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Joint source-channel turbo decoding of entropy coded sources

A new turbo joint source-channel decoding algorithm is presented. The proposed scheme, derived from a Bayesian network representation of the coding chain, incorporates three types of information: the source memory; the residual redundancy of the source coder; and finally the redundancy introduced by the channel coder. Specifically, we modify an existing algorithm by introducing an equivalent graph, that is shown to hold the same state-space while exhibiting far less undirected cycles. A fully consistent solution for joint turbo decoding within the Bayesian networks framework follows. The proposed algorithm is demonstrated to yield considerably better results along with a drastic reduction in computational complexity when compared to the existing one.