Abstract
In this paper, a novel technique for power amplifier (PA) linearization is presented. The Legendre wavelet neural networks (LWNN) is first utilized to model PA and inverse structure of the PA by applying practical transmission signals and the gradient descent algorithm is applied to estimate the coefficients of the LWNN. Secondly, this technique is implemented to identify and optimize the coefficient parameters of the proposed pre-distorter (PD), i.e., the inversion model of the PA. The proposed method is most efficient and the pre-distorter shows stability and effectiveness because of the rich properties of the LWNN. A quite significant improvement in linearity is achieved based on the measured data of the PA characteristics and out power spectrum has been compared.
Highlights
A lot of efforts have been made to improve the linearity of the power amplifier (PA) [1]-[3]
The approach presented in this paper has advantages: the Legendre wavelet neural networks (LWNN) is characterized by small network size and polynomial activation functions and fast learning speed; Legendre wavelet bases are expressed in closed form; adaptive piecewise approximation at different decomposition level; overcome the stagnation in the nonlinear system identification
In a second step, applying practical transmission signals (2013 China Post-Graduate Mathematic Contest in Modeling), the different polynomial bases including of the Legendre wavelet are used to PA model and compared with respect to the normalized mean squared error (NMSE) of the LWNN by using gradient descent algorithm
Summary
A lot of efforts have been made to improve the linearity of the PA [1]-[3]. Digital baseband pre-distortion is one of the most cost effective. Such of the stated problem mainly exists two difficulties which need to be overcome. The approach presented in this paper has advantages: the LWNN is characterized by small network size and polynomial activation functions and fast learning speed; Legendre wavelet bases are expressed in closed form; adaptive piecewise approximation at different decomposition level; overcome the stagnation in the nonlinear system identification.
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