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This article proposes a novel iterative algorithm based on Low Density Parity Check (LDPC) codes for compression of correlated sources at rates approaching the Slepian-Wolf bound. The setup considered in the article looks at the problem of compressing one source at a rate determined based on the knowledge of the mean source correlation at the encoder, and employing the other correlated source as side information at the decoder which decompresses the first source based on the estimates of the actual correlation. We demonstrate that depending on the extent of the actual source correlation estimated through an iterative paradigm, significant compression can be obtained relative to the case the decoder does not use the implicit knowledge of the existence of correlation.
We present sufficient conditions for multicasting a set of correlated sources over cooperative networks. We propose joint source-Wyner-Ziv encoding/sliding-window decoding scheme, in which each receiver considers an ordered partition of other nodes.
This paper deals with the problem of multicasting a set of discrete memoryless correlated sources (DMCS) over a cooperative relay network. Necessary conditions with cut-set interpretation are presented. A emph{Joint source-Wyner-Ziv encoding/sliding
The Slepian-Wolf bound on the admissible coding rate forms the most fundamental aspect of distributed source coding. As such, it is necessary to provide a framework with which to model more practical scenarios with respect to the arrangement of nodes
In this paper, we propose a polar coding based scheme for set reconciliation between two network nodes. The system is modeled as a well-known Slepian-Wolf setting induced by a fixed number of deletions. The set reconciliation process is divided into
This paper is focused on the derivation of some universal properties of capacity-approaching low-density parity-check (LDPC) code ensembles whose transmission takes place over memoryless binary-input output-symmetric (MBIOS) channels. Properties of t