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Broadband Power Amplifiers Behavioral Modeling Method Based on Instantaneous Input Power

This paper presents a novel Augmented Memory Polynomial (AMP) model for the broadband radio frequency high power amplifiers behavioral modeling and digital predistortion. The proposed model consists of a weighted memoryless sub model and a weighted memory sub model. The weighted memoryless sub model involving a weighting, polynomial error signal, and de-weighting parts is used for predicting the strong static nonlinear behaviors of the power amplifiers. The weighted memory sub model is used for the dynamic memory nonlinearity by introducing an instantaneous power dependent weight function into the static and dynamic terms of the conventional memory polynomial (CMP) model. The performance of the proposed model is assessed in terms of in-band and out-of-band errors, such as normalized mean square error (NSME) and adjacent channel power ratio (ACPR). Compared with the CMP, the weighted memory polynomial (WMP) is complex in computation due to the numbers of model coefficient. Furthermore, the proposed method is applied to the digital predistortion so as to cancel the nonlinearity of the power amplifier. The experimental results show that the proposed for modelling the power amplifier and applying the digital predistortion is feasible and effective because it can result in up to 10dB improvement in the ACPR for the same number of coefficients.