A fixed-lag particle smoother for blind SISO equalization of time-varying channels

We introduce a new sequential importance sampling (SIS) algorithm which propagates in time a Monte Carlo approximation of the posterior fixed-lag smoothing distribution of the symbols under doubly-selective channels. We perform an exact evaluation of the optimal importance distribution, at a reduced computational cost when compared to other optimal solutions proposed for the same state-space model. The method is applied as a soft input

• Type: Journal
• Year: 2010
• Where: TWC
• Authors: Alberto Gaspar Guimarães, Boujemaa Ait-El-Fquih, François Desbouvries
• Keywords: Artificial Intelligence, Monte Carlo Approximation, Optimal Importance Distribution, Posterior Fixed-lag Smoothing, TWC 2010