Distributed joint source-channel decoding for correlated Markov sources

Summary form only given. Most current distributed joint source-channel coding (DJSCC) work focus on sources without memory, like i.i.d sources. We consider a general scenario where the sources are not only correlated with each other but also have memory. In particular, we design a constrained distributed joint source-channel decoder (C-DJSCD) for the optimal variable length multiple access source code (MASC) encoded sources for the symmetric distributed coding scenario. The proposed decoder is based on maximum a posteriori probability (MAP) estimation.