This study presents a spatiotemporal traffic prediction approach for NextG mobile networks, ensuring the service-level agreements (SLAs) of each network slice. Our approach is multivariate, multi-step, and spatiotemporal. Leveraging 20 radio access network (RAN) features, peak traffic hour data, and mobility-based clustering, we propose a parametric SLA-based loss function to guarantee an SLA violation rate. We focus on single-cell, multi-cell, and slice-based prediction approaches and present a detailed comparative analysis of their performances, strengths, and limitations. First, we address the application of single-cell and multi-cell training architectures. While single-cell training offers individual cell-level prediction, multi-cell training involves training a model using traffic from multiple cells from the same or different base stations. We show that the single-cell approach outperforms the multi-cell approach and results in test loss improvements of 11.4% and 38.1% compared to baseline SLA-based and MAE-based models, respectively. Next, we explore slice-based traffic prediction. We present single-slice and multi-slice methods for slice-based downlink traffic volume prediction, arguing that multi-slice prediction offers a more accurate forecast. The slice-based model we introduce offers substantial test loss improvements of 28.2%, 36.4%, and 55.6% compared to our cell-based model, the baseline SLA-based model, and the baseline MAE-based model, respectively.

翻译：本研究提出了一种面向NextG移动网络的时空流量预测方法，能够保障每个网络切片的服务等级协议（SLA）。该方法具有多元、多步和时空特性。利用20个无线接入网（RAN）特征、高峰流量时段数据以及基于移动性的聚类分析，我们提出了一种参数化的基于SLA的损失函数，以确保SLA违例率。本研究聚焦于单小区、多小区和基于切片的预测方法，并对其性能、优势及局限性进行了详细的比较分析。首先，我们解决了单小区和多小区训练架构的应用问题。单小区训练提供单个小区级别的预测，而多小区训练则利用来自同一或不同基站的多个小区的流量数据训练模型。研究表明，单小区方法优于多小区方法，相较于基线基于SLA的模型和基于MAE的模型，其测试损失分别降低了11.4%和38.1%。随后，我们探索了基于切片的流量预测。针对基于切片的下行链路流量量预测，我们提出了单切片和多切片方法，并论证了多切片预测能够提供更精确的预测结果。我们引入的基于切片的模型相较于基于小区的模型、基线基于SLA的模型和基线基于MAE的模型，其测试损失分别显著降低了28.2%、36.4%和55.6%。