The emergence of 5G technology marks a significant milestone in developing telecommunication networks, enabling exciting new applications such as augmented reality and self-driving vehicles. However, these improvements bring an increased management complexity and a special concern in dealing with failures, as the applications 5G intends to support heavily rely on high network performance and low latency. Thus, automatic self-healing solutions have become effective in dealing with this requirement, allowing a learning-based system to automatically detect anomalies and perform Root Cause Analysis (RCA). However, there are inherent challenges to the implementation of such intelligent systems. First, there is a lack of suitable data for anomaly detection and RCA, as labelled data for failure scenarios is uncommon. Secondly, current intelligent solutions are tailored to LTE networks and do not fully capture the spatio-temporal characteristics present in the data. Considering this, we utilize a calibrated simulator, Simu5G, and generate open-source data for normal and failure scenarios. Using this data, we propose Simba, a state-of-the-art approach for anomaly detection and root cause analysis in 5G Radio Access Networks (RANs). We leverage Graph Neural Networks to capture spatial relationships while a Transformer model is used to learn the temporal dependencies of the data. We implement a prototype of Simba and evaluate it over multiple failures. The outcomes are compared against existing solutions to confirm the superiority of Simba.

翻译：5G技术的出现标志着电信网络发展的一个重要里程碑，为实现增强现实和自动驾驶汽车等激动人心的新应用提供了可能。然而，这些改进也带来了更高的管理复杂度，并在处理故障方面提出了特殊要求，因为5G拟支持的应用高度依赖于网络的高性能与低延迟。因此，自动自愈解决方案已成为满足这一需求的有效手段，使得基于学习的系统能够自动检测异常并执行根因分析（RCA）。然而，实现此类智能系统存在固有挑战。首先，由于故障场景的标注数据较为罕见，缺乏适用于异常检测与RCA的合适数据。其次，现有智能解决方案主要针对LTE网络设计，未能充分捕捉数据中存在的时空特征。针对这些问题，我们利用校准仿真器Simu5G，生成了正常与故障场景的开源数据集。基于此数据，我们提出了Simba——一种用于5G无线接入网络（RAN）异常检测与根因分析的先进方法。我们利用图神经网络捕捉空间关系，同时采用Transformer模型学习数据的时间依赖性。我们实现了Simba的原型系统，并在多种故障场景下进行评估。通过与现有解决方案的对比结果，验证了Simba的优越性。