Joint blind equalization and detection in chaotic communication systems using simulation-based methods

In this paper an importance sampling (IS)-based technique is proposed to achieve the blind equalizer and detector for chaotic communication systems. Chaotic signals are generated using nonlinear dynamical systems. These signals have wide applications in communication as a result of their appropriate properties such as pseudo-randomness, large bandwidth, and unpredictability for long time. Based on the different chaotic signal properties, different communication methods such as chaotic modulation, masking, and spread spectrum have been proposed before. In this paper, chaos masking is adopted for transmitting modulated message symbols over an unknown channel, in which the joint demodulation and equalization is a nonlinear problem. Several methods such as extended Kalman filter (EKF), particle filter (PF), minimum nonlinear prediction error (MNPE), have been previously presented for this problem. Here, a new approach, based on Monte Carlo sampling, is proposed to joint channel equalization and demodulation. At the receiver end, importance sampling is used to detect binary symbols according to maximum likelihood (ML) criterion. Simulation results show that the proposed method has better performance, compared to existing methods, especially at low SNR.