Adaptive Slepian-Wolf decoding using particle filtering based belief propagation

Authors:
Samuel Cheng;Shuang Wang;Lijuan Cui
Affiliations:
School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK;School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK;School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK
Venue:
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Year:
2009

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Abstract

A major difficulty that plagues the practical use of Slepian-Wolf coding (and distributed source coding in general) is that the precise correlation among sources need to be known a priori. To resolve this problem, we propose an adaptive Slepian-Wolf decoder using particle filtering based belief propagation. We show through experiments that the proposed algorithm can simultaneously reconstruct a compressed source and estimate the joint correlation between the sources. Further, comparing to the conventional Slepian-Wolf coder based on standard belief propagation, the proposed approach can achieve higher compression under varying correlation statistics.