Radio link failure (RLF) prediction system in Radio Access Networks (RANs) is critical for ensuring seamless communication and meeting the stringent requirements of high data rates, low latency, and improved reliability in 5G networks. However, weather conditions such as precipitation, humidity, temperature, and wind impact these communication links. Usually, historical radio link Key Performance Indicators (KPIs) and their surrounding weather station observations are utilized for building learning-based RLF prediction models. However, such models must be capable of learning the spatial weather context in a dynamic RAN and effectively encoding time series KPIs with the weather observation data. Existing works fail to incorporate both of these essential design aspects of the prediction models. This paper fills the gap by proposing GenTrap, a novel RLF prediction framework that introduces a graph neural network (GNN)-based learnable weather effect aggregation module and employs state-of-the-art time series transformer as the temporal feature extractor for radio link failure prediction. The proposed aggregation method of GenTrap can be integrated into any existing prediction model to achieve better performance and generalizability. We evaluate GenTrap on two real-world datasets (rural and urban) with 2.6 million KPI data points and show that GenTrap offers a significantly higher F1-score (0.93 for rural and 0.79 for urban) compared to its counterparts while possessing generalization capability.

翻译：无线接入网络中的无线链路故障预测系统对于确保无缝通信并满足5G网络高数据速率、低时延和高可靠性的严苛要求至关重要。然而，降水、湿度、温度和风速等气象条件会影响这些通信链路。通常，基于学习的RLF预测模型会利用历史无线链路关键性能指标及其周边气象站观测数据。但此类模型必须能够学习动态RAN中的空间气象上下文，并有效编码时间序列KPI与气象观测数据。现有研究未能兼顾预测模型这两个关键设计维度。本文通过提出GenTrap框架填补了这一空白，该新型RLF预测框架引入了基于图神经网络的动态气象效应聚合模块，并采用最先进的时间序列Transformer作为无线链路故障预测的时序特征提取器。GenTrap提出的聚合方法可集成至任意现有预测模型，以获得更优性能与泛化能力。我们在包含260万KPI数据点的两个真实数据集（农村与城市场景）上评估GenTrap，结果表明相较于现有方法，GenTrap在保持泛化能力的同时实现了显著更高的F1分数（农村场景0.93，城市场景0.79）。