In this paper, we first apply the recently developed nonlinear filtered-x LMS and adjoint nonlinear LMS algorithm to design an adaptive Hammerstein nonlinear predistorter for a high power amplifier (HPA) preceded by a linear system. Compared with the adaptive Hammerstein nonlinear predistorter with either direct learning or indirect learning, our developed adaptive nonlinear predistorter is computationally efficient and can be easily implemented via DSP hardware and software. By exploring the robustness of our proposed algorithm and the statistical properties of our virtual filter, we further simplify the adaptive Hammerstein nonlinear predistorter to further reduce the computational complexity and implementation cost. Simulation results confirm the effectiveness of our proposed algorithm.