Blind equalization for time-varying channels and multiple samples processing using particle filtering

In this paper we address the problem of equalization of time-varying frequency-selective channels. We formulate the problem by modeling the frequency-selective channel by an FIR filter with time-varying tap weights whose variation is characterized by an AR process. Our approach to the problem is based on Bayesian estimation using sequential Monte Carlo filtering commonly referred to as particle filtering. This estimation method represents the target posterior distribution by a set of random discrete samples and their associated weights. In this paper, we also extend the technique of equalization using particle filtering for cases where we have multiple samples per symbol and demonstrate that significant performance improvement can be achieved by processing multiple samples. The proposed algorithm is recursive and blind for it requires no training symbols for channel estimation. However, it assumes knowledge of the variance of the additive noise and the coefficients of the AR process used to model the variation of the fading channel tap weights. The proposed scheme is highly parallelizable and hence is suitable for VLSI (very large scale integration) implementation.