AI Assisted Resource Management for Private 5G Networks

Abstract

Private fifth generation mobile networks offer industrial sites direct control over coverage, quality of service, security boundaries, and integration with operational applications. Their flexibility also creates a difficult resource management problem because radio spectrum, transport, computing, and network functions must serve traffic with different latency, reliability, throughput, and device density requirements. This technical review examines how artificial intelligence can assist resource management without weakening service guarantees. It relates 5G system architecture, network slicing, multi access edge computing, and open radio access network control to supervised learning, anomaly detection, contextual bandits, and deep reinforcement learning. A practical decision framework separates planning, prediction, optimization, admission control, and closed loop adaptation. It recommends that learning models estimate demand and radio conditions, while policy engines and constrained optimizers retain explicit responsibility for service limits. For higher autonomy, learned actions should pass through feasibility and safety checks, operate within bounded scopes, and fall back to deterministic scheduling. Unlike prior surveys that catalogue machine-learning techniques across wireless networking broadly, this review is scoped specifically to the industrial private-5G setting and to the governance boundary between learned recommendations and the deterministic policy engine that retains authority over service limits. The article identifies data requirements, digital twin and testbed needs, evaluation metrics, security controls, and a staged pathway suitable for manufacturing campuses. The main conclusion is that artificial intelligence should be evaluated against strong engineering baselines and judged by tail latency, reliability violations, stability, and operational burden rather than average throughput alone. This approach enables private 5G systems to gain adaptive efficiency while preserving auditable control.