Boosting 5G on Smart Grid Communication: A Smart RAN Slicing Approach

TL;DR

This paper proposes an AI-driven RAN slicing framework for 5G-enabled smart grids, enhancing resource management and service differentiation to meet smart grid requirements.

Contribution

It introduces a novel AI-based RAN slicing approach using deep reinforcement learning for smart grid services in 5G networks.

Findings

Effective radio resource management with deep reinforcement learning

Supports IEC 61850 smart grid services

Conforms to smart grid performance requirements

Abstract

Fifth-generation (5G) and beyond systems are expected to accelerate the ongoing transformation of power systems towards the smart grid. However, the inherent heterogeneity in smart grid services and requirements pose significant challenges towards 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 (AI) 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 AI-native approaches for RAN slicing in beyond-5G systems, and lay the foundations for differentiated service provisioning in the smart grid.