Distributed Source Coding Using Syndromes (DISCUS): Design and Construction
DCC '99 Proceedings of the Conference on Data Compression
DCC '03 Proceedings of the Conference on Data Compression
Compression with Side Information Using Turbo Codes
DCC '02 Proceedings of the Data Compression Conference
Joint Source-Channel Decoding of Correlated Sources over Noisy Channels
DCC '01 Proceedings of the Data Compression Conference
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Joint turbo decoding and estimation of hidden Markov sources
IEEE Journal on Selected Areas in Communications
Joint Source-Channel Decoding ASIP Architecture for Sensor Networks
ICESS '07 Proceedings of the 3rd international conference on Embedded Software and Systems
ADHOC-NOW '09 Proceedings of the 8th International Conference on Ad-Hoc, Mobile and Wireless Networks
Distributed source coding using raptor codes for hidden Markov sources
IEEE Transactions on Signal Processing
Performance bounds and codes design criteria for channel decoding with a-priori information
IEEE Transactions on Wireless Communications
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We consider the case of two correlated sources, S1 and S2. The correlation between them has memory, and it is modelled by a hidden Markov chain. The paper studies the problem of reliable communication of the information sent by the source S1 over an additive white Gaussian noise (AWGN) channel when the output of the other source S2 is available as side information at the receiver. We assume that the receiver has no a priori knowledge of the correlation statistics between the sources. In particular, we propose the use of a turbo code for joint source-channel coding of the source S1. The joint decoder uses an iterative scheme where the unknown parameters of the correlation model are estimated jointly within the decoding process. It is shown that reliable communication is possible at signal-to-noise ratios close to the theoretical limits set by the combination of Shannon and Slepian-Wolf theorems.