Predictive Congestion Avoidance via Dominant Mobility Paths
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Solution Overview
Problem
Mobile wireless networks face challenges in predicting and preventing resource overload in cells due to varying user density and complex mobility patterns, leading to undesirable overload scenarios and poor user experience.
Innovation Solution
A system and method that utilize a central or distributed radio resource controller to analyze handover data and build maps of predominant mobility paths across cells, predicting future network conditions and proactively allocating resources to prevent overload by simulating user movement along dominant paths and reserving resources in anticipation of peak demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If reactive resource management based on initial planning and periodic drive testing is used, then operator costs are reduced through simplified operations, but network reliability deteriorates due to inability to proactively prevent overload scenarios
Solution Approach 1:
The system performs preliminary actions by proactively predicting future network conditions based on dominant mobility paths and preemptively allocating resources before overload occurs. The radio resource controller analyzes current traffic load and user mobility patterns to anticipate future demand, then reserves resources in advance along predicted dominant paths, transforming reactive management into proactive prevention while maintaining operational simplicity.
2Reliability
If autonomous predictive methods are implemented, then network reliability improves through proactive overload prevention, but device complexity increases due to additional prediction and control systems
Solution Approach 1:
The system enables self-service by allowing the network to autonomously predict its own future conditions and manage its resources without external intervention. The radio resource controller automatically analyzes mobility patterns, predicts overload scenarios, and reallocates resources based on dominant paths, making the network self-regulating while keeping the added complexity centralized in the controller rather than distributed across all network elements.
3Reliability
If path-based predictive allocation is implemented, then resource allocation reliability improves through proactive reservation, but loss of time increases due to computational overhead in analyzing mobility patterns
Solution Approach 1:
The system performs preliminary computation of dominant paths and mobility patterns during periods of lower demand, building predictive models in advance. By pre-analyzing mobility patterns and establishing dominant paths before peak demand occurs, the system reduces real-time computational overhead while maintaining high allocation reliability when needed most.
4Productivity
If dominant path analysis is performed to predict user movement, then productivity improves through optimized resource allocation, but difficulty of detecting and measuring increases due to complexity in tracking mobility patterns
Solution Approach 1:
The system extracts and focuses only on the essential mobility information needed for prediction - specifically dominant paths and their associated traffic loads - rather than attempting to analyze all possible mobility patterns. By isolating the critical path information from the complex full mobility dataset, the system achieves high productivity through optimized resource allocation while reducing the effective complexity of detection and measurement to only the most relevant parameters.
Data Source
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AI summary
A method for path predictive congestion avoidance includes receiving activity data for a plurality of cells, determining dominant paths for each of a plurality of cells based on the activity data, predicting a future network condition based on the dominant paths, and allocating network resources based on the predicted future network condition.