Boosting 5G on Smart Grid Communication: A Smart RAN Slicing Approach
Carrillo, Dick; Kalalas, Charalampos; Raussi, Petra; Michalopoulos, Diomidis S.; Rodriguez, Demostenes Z.; Kokkoniemi-Tarkkanen, Heli; Ahola, Kimmo; Nardelli, Pedro H.J.; Fraidenraich, Gustavo; Popovski, Petar (2022-08-22)
IEEE
22.08.2022
D. Carrillo et al., "Boosting 5G on Smart Grid Communication: A Smart RAN Slicing Approach," in IEEE Wireless Communications, vol. 30, no. 5, pp. 170-178, October 2023, doi: 10.1109/MWC.004.2200079.
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202312083570
https://urn.fi/URN:NBN:fi:oulu-202312083570
Tiivistelmä
Abstract
Fifth-generation (5G) and beyond systems are expected to accelerate the ongoing transformation of power systems toward the smart grid. However, the inherent heterogeneity in smart grid services and requirements pose significant challenges toward the definition of a unified network architecture. In this context, radio access network (RAN) slicing emerges as a key 5G enabler to ensure interoperable connectivity and service management in the smart grid. This article introduces a novel RAN slicing framework which leverages the potential of artificial intelligence (Al) to support IEC 61850 smart grid services. With the aid of deep reinforcement learning, efficient radio resource management for RAN slices is attained, while conforming to the stringent performance requirements of a smart grid self-healing use case. Our research outcomes advocate the adoption of emerging Al-native approaches for RAN slicing in beyond-5G systems, and lay the foundations for differentiated service provisioning in the smart grid.
Fifth-generation (5G) and beyond systems are expected to accelerate the ongoing transformation of power systems toward the smart grid. However, the inherent heterogeneity in smart grid services and requirements pose significant challenges toward the definition of a unified network architecture. In this context, radio access network (RAN) slicing emerges as a key 5G enabler to ensure interoperable connectivity and service management in the smart grid. This article introduces a novel RAN slicing framework which leverages the potential of artificial intelligence (Al) to support IEC 61850 smart grid services. With the aid of deep reinforcement learning, efficient radio resource management for RAN slices is attained, while conforming to the stringent performance requirements of a smart grid self-healing use case. Our research outcomes advocate the adoption of emerging Al-native approaches for RAN slicing in beyond-5G systems, and lay the foundations for differentiated service provisioning in the smart grid.
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