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https://open.uns.ac.rs/handle/123456789/1545
Nаziv: | Linear state estimation via 5G C-RAN cellular networks using Gaussian belief propagation | Аutоri: | Cosovic M. Vukobratović, Dejan Stankovic V. |
Dаtum izdаvаnjа: | 8-јун-2018 | Čаsоpis: | IEEE Wireless Communications and Networking Conference, WCNC | Sažetak: | © 2018 IEEE. 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 infrastructures. 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. | URI: | https://open.uns.ac.rs/handle/123456789/1545 | ISBN: | 9781538617342 | ISSN: | 15253511 | DOI: | 10.1109/WCNC.2018.8377399 |
Nаlаzi sе u kоlеkciјаmа: | FTN Publikacije/Publications |
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