Correlator for Wireless KPI Accuracy
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Solution Overview
Problem
Hybrid Mobile Network Operators face challenges in accurately determining and monitoring Key Performance Indicators (KPIs) such as session drops and throughput drops, particularly due to false or 'ghost' call drops during user equipment device migrations between Mobile Virtual Network Operator (MVNO) and Multiple System Operator (MSO) networks, leading to incorrect performance assessments and unnecessary investigations.
Innovation Solution
A method and apparatus that utilize a cloud connection manager and correlator to receive user equipment snapshot data records, identify false session drops by comparing data from both networks, and modify Operations Support System (OSS) data to accurately reflect successful migrations, thereby avoiding misclassification of session drops and optimizing network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If UE devices migrate between MVNO and MSO networks independently, then network flexibility and load balancing are improved, but KPI accuracy deteriorates due to false session drop detection
Solution Approach 1:
A correlation system acts as an intermediary between MVNO and MSO networks, receiving session data from both networks and determining whether session drops are actual failures or result from legitimate migrations. The correlation system uses correlation identifiers to match sessions across networks and classify drops accurately, preventing false KPI degradation while maintaining network migration flexibility.
2Ease of operation
If networks operate independently without coordination, then network autonomy and operational independence are improved, but session transfer identification capability deteriorates
Solution Approach 1:
The system segments the KPI monitoring function from network operations. Each network maintains its own autonomous operation and generates its own session data independently. The correlation system separately processes data from both networks using correlation identifiers, determining session transfers through data comparison rather than requiring real-time coordination between networks during operation.
3Measurement precision
If session data from multiple networks is collected and correlated, then KPI accuracy is improved, but system complexity increases
Solution Approach 1:
The correlation system creates virtual copies of session data from both MVNO and MSO networks, storing them in a correlation database with correlation identifiers. This copying approach allows the system to analyze and compare session information without interfering with actual network operations. The copied data can be processed, correlated, and used for KPI accuracy improvement while maintaining network independence and reducing operational complexity.
Data Source
AI summary
The present invention relates to methods and apparatus for determining and/or using Key Performance Indictors, e.g., session drops, in wireless systems. An exemplary embodiment includes operation of a correlator comprising the steps of: receiving from a cloud connection manager a plurality of user equipment snapshot data records corresponding to successful user equipment device migrations from a first wireless network to a second wireless network; receiving from a first network core first Operations Support System (OSS) data, the first OSS data including a first plurality of session data records; identifying one or more false session drops in the first OSS data using the plurality of user equipment snapshot data records and the first plurality of session data records; modifying the first OSS data to mark or remove in the first OSS data the identified false session drops; and communicating the modified first OSS data to the first network core.