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

Mirsad Cosovic, Dejan Vukobratovic, Vladimir Stankovic

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)
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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 languageEnglish
Number of pages6
Publication statusPublished - 15 Apr 2018
EventIEEE Wireless Communications and Networking Conference - Barcelona, Spain
Duration: 15 Apr 201818 Apr 2018


ConferenceIEEE Wireless Communications and Networking Conference
Abbreviated titleWCNC
Internet address


  • 5G mobile cellular networks
  • telecommunications
  • large-scale information processing
  • Gaussian Belief-Propagation
  • GBP
  • cloud radio access network
  • C-RAN

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