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.
Original language | English |
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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 http://wcnc2018.ieee-wcnc.org/ |
Conference
Conference | IEEE Wireless Communications and Networking Conference |
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Abbreviated title | WCNC |
Country/Territory | Spain |
City | Barcelona |
Period | 15/04/18 → 18/04/18 |
Internet address |
Keywords
- 5G mobile cellular networks
- telecommunications
- large-scale information processing
- Gaussian Belief-Propagation
- GBP
- cloud radio access network
- C-RAN