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 filed (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.