This paper focuses on predicting downlink (DL) traffic volume in mobile networks while minimizing overprovisioning and meeting a given service-level agreement (SLA) violation rate. We present a multivariate, multi-step, and SLA-driven approach that incorporates 20 different radio access network (RAN) features, a custom feature set based on peak traffic hours, and handover-based clustering to leverage the spatiotemporal effects. In addition, we propose a custom loss function that ensures the SLA violation rate constraint is satisfied while minimizing overprovisioning. We also perform multi-step prediction up to 24 steps ahead and evaluate performance under both single-step and multi-step prediction conditions. Our study makes several contributions, including the analysis of RAN features, the custom feature set design, a custom loss function, and a parametric method to satisfy SLA constraints.

翻译：本文聚焦于移动网络下行链路流量预测，旨在最小化过度配置并满足给定的服务等级协议（SLA）违规率。我们提出了一种多变量、多步且SLA驱动的方法，该方法整合了20种不同的无线接入网（RAN）特征、基于高峰流量时段的定制特征集以及基于切换的聚类技术以利用时空效应。此外，我们设计了一种定制损失函数，确保在满足SLA违规率约束的同时最小化过度配置。我们还实现了长达24步的多步预测，并在单步与多步预测条件下评估性能。本研究的贡献包括：RAN特征分析、定制特征集设计、定制损失函数以及满足SLA约束的参数化方法。