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Scalable source/channel decoding for large-scale sensor networks

We consider the sensor reachback problem, in which a large number of sensor nodes are deployed on a field, and the goal is to reconstruct at a remote location the correlated data collected and transmitted by all the nodes. In this paper, we assume that each sensor node uses a very simple encoder (a scalar quantizer and a modulator) and focus on decoding algorithms that exploit the correlation structure of the sensor data to produce the best possible estimates under the minimum mean square error (MMSE) criterion. Our analysis shows that the optimal MMSE decoder is unfeasible for large scale sensor networks, because its complexity grows exponentially with the number of nodes in the network. Seeking a scalable alternative, we use factor graphs to obtain a simplified model for the correlation structure of the sensor data. This model allows us to use an iterative decoding algorithm whose complexity can be made to grow linearly with the size of the network.