Particle Filters for Joint Blind Equalization and Decoding in Frequency-Selective Channels

This paper introduces new algorithms for joint blind equalization and decoding of convolutionally coded communication systems operating on frequency-selective channels. The proposed method is based on particle filters (PF), recursively approximating maximum a posteriori (MAP) estimates of the transmitted data without explicitly determining channel parameters. Further elaborating on previous works, we assume that both the channel order and the noise variance are unknown random variables, and develop a new formulation for PF weight propagation which allows these quantities to be analytically integrated out. We verify via numerical simulations that the proposed methods lead to near optimal performance, closely approximating that of algorithms that require exact knowledge of all channel parameters.