Code design for lossless multiterminal networks

This paper considers a general multiterminal (MT) system, which consists of L encoders and P decoders. Let X/sub 1/,..., X/sub L/ be memoryless, uniform, correlated random binary vectors of length n, and let x/sub 1/,..., x/sub L/ denote their realizations. Let further /spl Sigma/ = {1,...,L}. The i...

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Hauptverfasser: Stankovic, V., Liveris, A.D., Zixiang Xiong, Georghiades, C.N.
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Zixiang Xiong
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description This paper considers a general multiterminal (MT) system, which consists of L encoders and P decoders. Let X/sub 1/,..., X/sub L/ be memoryless, uniform, correlated random binary vectors of length n, and let x/sub 1/,..., x/sub L/ denote their realizations. Let further /spl Sigma/ = {1,...,L}. The i-th encoder compresses X/sub i/ independently from other encoders. The j-th decoder receives the bitstreams from a set of encoders /spl Sigma//sub j//spl sube/ /spl Sigma/ and jointly decodes them. It should reconstruct the received source messages with arbitrarily small probability of error. To construct a practical coding scheme for this network, we exploit the fact that such a network can be split into P subnetworks, each being regarded as a Slepian-Wolf (SW) coding system with multiple sources. This SW subnetwork consists of a decoder which receives encodings of all X/sub k/'s such that k/spl isin//spl Sigma//sub sw//spl sube//spl Sigma/ and attempts to reconstruct them perfectly. Based on (V. Stankovic et al. 2004), we first provide a code design for this setting, and then extend it to the general case.
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Let X/sub 1/,..., X/sub L/ be memoryless, uniform, correlated random binary vectors of length n, and let x/sub 1/,..., x/sub L/ denote their realizations. Let further /spl Sigma/ = {1,...,L}. The i-th encoder compresses X/sub i/ independently from other encoders. The j-th decoder receives the bitstreams from a set of encoders /spl Sigma//sub j//spl sube/ /spl Sigma/ and jointly decodes them. It should reconstruct the received source messages with arbitrarily small probability of error. To construct a practical coding scheme for this network, we exploit the fact that such a network can be split into P subnetworks, each being regarded as a Slepian-Wolf (SW) coding system with multiple sources. This SW subnetwork consists of a decoder which receives encodings of all X/sub k/'s such that k/spl isin//spl Sigma//sub sw//spl sube//spl Sigma/ and attempts to reconstruct them perfectly. Based on (V. 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subjects Applied sciences
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Decoding
Exact sciences and technology
Hamming distance
Information theory
Information, signal and communications theory
Linearity
Software
Telecommunications and information theory
title Code design for lossless multiterminal networks
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