Blind Detection and Equalization in Chaotic Communication Systems Using Importance Sampling

In this paper an Importance Sampling technique is proposed to achieve blind equalizer and detector for chaotic communication systems. Chaotic signals are generated with dynamic nonlinear systems. These signals have wide applications in communication due to their important properties like randomness, large bandwidth and unpredictability for long time. Based on the different chaotic signals properties, different communication methods have proposed such as chaotic modulation, masking, and spread spectrum. In this article, chaos masking is assumed for transmitting modulated message symbols. In this case, channel estimation is a nonlinear problem. Several methods such as extended Kalman filter (EKF), particle filter (PF), minimum nonlinear prediction error (MNPE) and. .. are previously presented for this problem. Here, a new approach based on Monte Carlo sampling is proposed to joint channel estimation and demodulation. At the receiver end, Importance Sampling is used to detect binary symbols according to maximum likelihood criteria. Simulation results show that the proposed method has better performance especially in low SNR.