Mobility Robustness Optimization via Failure Categorization
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Solution Overview
Problem
Existing mobile communication networks face challenges in analyzing and categorizing mobility failures during handover procedures, particularly when the network lacks necessary UE context, leading to difficulties in optimizing L1/L2 mobility parameters.
Innovation Solution
A method is disclosed that detects mobility failures and successful handovers, determines the type of failure, generates mobility failure information, and provides this information to enable networks to identify misconfigurations of parameters controlling L3 handover procedures or L1/2 inter-cell mobility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the network stores UE context information for extended periods to enable mobility failure analysis, then the ability to categorize mobility failures improves, but the network resource consumption and system complexity increase
Solution Approach 1:
The network performs preliminary actions by storing mobility failure information and UE context in advance before failures occur. This allows the network to have ready-to-analyze data when mobility failures happen, eliminating the need for complex real-time data collection and processing during failure events.
Solution Approach 2:
The patent introduces an intermediary mechanism (storage system/database) that retains UE context and mobility failure information. This intermediary allows the network to retrieve and analyze historical data without requiring continuous real-time monitoring complexity, bridging the gap between information availability and system simplicity.
2Measurement precision
If the network collects and stores detailed mobility failure information, then the precision of mobility failure categorization improves, but the information processing load and storage requirements increase
Solution Approach 1:
The patent extracts only the essential and relevant mobility failure information parameters needed for accurate categorization, rather than storing all possible UE context data. This selective extraction reduces the volume of stored information while maintaining sufficient precision for failure analysis.
Solution Approach 2:
The network applies local quality by storing different types and amounts of information based on specific needs - detailed mobility failure information is stored when analysis is required, while routine operations use streamlined data sets. This allows high precision where needed without uniformly increasing storage requirements across all operations.
3Reliability
If the network adjusts L3 handover parameters frequently to optimize mobility, then the mobility robustness improves, but the parameter tuning complexity and potential for instability increase
Solution Approach 1:
The patent implements feedback mechanisms where mobility failure information is collected, analyzed, and used to adjust L3 handover parameters. This closed-loop feedback allows the network to optimize mobility robustness based on actual performance data while maintaining systematic control over parameter adjustments, reducing the complexity of frequent manual tuning.
Solution Approach 2:
The network performs preliminary parameter adjustments based on pre-analyzed mobility patterns and historical failure data. This allows optimization to be done in advance during low-traffic periods, reducing the need for frequent reactive adjustments and simplifying the overall parameter management process.
Data Source
AI summary
A method detecting at least one of a mobility failure and a respective successful handover; determining a type of the mobility failure, wherein the type of the mobility failure is determined in response to the detected mobility failure; generating mobility failure information indicative of one or more parameters associated with the type of mobility failure, wherein the mobility failure information is generated, at least in part, based on the determined type of the mobility failure; and providing the mobility failure information and/or successful handover information including, at least in part, one or more parameters associated with the respective successful handover, wherein mobility failure information and/or successful handover information enable a network to determine whether a root cause associated with at least one of a respective mobility failure information and a respective successful handover information is a misconfiguration of one or more parameters controlling L3 handover procedures or L1/2 inter-cell mobility.