Blind equalization by sequential importance sampling

This paper introduces a novel blind equalization algorithm for frequency-selective channels based on a Bayesian formulation of the problem and the Sequential Importance Sampling (SIS) technique. SIS methods rely on building a Monte Carlo (MC) representation of the probability distribution of interest that consists of a set of samples and associated weights, computed recursively in time. We elaborate on this principle to derive a blind sequential algorithm that performs Maximum A Posteriori (MAP) symbol detection without explicit estimation of the channel parameters.