Particle filter algorithms for joint blind equalization/decoding of convolutionally coded signals

This work introduces the use of particle filters for joint blind equalization/decoding of convolutionally coded signals transmitted over frequency selective channels. As in the equalization-only case, we show how to evaluate the optimal importance function recursively via a bank of Kalman filters. Numerical simulation investigations using both stochastic and deterministic particle selection strategies show the outstanding superiority of the deterministic joint equalization/decoding method over approaches that perform blind equalization using particle filters prior to optimal decoding.