Abstract
A new adaptive predistortion method for compensation of unknown linear and nonlinear distortion in a high power amplifier (HPA) is investigated by a databased approach using the support vector regression (SVR). The proposed approach can construct an inverse model of the nonlinear HPA by learning procedure using training input-output data samples. An on-line adaptive algorithm for updating the parameters of the SVR machines is also given to reduce the computational burden of the batch processed optimization. By comparing with an ordinary look-up table scheme, it is shown that the proposed data-based approach can attain improved suppression of the power spectrum of the amplifier output outside the input frequency band caused by the nonlinear distortions.
