We show how low-density parity-check (LDPC) codes can be used to compress close to the Slepian-Wolf limit for correlated binary sources. Focusing on the asymmetric case of compression of an equiprobable memoryless binary source with side information at the decoder, the approach is based on viewing the correlation as a channel and applying the syndrome concept. The encoding and decoding procedures are explained in detail. The performance achieved is seen to be better than recently published results using turbo codes and very close to the Slepian-Wolf limit.

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A Slepian-Wolf coding scheme for compressing two uniform memoryless binary sources using a single channel code that can achieve arbitrary rate allocation among encoders was outlined in the work of Pradhan and Ramchandran. Inspired by this work, we address the problem of practical code design for general multiterminal lossless networks where multiple memoryless correlated binary sources are separately compressed and sent; each decoder receives a set of compressed sources and attempts to jointly reconstruct them. First, we propose a near-lossless practical code design for the Slepian-Wolf system with multiple sources. For two uniform sources, if the code approaches the capacity of the channel that models the correlation between the sources, then the system will approach the theoretical limit. Thus, the great advantage of this design method is its possibility to approach the theoretical limits with a single channel code for any rate allocation among the encoders. Based on Slepian-Wolf code constructions, we continue with providing practical designs for the general lossless multiterminal network which consists of an arbitrary number of encoders and decoders. Using irregular repeat-accumulate and turbo codes in our designs, we obtain the best results reported so far and almost reach the theoretical bounds

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Abstrucf-We use systematic irregular repeat accumulate (IRA) codes as source-channel cod6 for the transmission of an equiprobable memoryless binary source with side information at the decoder. A special case of this problem is Joint source-channel coding for a nonequiprobable memoryless binary source. The theoretical limits of this problem are given by combining the Slepian-Wolf theorem, the source entropy in the special case, with the channel capacity. The approach is based on viewing the correlation between the binary source output and the side information as a separate channel or an enhancement of the original channel. The joint source-channel encoding, decoding and code design procedures are explained in detail. The simulated performance results are better than the recently published solutions using turbo codes and very close to the theoretical limit.