  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.
