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

Chaotic modulation is an important spread spectrum (SS) technique amongst c haotic communications. The logistic chaotic signal acts as the modulation s ignal in this paper. An adaptive demodulator based on the radial basis func tion (RBF) neural network is proposed. The demodulator makes use of the goo d approximate capacity of RBF network for a nonlinear dynamical system. Usi ng the proposed adaptive learning algorithm, the source message can be reco vered fi om the received SS signal. The recovering procedure is on line and adaptive. The simulated examples are included to demonstrate the new metho d. For the purpose of comparison, the extended-Kalman-filter-based (EKF) de modulator was also performed. The results indicate that the mean square err or (MSE) of the recovered source signal by the proposed demodulator is sign ificantly reduced, especially for the SS signal with a higher signal-to-noi se ratio (SNR).