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.
|Number of pages||6|
|Publication status||Published - 15 Apr 2018|
|Event||IEEE Wireless Communications and Networking Conference - Barcelona, Spain|
Duration: 15 Apr 2018 → 18 Apr 2018
|Conference||IEEE Wireless Communications and Networking Conference|
|Period||15/04/18 → 18/04/18|
- 5G mobile cellular networks
- large-scale information processing
- Gaussian Belief-Propagation
- cloud radio access network
Cosovic, M., Vukobratovic, D., & Stankovic, V. (2018). Linear state estimation via 5G C-RAN cellular networks using Gaussian belief propagation. Paper presented at IEEE Wireless Communications and Networking Conference, Barcelona, Spain.