Joint source decoding in large scale sensor networks using Markov random field models

Scalable joint decoding of correlated observations transmitted using distributed quantization in a sensor-network is considered. In particular, quantized observations are modeled as a Markov-random field (MRF), from which we construct a factor-graph for implementing the decoder using the well known...

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Bibliographic Details
Main Author: Yahampath, P.
Format: Conference Proceeding
Language:English
Subjects:
Computational complexity
Decoding
Distributed quantization
factor-graphs
Intelligent networks
Large-scale systems
Markov random fields
Performance gain
Quantization
Source coding
Sum product algorithm
Wireless sensor networks
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Description
Summary:Scalable joint decoding of correlated observations transmitted using distributed quantization in a sensor-network is considered. In particular, quantized observations are modeled as a Markov-random field (MRF), from which we construct a factor-graph for implementing the decoder using the well known sum-product algorithm. An attractive property of this approach is that the decoder complexity can be controlled by the choice of the clique structure used to define the Gibbs distribution of the MRF model. The experimental results obtained with a widely used correlated Gaussian observation model is presented, which demonstrate that substantial performance gains can be achieved by joint decoding based on simple clique structures and potential functions.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2009.4960197