Prediction of state transitions in Rayleigh fading channels using particle filter

This paper presents a new method based on particle filter theory in the presence of non_gaussian noise of environment for cognitive radio systems. It has been shown that a broad and increasingly important class of non-Gaussian phenomena encountered in practice can be characterized as impulsive noise [1]. Herein alpha-stable distribution is proposed for such a noise. For the proposed noise model, we apply particle filter to estimate CIR, which is rooted in Bayesian estimation and Monte Carlo simulation. To our knowledge, the implementation of the Particle filter is novel for such a system. Furthermore we compared performance of Kalman filter and Particle filter in the presence of non_gaussian noise environment. Our results reveals that filter predictor has better results than Kalman filter for a non-Gaussian noise environment.