Application　of　Recurrent　Wavelet　Neural　Networks　to　the　Digital　Communications　Channel　Blind　Equalization

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This paper investigates the application of a Recurrent Wavelet Neural Network (RWNN)to the blind equalization of nonlinear communication channels.In contrast to the wavelet networks introduced in,the RWNN is well suited for use in real time adaptive signal processing.Furthermore,the RWNN has the advantage that a priori information of the underlying system need not be known,the dynamics of the system are configured in the recurrent connections and the network approximates the system over time.An RWNN based structure and a novel training approach for blind equalization was proposed and its performance evaluated via computer simulations for nonlinear communication channel model.It is shown that the RWNN blind equalizer performs much better than the linear Constant Modulus Algorithm (CMA) and the Recurrent Radial Basis Function (RRBF) Networks based blind equalizers in nonlinear channel case.The small size and high performance of the RWNN equalizer make it suitable for high speed channel blind equalization.