Graph-Based Anomaly Detection in 5G Cellular Networks

The system enables self-service through automated anomaly detection and root cause analysis. The AI-based platform automatically monitors network elements, detects anomalies, identifies root causes, and executes healing actions without human intervention. This self-service capability addresses the infeasibility of traditional manual O&M in complex 5G networks by allowing the network to manage itself autonomously.

The patent replaces manual mechanical operations with AI-based automated systems. Instead of human operators manually monitoring and managing network elements, the system uses machine learning models, graph neural networks, and automated alerting mechanisms to perform these functions. This substitution enables feasible network management despite the complexity introduced by increased cell density and legacy network coexistence.

The system performs preliminary actions by continuously monitoring network elements and pre-processing data to build contextual understanding before anomalies occur. The platform maintains baseline profiles of network element behavior and pre-computes relationships between elements, enabling rapid detection and analysis when anomalies manifest. This preliminary preparation eliminates the need for time-consuming manual analysis during incident response.

The system implements continuous feedback loops where detected anomalies and their root causes feed back into the system to refine AI models and improve future detection accuracy. The automated healing actions also provide feedback on the effectiveness of responses, enabling the system to learn from past incidents and improve its performance over time. This feedback mechanism maintains high productivity as the system adapts to evolving network conditions.

The system applies local quality by tailoring monitoring and analysis approaches to the specific characteristics of each network element and its context. Rather than applying uniform monitoring to all elements, the platform adapts its surveillance intensity and analysis methods based on the element's role, criticality, and behavioral patterns. This localized approach maintains high detection accuracy for critical elements while reducing monitoring overhead for less critical ones, thereby managing complexity effectively.

The patent segments the monitoring function by organizing network elements into hierarchical structures and functional groups. The system divides the network into manageable segments (e.g., radio access network, core network, specific cell sites) and applies targeted monitoring strategies to each segment. This segmentation enables comprehensive coverage of all elements while maintaining manageable complexity through structured organization and prioritized surveillance based on segment criticality.

The system compensates for implementation complexity through self-service capabilities. Once deployed, the AI-based platform automatically performs anomaly detection, root cause analysis, and healing actions without requiring ongoing complex manual management. The self-service nature continues to reduce operational expenses by eliminating the need for human intervention in routine monitoring and corrective actions, offsetting the initial implementation complexity through long-term operational simplification.

The patent replaces complex manual management processes with AI-based automated systems that use machine learning, graph neural networks, and rule-based decision-making. These automated mechanisms handle the complexity of network management tasks such as anomaly detection, root cause analysis, and healing action execution. The substitution of mechanical manual operations with intelligent automated systems reduces operational expenses while managing the implementation complexity through standardized AI frameworks and modular system architecture.