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A new adaptive technique for digital predistortion is presented. The proposed method uses the real-time digital processing of baseband signals to compensate the nonlinearities and memory effects in radio-frequency Power Amplifier. Kalman filtering algorithm with sliding time-window is adapted to track the changes in the PA characteristics. Simulation and measurement results, using digital signal processing, are presented for multicarrier signals to demonstrate the effectiveness of this new approach.

In this paper, we present a new identification procedure for radio frequency Power Amplifier (PA) in the presence of nonlinear distortion and memory effects. The proposed procedure uses a continuous-time model where PA dynamics are modeled with a multivariable filter and a general polynomial function. Using the baseband input and output data, model parameters are obtained by an iterative identification algorithm. Finally, the proposed estimation method is tested and validated on experimental data by comparison of the quadrature IQ signals in time domain. I. IntroductionNumerous approaches in Power Amplifier identification area have been developed to characterize the input to output complex envelope relationship [1][2] [3]. The model forms used in identification are generally classified into two methods depending on the physical knowledge of the system: discrete and continuous model. A discrete model is a system where no physical insight and prior information available. This approach have been widely used in many research studies to predict and linearize the output of the Nonlinear PA such as neural networks and Volterra series [4][5][6][7]. However, this method suffer from the high number of parameters and the time consuming in computation. On the opposite, continuous model is a system where the mathematical representation, under some assumptions, is perfectly known. The drawback of this model is the complexity of the electrical modelling but the main advantage is that the resulting parameters have physical significance like gain conversion, damping coefficient and cut-off frequency [10].The model considered in this paper is a Multi-Input/MultiOutput (MIMO) system described in continuous-time domain. This structure is similar to Hammerstein discrete-time model including nonlinear transfer functions and multivariable continuous filter.Model parameters are achieved using an iterative identification algorithm based on Output Error (OE) method. This technique is based on minimization of a quadratic criterion by a Non Linear Programming (NLP) algorithm. This technique requires much more computation and do not converge to unique optimum. But, OE methods present very attractive features, because the simulation of the output model is based only