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arXiv · 2606.05755

Policy-Guided ML for Energy Savings: Cell On/Off Switching under Operator QoS Constraints in Real 5G Networks

D. Reiss·M. Catalan-Cid·D. Camps-Mur·O. Sallent
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Abstract

Energy efficiency is a critical concern in the deployment and operation of 5G networks, particularly due to the low utilization of 4G and 5G carriers during off-peak hours. While considerable research has focused on designing energy-efficient cell on/off switching strategies that avoid disrupting user connectivity, the integration of operator-specific policies to guarantee particular Quality of Service (QoS) levels has received limited attention. This paper presents a machine learning (ML)-based energy saving strategy, trained using a real-world dataset from a European mobile operator, that enforces operator-defined policies that jointly consider strong throughput requirements and maximum outage tolerance constraints. By tuning the model's class ratios during training, the proposed solution enables operators to manage the trade-off between energy savings and QoS policy compliance prior to deployment in live networks. Evaluation results show that the method provides substantial energy savings while maintaining policy-compliant service levels under realistic 5G operating conditions.

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D. Reiss, M. Catalan-Cid, D. Camps-Mur, O. Sallent. 2026-06-04. Policy-Guided ML for Energy Savings: Cell On/Off Switching under Operator QoS Constraints in Real 5G Networks. https://doi.org/10.1109/mswim67937.2025.11308878

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