A Wavelet neural network for detection of signals in communications

Our objective is the design and simulation of an efficient system for detection of signals in communications in terms of speed and computational complexity. The proposed scheme takes advantage of two powerful frameworks in signal processing: Wavelets and Neural Networks. The decision system will take a decision based on the computation of the a priori probabilities of the input signal. For the estimation of such probability density functions, a Wavelet Neural Network (WNN) has been chosen. The election has arosen under the following considerations: (a) neural networks have been established as a general approximation tool for fitting nonlinear models from input/output data and (b) the increasing popularity of the wavelet decomposition as a powerful tool for approximation. The integration of the above factors leads to the wavelet neural network concept. This network preserve the universal approximation property of wavelet series, with the advantage of the speed and efficient computation of a neural network architecture. The topology and learning algorithm of the network will provide an efficient approximation to the required probability density functions