Predictive Mobile Network Handover Management Without Manual Whitelists
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Solution Overview
Problem
Existing handover management in mobile communication networks is challenging, particularly when a mobile terminal moves between different operator networks, requiring manual effort for IMSI whitelist management and radio network planning, which does not scale well and fails to address overlapping network challenges.
Innovation Solution
A method and system that utilize data analysis and machine-learning models to predict the need for handover by evaluating the behavior of mobile terminals, generating control signals to trigger seamless handovers between networks, and manage service execution during the handover process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual IMSI whitelist management is used to control handover authorization, then network security and authorized access are ensured, but the management complexity and manual effort increase significantly
Solution Approach 1:
The system enables automated handover management where the network automatically determines authorization and triggers handovers based on behavioral analysis, eliminating the need for manual IMSI whitelist configuration and reducing management complexity while maintaining security
Solution Approach 2:
Manual administrative processes for whitelist management are replaced with automated machine-learning-based behavioral analysis systems that automatically identify and authorize handovers based on predicted user behavior patterns
2Reliability
If manual radio network planning is performed to guarantee handover success, then service quality is improved, but the planning time and resource requirements increase
Solution Approach 1:
The system performs preliminary behavioral analysis and handover prediction in advance, identifying potential handover needs before they occur, which allows proactive preparation and reduces the need for extensive manual radio network planning
Solution Approach 2:
The system continuously monitors mobile terminal behavior and uses feedback from behavioral analysis to dynamically adjust handover decisions, improving handover success rates through data-driven optimization rather than static manual planning
3Measurement precision
If automated behavioral analysis is used to predict handover needs, then handover decision accuracy is improved, but the computational resources and data processing requirements increase
Solution Approach 1:
The system applies behavioral analysis selectively to identify specific handover scenarios that benefit most from prediction, rather than uniformly applying complex analysis to all cases, thus balancing accuracy with computational efficiency
Data Source
AI summary
A method for triggering a handover of a mobile terminal from a first network to a second network is provided, the method comprises: accessing data descriptive of a behavior of mobile terminals served by the first mobile communication network; detecting a mobile terminal whose behavior is indicative of an expected need of a handover; generating a control signal to a control entity of the first mobile communication network to request triggering of a handover of the mobile terminal to the second mobile communication network. A computing system, a computer program and a communication system are also provided to.


