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.

翻译：能源效率是5G网络部署与运行中的关键问题，尤其在非高峰时段4G与5G载波利用率较低的背景下尤为突出。尽管已有大量研究聚焦于设计避免中断用户连接的节能型小区开关切换策略，但融合运营商特定策略以保障特定服务质量（QoS）水平的研究仍显不足。本文提出一种基于机器学习的节能策略，利用欧洲移动运营商的实际数据集进行训练，该策略强制执行运营商定义的策略，同时兼顾高吞吐量需求与最大中断容忍约束。通过在训练过程中调整模型类别比例，所提方案使运营商能够在实际网络部署前管理节能与QoS策略合规性之间的权衡。评估结果表明，该方法在真实5G运行条件下，既能实现显著节能效果，又能维持策略合规的服务水平。