AI-Enabled Predictive Maintenance in 5G Network Infrastructure Using PPO
Keywords:
predictive maintenance, 5G network infrastructure, deep reinforcement learning, Proximal Policy Optimization, network slicing, system architecture, governance, fairness, sustainabilityAbstract
The evolution of fifth-generation (5G) mobile networks has introduced unprecedented complexity in network infrastructure, characterized by dense heterogeneous deployments, network slicing, and stringent quality-of-service requirements. Ensuring high reliability and minimal downtime through predictive maintenance has become a critical operational challenge. This paper proposes a framework for AI-enabled predictive maintenance in 5G networks that leverages the Proximal Policy Optimization (PPO) algorithm, a state-of-the-art deep reinforcement learning method. We provide a system-level analysis of how PPO can be integrated into 5G network operations to anticipate and mitigate equipment failures, optimize resource allocation, and reduce operational expenditure. The discussion emphasizes structural trade-offs between predictive accuracy, computational overhead, and real-time decision-making constraints. We examine architectural considerations for embedding reinforcement learning agents within network management and orchestration layers, including data pipeline design, reward shaping, and policy deployment across distributed edge and core nodes. Furthermore, we explore cross-domain implications related to governance, fairness, and policy, particularly concerning data privacy, model interpretability, and the socio-technical impact of autonomous maintenance decisions on service-level agreements and network access equity. Through conceptual analysis and illustrative use cases, we argue that PPO-based predictive maintenance offers a robust path toward self-healing networks but requires careful calibration of reward structures to avoid biased outcomes. We also discuss sustainability challenges related to the energy footprint of training and inference, as well as the need for standardized benchmarks and regulatory oversight. The paper concludes with forward-looking perspectives on the convergence of AI and telecommunications infrastructure, highlighting open research questions in transfer learning, multi-agent coordination, and human-in-the-loop oversight.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
