Machine-type communications and large-scale information processingarchitectures are among key (r)evolutionary enhancements of emergingfifth-generation (5G) mobile cellular networks. Massive data acquisition andprocessing will make 5G network an ideal platform for large-scale systemmonitoring and control with applications in future smart transportation,connected industry, power grids, etc. In this work, we investigate a capabilityof such a 5G network architecture to provide the state estimate of anunderlying linear system from the input obtained via large-scale deployment ofmeasurement devices. Assuming that the measurements are communicated viadensely deployed cloud radio access network (C-RAN), we formulate and solve theproblem of estimating the system state from the set of signals collected atC-RAN base stations. Our solution, based on the Gaussian Belief-Propagation(GBP) framework, allows for large-scale and distributed deployment within theemerging 5G information processing architectures. The presented numerical studydemonstrates the accuracy, convergence behavior and scalability of the proposedGBP-based solution to the large-scale state estimation problem.
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