Legendre Wavelet Neural Networks for Power Amplifier Linearization

Xiaoyang Zheng, Zhengyuan Wei, Xiaozeng Xu

College of Mathematics and Statistics, Chongqing University of Technology, Chongqing, China.

DOI: 10.4236/am.2014.520302                

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.

Keywords

Power Amplifier, Pre-Distortion, Legendre Wavelet, Legendre Wavelet Neural Networks

Conflicts of Interest

The authors declare no conflicts of interest.

References

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[14]	Liu, T., Boumaiza, S. and Ghannouchi, F.M. (2006) Augmented Hammerstein Predistorter for Linearization of Broad-Band Wireless Transmitters. IEEE Transactions on Microwave Theory and Techniques, 54, 1340-1349. http://dx.doi.org/10.1109/TMTT.2006.871230
[15]	Alpert, B., Beylkin, G., Gines, D. and Vozovoi, L. (2002) Adaptive Solution Partial Differential Equations in Multiwavelet Bases. Journal of Computational Physics, 182, 149-190. http://dx.doi.org/10.1006/jcph.2002.7160
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[19]	Zhou, G.T. and Kenney, J.S. (2002) Predicting Spectral Regrowth of Nonlinear Power Amplifiers. IEEE Transactions on Communications, 50, 718-722. http://dx.doi.org/10.1109/TCOMM.2002.1006553
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[21]	Figueroa, J., Cousseau, J. and de Figueiredo, R. (2004) A Simplicial Canonical Piecewise Linear Adaptive Filter. Circuits, Systems, and Signal Processing, 5, 365-386. http://dx.doi.org/10.1007/s00034-004-0808-6
[22]	Zhou, D. and De Brunner, V.E. (2007) Novel Adaptive Nonlinear Predistorters Based on the Direct Learning Algorithm. IEEE Transactions on Signal Process, 55, 120-130. http://dx.doi.org/10.1109/TSP.2006.882058