Dynamic Traffic Forecasting for 5G Networks Using PPO-Enhanced Reinforcement Learning
Keywords:
5G networks, traffic forecasting, deep reinforcement learning, proximal policy optimization, network slicing, quality of service, system architectureAbstract
The dynamic and heterogeneous nature of traffic in fifth-generation (5G) networks imposes unprecedented demands on resource allocation, latency management, and quality-of-service (QoS) assurance. Traditional forecasting methods, including time-series models and static machine learning approaches, struggle to adapt to the rapid fluctuations and multi-tenancy environments characteristic of 5G infrastructures. This paper proposes a dynamic traffic forecasting framework that leverages the Proximal Policy Optimization (PPO) algorithm, a state-of-the-art deep reinforcement learning technique, to optimize prediction accuracy while maintaining computational efficiency and system robustness. We examine the architectural trade-offs inherent in embedding reinforcement learning agents within network slicing and edge computing layers, emphasizing system-level implications for governance, fairness, and sustainability. The PPO-enhanced approach offers a stable, sample-efficient learning mechanism that balances exploration and exploitation, addressing the convergence and stability issues observed in prior value-based and policy-gradient methods. Through a conceptual analysis of deployment scenarios, including urban macro-cells, industrial IoT clusters, and autonomous vehicle corridors, we illustrate how the PPO framework can integrate with software-defined networking and network function virtualization to enable real-time adaptive forecasting. We further discuss policy and regulatory considerations, such as data privacy, model interpretability, and cross-domain accountability, that arise when deploying reinforcement learning in critical communication infrastructures. The findings indicate that PPO-based traffic forecasting can significantly reduce prediction error variance and improve slice-level QoS assurance, although careful attention must be paid to training overhead, reward sparsity, and the risk of feedback loops. By situating the technical mechanism within broader socio-technical systems, this paper contributes to the discourse on intelligent network governance and the sustainable evolution of 5G and beyond.
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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.
