DCC '03 Proceedings of the Conference on Data Compression
Compression with Side Information Using Turbo Codes
DCC '02 Proceedings of the Data Compression Conference
Design of Slepian-Wolf Codes by Channel Code Partitioning
DCC '04 Proceedings of the Conference on Data Compression
On Some New Approaches to Practical Slepian-Wolf Compression Inspired by Channel Coding
DCC '04 Proceedings of the Conference on Data Compression
DCC '04 Proceedings of the Conference on Data Compression
An efficient SF-ISF approach for the Slepian-wolf source coding problem
EURASIP Journal on Applied Signal Processing
Nested linear/lattice codes for structured multiterminal binning
IEEE Transactions on Information Theory
Distributed source coding using syndromes (DISCUS): design and construction
IEEE Transactions on Information Theory
Distributed arithmetic coding for the Slepian-Wolf problem
IEEE Transactions on Signal Processing
Distributed source coding with cyclic codes and their duals
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
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This paper considers symmetric Slepian-Wolf (SW) coding of two memoryless binary symmetric sources. We propose a simple and general framework, termed the Symmetric SF-ISF Framework (SSIF), (i) which can be efficiently applied to any linear channel code, (ii) which incurs no rate loss when converting the channel code to the Slepian-Wolf code, and (iii) which can achieve an arbitrary point in the Slepian-Wolf rate region. The proposed SW encoder implements the binning approach through a syndrome former (SF). The proposed SW decoder performs optimal estimation by first recovering the difference pattern between the sources using a matching inverse syndrome former (ISF), and subsequently recovering individual source sequences through syndrome former partitioning. Through rigorous proof and discussion, we show that the proposed framework is capable of achieving any rate pair promised by the theory. Hamming codes, turbo product codes, turbo codes and LDPC codes are provided as examples to demonstrate the generality and efficiency of the framework.