Linear state estimation via 5G C-RAN cellular networks using Gaussian belief propagation

Abstract

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.

Effect of Communication Failures on State Estimation of 5G-Enabled Smart Grid

“…The study showed state estimation accuracy can be significantly affected by a varying cell coverage range and number of contending devices in the system. Cosovic et al in [13], [14] propose to leverage 5G cellular technologies to enable a distributed state estimation for smart grid. Latency and reliability of distributed state estimation on 5G communication networks are analyzed.…”