Self-Organizing Network Traffic Forecasting for Event-Driven Load Balancing
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Solution Overview
Problem
Existing SON systems in mobile communication networks only optimize based on internal network data, neglecting external events that significantly influence network performance, such as gatherings due to public happenings, leading to inefficiencies.
Innovation Solution
A self-organizing system that collects external data on events like gatherings, processes it to forecast traffic, correlates with internal network data to identify potential issues, and generates configuration updates to mitigate these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SON systems only use internal network data for optimization, then the system complexity remains manageable, but the optimization performance is limited and cannot account for external event impacts
Solution Approach 1:
The patent merges internal network data with external event data to create a comprehensive optimization system. The SON system collects both internal data (network performance metrics) and external data (event information from various sources), then processes and correlates this combined data to generate optimized network configurations that account for both internal and external factors.
Solution Approach 2:
The patent introduces an intermediary processing layer that handles external data collection, processing, and correlation with internal network data. This intermediary component (the external data processing module) mediates between external event sources and the core SON optimization functions, enabling integration without directly increasing the complexity of the core SON system.
2Reliability
If the amount of internal network data retrieved from the mobile communication network is increased, then the optimization performances are improved, but the data processing burden and system complexity increase
Solution Approach 1:
The patent segments the data processing function into distinct modules: internal data collection, external data collection, data processing, and optimization generation. This segmentation allows each module to specialize in handling specific data types, improving overall processing efficiency and reducing the burden on any single component.
Solution Approach 2:
The system performs preliminary processing of external event data to extract relevant information and correlate it with internal network data before generating optimization configurations. This preliminary action filters and prepares data in advance, reducing the processing burden during the main optimization cycle.
3Reliability
If external event data is collected and processed to forecast traffic, then network performance optimization is enhanced, but the system complexity and data processing requirements increase
Solution Approach 1:
The external data processing module is designed with multi-functionality, handling data collection from diverse sources, data processing, traffic forecasting, and correlation with internal network data. This universal component performs multiple functions that would otherwise require separate systems, reducing overall architectural complexity while enhancing optimization capability.
4Ease of operation
If human operators are involved in network optimization, then detailed control and adjustment are possible, but the operator burden and manual work increase
Solution Approach 1:
The SON system performs self-optimization by automatically collecting internal and external data, processing this data, generating optimization configurations, and enforcing changes without requiring manual intervention from human operators. The system serves itself by continuously monitoring network conditions and external events, then autonomously adjusting network parameters to optimize performance.
Data Source
AI summary
A self-organizing system for mobile communication optimization includes circuitry that collects external data related to an external event causing gathering of user equipment in a geographic area. The circuitry obtains spatial and time identifiers of the external event based on the external data in order to forecast data traffic due to the external event. The circuitry then generates a corresponding critical issues report identifying potential critical issues based on the data traffic forecast, and generates and enforces a network communication update based on the critical issues report.