Autonomous Private 5G Networks For Industry 4.0: AI-Native Operations And Closed-Loop Automation

Abstract

The convergence of fifth-generation wireless technology with Industry 4.0 applications necessitates autonomous network operations capable of supporting ultra-reliable, low-latency communications with minimal human intervention. This research investigates the implementation of AI-native operations and closed-loop automation in private 5G networks, focusing on intent-based networking paradigms that enable self-configuring, self-optimizing, and self-healing network infrastructures. The study examines machine learning algorithms for radio resource management, predictive analytics for performance optimization, and automated policy enforcement mechanisms. Through comprehensive analysis of network slicing architectures, edge computing integration, and time-sensitive networking protocols, this paper demonstrates how autonomous private 5G networks can achieve latencies below 1 millisecond while maintaining 99.999% availability. The research presents a framework for closed-loop automation that reduces operational expenditure by 35% while improving network efficiency by 42% compared to traditional management approaches. Key findings indicate that AI-driven intent translation mechanisms can process natural language network policies with 94% accuracy, enabling rapid deployment of industrial applications requiring massive machine-type communications and enhanced mobile broadband services.