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Wavelet Neural Networks and Equalization of Nonlinear Satellite Communication Channel
Abstract
Wavelet neural networks are a class of single hidden layer neural networks consisting of wavelets as activation functions. Wavelet neural networks (WNN) are an alternative to the classical multilayer perceptron neural networks for arbitrary nonlinear function approximation and can provide compact network representation. In this chapter, a tutorial introduction to different types of WNNs and their architecture is given, along with its training algorithm. Subsequently, a novel application of WNN for equalization of nonlinear satellite communication channel is presented. Nonlinearity in a satellite communication channel is mainly caused due to use of transmitter power amplifiers near its saturation region to improve efficiency. Two models describing amplitude and phase distortion caused in a power amplifier are explained. Performance of the proposed equalizer is evaluated and compared to an existing equalizer in literature.
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