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