An adaptive demodulator for the chaotic modulation communication system with RBF neural network

Chaotic modulation is an important spread spectrum (SS) technique amongst chaotic communications. The logistic chaotic signal acts as the modulation signal in this paper. An adaptive demodulator based on the radial basis function (RBF) neural network is proposed. The demodulator makes use of the good approximant capacity of RBF network for a nonlinear dynamical system. Using the proposed adaptive learning algorithm, the source message can be recovered from the received SS signal. The recovering procedure is on line and adaptive. The simulated examples are included to demonstrate the new method. For the purpose of comparison, the extended-Kalman-filter-based (EKF) demodulator was also analysed. The results indicate that the mean square error (MSE) of the recovered source signal by the proposed demodulator Is significantly reduced, especially for the SS signal with a higher signal-to noise ratio (SNR).

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