Linear State Estimation via 5G C-RAN Cellular Networks using Gaussian Belief Propagation

TL;DR

This paper proposes a scalable, distributed method for linear state estimation in 5G C-RAN networks using Gaussian Belief Propagation, enabling large-scale system monitoring and control.

Contribution

It introduces a novel GBP-based framework for large-scale, distributed state estimation in 5G C-RAN architectures, addressing scalability and accuracy.

Findings

Demonstrates high accuracy of the GBP-based estimation method.

Shows convergence and scalability in large-scale simulations.

Validates effectiveness for real-time system monitoring.

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.