🤖 AI Summary
This study addresses the challenge of base station energy consumption during low-traffic periods in 5G networks by proposing a machine learning–based energy-saving strategy that explicitly incorporates operator-defined quality-of-service (QoS) constraints. Leveraging real-world 5G data within a supervised learning framework, the method innovatively embeds QoS policies—such as throughput guarantees and maximum allowable service interruption—into the model optimization process through a policy-guided class reweighting mechanism during training. This enables a controllable trade-off between energy efficiency and service compliance. Experimental results demonstrate that the proposed approach significantly reduces base station energy consumption in operational networks while strictly adhering to the stipulated QoS requirements.
📝 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.