Direct Learning Predistorter with a New Loop Delay Compensation Algorithm

Digital baseband predistortion technology can effectively reduce the system nonlinear distortion. However, the predistortion algorithms based on indirect structure are sensitive to measurement noise and the traditional loop-delay estimation algorithms are vulnerable to the amplifier´s nonlinear distortion especially in the amplifier saturation region. In this paper, we first present an adaptive nonlinear predistorter based on direct learning structure and then propose a new loop-delay estimation algorithm which applies to PA nonlinear distortion conditions by analyzing the relationship between the system loop-delay misalignment and the amplitude of HPA model identification error output. Simulation results show that the power spectral density (PSD) after the predistortion is very close to that of the ideal signal and a good NMSE performance is achieved.