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Legendre Wavelet Neural Networks for Power Amplifier Linearization
College of Mathematics and Statistics, Chongqing University of Technology, Chongqing, China
College of Mathematics and Statistics, Chongqing University of Technology, Chongqing, China
College of Mathematics and Statistics, Chongqing University of Technology, Chongqing, China
- 1 College of Mathematics and Statistics, Chongqing University of Technology, Chongqing, China
- 2 College of Mathematics and Statistics, Chongqing University of Technology, Chongqing, China
- 3 College of Mathematics and Statistics, Chongqing University of Technology, Chongqing, China
Applied Mathematics·Volume 05 (2014)·Pages 3249–3255·Published 20 November 2014·DOI10.4236/am.2014.520302
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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.
KeywordsPower AmplifierPre-DistortionLegendre WaveletLegendre Wavelet Neural Networks
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