AI-Driven 5G Network Slicing: Revolutionizing Enterprise Connectivity
Year: 2025
Type: article
Abstract: This article explores the transformative impact of AI-enhanced 5G network slicing on enterprise connectivity across various industries. Network slicing represents a paradigm shift from traditional networking approaches, enabling the creation of multiple virtualized networks on shared physical infrastructure, each optimized for specific applications. While network slicing offers significant advantages over conventional models, its true potential emerges through artificial intelligence integration. The article examines how AI transforms network slicing from static configuration into dynamic, self-optimizing systems through capabilities including dynamic resource allocation, predictive analytics, enhanced security, and quality of service optimization. Industry-specific implementations across manufacturing, healthcare, transportation, and enterprise workplaces demonstrate the practical benefits of this technology. The article also highlights intelligent device management aspects including adaptive allocation, performance monitoring, security, and seamless transitions. Despite its potential, AI-driven network slicing faces challenges related to model complexity, integration with legacy systems, and regulatory compliance. Looking ahead, the article envisions increasing autonomy through self-healing networks, intent-based networking, and potential quantum computing enhancements for network optimization.
Author: Arun Sugumar
Institution: Anna University, Chennai
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408986975