Joint source-channel decoding of correlated sources over ISI channels

We propose to combine turbo equalization and source-channel coding for the transmission of correlated sources over sensor networks. Concretely, we study an asymmetrical scheme where two correlated sources, S/sub 1/ and S/sub 2/, have to be transmitted to a central node. Source S/sub 1/ is compressed and transmitted over a Gaussian ISI channel, whereas source S/sub 2/ is assumed to be available as side information at the receiver. The correlation between sources has memory and is modelled by a hidden Markov model (HMM). For maximum a posteriori (MAP) and minimum mean squared error (MMSE) equalization techniques, we propose a joint equalization and decoding scheme that achieves reliable communication of source S/sub 1/ at signal-to-noise ratios close to the theoretical bounds set by the combination of the Slepian-Wolf and Shannon theorems.