[2207.11117] Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks
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Computer Science > Machine Learning

arXiv:2207.11117 (cs)
[Submitted on 22 Jul 2022]

Title:Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks

Authors:Ognjen Kundacina, Miodrag Forcan, Mirsad Cosovic, Darijo Raca, Merim Dzaferagic, Dragisa Miskovic, Mirjana Maksimovic, Dejan Vukobratovic
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Abstract:Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays.
Subjects: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
Cite as: arXiv:2207.11117 [cs.LG]
  (or arXiv:2207.11117v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2207.11117

Submission history

From: Ognjen Kundacina [view email]
[v1] Fri, 22 Jul 2022 14:48:10 UTC (3,443 KB)
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