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.

5G 技术带来了重大的里程碑发展，允许新的应用，如增强现实和自动驾驶车辆。然而，这些改进带来了管理复杂性的增加和对故障处理的特别关注，因为 5G 旨在支持的应用程序严重依赖于高网络性能和低延迟。因此，自动自愈解决方案已经成为处理这一要求的有效方式，允许基于学习的系统自动检测异常并执行根本原因分析 (RCA)。然而，实施这种智能系统存在固有的挑战。首先，在异常检测和 RCA 方面缺乏合适的数据，因为故障场景的标记数据不常见。其次，当前的智能解决方案针对 LTE 网络定制，没有完全捕捉到数据中存在的时空特征。鉴于此，我们利用一个校准的模拟器 Simu5G，生成开源的正常和故障场景数据。使用该数据，我们提出了 Simba，一个在 5G 无线接入网络 (RANs) 中进行异常检测和根本原因分析的最先进方法。我们利用图神经网络来捕捉空间关系，而 Transformer 模型则用于学习数据的时间依赖性。我们实现了 Simba 的原型并对多个故障进行了评估。将结果与现有解决方案进行比较，验证了 Simba 的优越性。