Service Trajectory Analysis for Mobile Network Fault Localization
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Solution Overview
Problem
Existing network fault diagnosis systems struggle to accurately identify the root causes of soft failures in mobile wireless infrastructures due to changing network topology, lack of continuous monitoring, and difficulties in reproducing problematic conditions, which are not effectively addressed by current service performance monitoring and network event counters.
Innovation Solution
A method and apparatus that utilize movement trajectories of mobile terminals correlated with service session records to determine service trajectories, clustering these trajectories to identify network elements causing service degradation, employing mobility and service monitor modules with analyser capabilities for spatial and temporal clustering to pinpoint the location of failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing O&M solutions use alarm-based Fault Management and Performance Management systems, then hard failures can be diagnosed, but soft failures cannot be detected or localized
Solution Approach 1:
The system dynamically adapts to changing network conditions by continuously monitoring service quality metrics and adjusting the analysis in real-time. The service trajectory analysis dynamically tracks mobile terminals as they move through the network, adapting to topology changes without requiring static inference graphs. This dynamic approach enables detection of soft failures that static alarm-based systems miss.
Solution Approach 2:
The invention changes the monitoring parameters from binary alarm states to continuous service quality metrics. By measuring parameters like service quality indicators, trajectory convergence, and performance degradation levels, the system can detect gradual soft failures. The analysis transitions from discrete alarm events to continuous parameter monitoring, enabling detection of intermittent performance impairments.
2Measurement precision
If full-scale continuous probing or monitoring infrastructure is deployed, then soft failures can be detected, but resource requirements and deployment cost increase significantly
Solution Approach 1:
The invention extracts only the necessary monitoring data from the network - specifically service quality metrics and terminal location information - rather than implementing full-scale probing infrastructure. By taking out only the essential data elements needed for trajectory analysis, the system achieves soft failure detection with minimal additional resources. The approach extracts meaningful signals from existing network data without requiring extensive monitoring points.
Solution Approach 2:
The system leverages existing network data and self-organizes the analysis through automated trajectory computation and convergence detection. Rather than requiring external probing infrastructure, the network's own service quality reports and location data are utilized. The automated analysis engine processes this data to identify soft failures, making the system self-sufficient without additional monitoring hardware.
3Device complexity
If network topology is assumed static or changes slowly, then inference graphs can be built for fault diagnosis, but mobile networks with frequent topology changes cannot be effectively diagnosed
Solution Approach 1:
The system replaces static inference graphs with dynamic service trajectory analysis. Instead of building complex causal relation graphs that assume static topology, the invention dynamically tracks terminal service trajectories and identifies failures through trajectory convergence. This dynamic approach naturally adapts to mobile network topology changes without requiring graph reconstruction, simplifying the diagnostic mechanism while maintaining effectiveness in mobile environments.
4Loss of information
If service performance monitoring uses terminal reports and packet traces, then service quality metrics can be calculated, but root cause localization remains difficult
Solution Approach 1:
The invention adds a spatial dimension to service quality analysis by incorporating terminal location and movement trajectory information. Instead of analyzing service metrics in isolation, the system maps these metrics onto the spatial trajectory of terminals. The convergence of multiple service trajectories in space and time provides precise localization of the failure point, transforming quality information into actionable location data through dimensional augmentation.
Data Source
AI summary
A method for determining location of a failure causing degradation of a service in a mobile communications network is disclosed. Service trajectories for the mobile terminals are determined (104) by correlating earlier obtained movement trajectories (102) of individual mobile terminals with service session records of the mobile terminals. In the next step distributions of the service trajectories of mobile terminals with degraded service are determined and then a network element around which the service trajectories converge is identified (108) as the location of the failure. An apparatus, a communications network and a computer program product are also disclosed.