Wavelet-Based Sequential Monte Carlo Blind Receivers in Fading Channels With Unknown Channel Statistics

Abstract

Recently, an adaptive Bayesian receiver for blind detection in flat-fading channels was developed by the present authors, based on the sequential Monte Carlo methodology. That work is built on a parametric modeling of the fading process in the form of a state-space model and assumes the knowledge of the second-order statistics of the fading channel. In this paper, we develop a nonparametric approach to the problem of blind detection in fading channels, without assuming any knowledge of the channel statistics. The basic idea is to decompose the fading process using a wavelet basis and to use the sequential Monte Carlo technique to track both the wavelet coefficients and the transmitted symbols. A novel resampling-based wavelet shrinkage technique is proposed to dynamically choose the number of wavelet coefficients to best fit the fading process. Under such a framework, blind detectors for both flat-fading channels and frequency-selective fading channels are developed. Simulation results are provided to demonstrate the excellent performance of the proposed blind adaptive receivers.