RAN Enhanced Session Records for Real-Time User Plane Monitoring
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Solution Overview
Problem
Existing RAN monitoring systems face challenges in efficiently processing and analyzing raw trace-port data for real-time UP experience monitoring, as they are resource-intensive and lack sufficient information for assessing quality of user plane experience, particularly in cases of call gaps affecting multiple subscribers.
Innovation Solution
A method and system for generating enhanced session records (ESRs) by processing access stratum data from cell trace records, incorporating geolocation, radio frequency conditions, and user plane data to detect state transitions and output enriched records suitable for machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw trace-port data is processed using conventional monitoring systems, then comprehensive data analysis is achieved, but resource consumption increases and real-time processing becomes infeasible
Solution Approach 1:
The patent segments the monitoring system into multiple specialized components: a data collection module that gathers raw trace-port data, a data processing module that filters and enriches the data, and a machine learning module that performs analysis. This segmentation allows each component to operate independently and efficiently, enabling real-time processing while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent introduces an intermediary data processing layer between raw data collection and machine learning analysis. This intermediary layer pre-processes, filters, and enriches the raw trace-port data, transforming it into a format suitable for ML consumption. This mediation reduces the computational burden on the ML system and enables real-time processing.
2Productivity
If Location Session Records (LSRs) are used for monitoring, then data volume is reduced, but insufficient information is available for assessing quality of user plane experience
Solution Approach 1:
The system performs preliminary enrichment of LSR data by incorporating user plane metrics (throughput, packet loss, latency) and radio access network conditions before the data reaches the analysis stage. This preliminary action ensures that when data is condensed into LSR format, critical user plane experience information is already embedded, preventing information loss while maintaining processing efficiency.
Solution Approach 2:
The patent applies local quality enhancement by selectively adding specific user plane metrics and RAN conditions to relevant LSR records based on the particular monitoring needs and event types. Rather than uniformly expanding all LSR data, the system locally enriches only the portions of data that require additional user plane experience information, maintaining efficiency while preventing information loss.
3Measurement precision
If extensive data processing is performed for troubleshooting, then analysis depth is improved, but processing time increases making it unsuitable for ongoing monitoring
Solution Approach 1:
The patent implements dynamic processing that adapts the level of data analysis based on the operational context. During ongoing monitoring, the system performs lightweight real-time analysis. When troubleshooting is triggered by detected anomalies or user requests, the system dynamically switches to extensive deep-dive analysis mode. This dynamic approach allows the system to maintain low processing time during normal operation while providing deep analysis capability when needed.
Solution Approach 2:
The system employs periodic lightweight monitoring combined with event-triggered extensive analysis. Rather than continuously performing deep troubleshooting analysis, the system periodically checks key metrics and only initiates extensive processing when specific events occur (such as call gaps, quality degradation, or user complaints). This periodic action pattern reduces average processing time while maintaining the capability for deep analysis when required.
Data Source
AI summary
A computer-implemented method of monitoring a radio access network (RAN) is provided. The method includes receiving access stratum data that is a function of cell trace records (CTRs) associated with wireless communication transported to or from one or more cells of the RAN, wherein the CTRs are obtained at a granularity sufficient to detect one or more events, the events defining a segment or occurring during a segment, wherein a segment is defined by the beginning, end, or any handovers of a call included in the wireless communication. The method further includes detecting in the access stratum data one or more state transitions as indicated by the events and outputting an enhanced session record (ESR) including information processed from the access stratum data associated with the respective one or more detected state transitions.