Mobile Network Signalling Storm Prediction Using NWDAF

The system performs preliminary detection and prediction of signalling storms before they cause network overload. The NWDAF collects metrics and uses machine learning models to predict abnormal signalling patterns in advance, enabling proactive mitigation actions to be taken before the harmful effects manifest, thus maintaining network reliability while preventing overload

The NWDAF acts as an intermediary component between the control plane elements and the network infrastructure. It collects metrics from control plane signalling, analyzes patterns using machine learning, and triggers mitigation mechanisms when abnormal patterns are detected, thereby mediating between free signalling flow and network protection

The NWDAF is designed as a multi-functional network function that performs multiple roles: collecting metrics from various control plane elements, storing historical data, training machine learning models, performing real-time prediction, and triggering mitigation mechanisms. This universal design consolidates complexity into a single specialized component rather than distributing it across multiple systems

The NWDAF employs self-service mechanisms by automatically collecting metrics, training its own machine learning models using historical data, and autonomously making prediction decisions. The system uses its own collected data to improve its prediction accuracy over time through continuous model training, reducing the need for external intervention and manual configuration