A Volterra Pre-distortion Behavioral Modelling for Power Amplifiers with Memory Effects

Most existing behavioral models of the radio frequency (RF) power amplifiers assume that power amplifiers are memoryless devices. But in broadband communication systems, memory effects of the RF power amplifiers are significantly observed. The traditional memoryless models can no longer accurately depict the input-output relationship of power amplifiers. This paper investigates power amplifiers memory effects and a pre-distortion method is proposed for linearizing RF power amplifiers with memory effects. A valid behavioral model based on the Volterra series is suggested to treat memory effects. Since the characteristics of amplifiers change during transmission time, a recursive least squares algorithm with size- fixed observation matrices is developed to update the parameters of the proposed power amplifiers model. The identification algorithm can decrease computational complexity and data storage space and facilitate real-time online identification. Simultaneously, the pre-distortion model is constructed to simulate. Simulation results show that the proposed method can effectively compensate for the nonlinear distortion and memory effects of RF power amplifiers. The model's accuracy is used to evaluate power amplifiers memory effects by the enormalized mean square error (NMSE). In the end, simulation and evaluation of this pre-distortion system proceeded, and the power spectral density estimation was also applied to calculate the adjacent channel power ratio.