Wireless Network Root Cause Detection Using UE Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wireless communication systems face challenges in identifying network issues that affect user equipment (UE) performance, as conventional methods relying on network data alone often fail to correlate with the user experience, leading to degraded service and suboptimal data throughput.
Innovation Solution
A server collects and analyzes UE data, including device-specific information such as 5G New Radio measurements and key performance indicators, to identify abnormal patterns and root causes of network issues using machine learning models, complementing network data for more accurate problem detection and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional network monitoring methods are used, then network data can be collected, but the ability to identify actual user experience issues is insufficient
Solution Approach 1:
The patent combines network data from the base station with device data from user equipment to create a comprehensive monitoring system. This merging of data sources allows correlation between network-side observations and actual user experience, resolving the contradiction by integrating multiple information streams rather than relying on network data alone.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes both network data and device data, using machine learning models to correlate the two data sources. This intermediary system bridges the gap between network monitoring capabilities and user experience assessment, enabling accurate issue identification.
2Reliability
If only network data is analyzed, then system complexity is low, but the reliability of service quality assessment deteriorates
Solution Approach 1:
The patent segments the monitoring system into distinct components: network data collection at the base station, device data collection at the user equipment, and centralized analysis. This segmentation allows each component to perform its specific function reliably while maintaining overall system manageability despite increased complexity.
Solution Approach 2:
The patent implements feedback mechanisms where device performance data is continuously monitored and fed back to the network system. This feedback loop enables dynamic adjustment and continuous improvement of service quality assessment, enhancing reliability through iterative optimization.
3Difficulty of detecting and measuring
If comprehensive UE data is collected, then anomaly detection capability improves, but data processing requirements increase
Solution Approach 1:
The patent applies preliminary processing and filtering to device data before comprehensive analysis. By pre-processing data at the source and filtering out irrelevant information beforehand, the system reduces the burden on centralized processing while maintaining strong anomaly detection capabilities through targeted analysis of pre-filtered data.
Data Source
AI summary
In some implementations, a device may identify a network node that supports a first radio access technology (RAT), wherein the network node is associated with sessions with user equipments (UEs). The device may calculate a percentage of traffic from the sessions that is associated with the first RAT and not a second RAT. The device may determine that the network node is associated with a score based on the percentage of traffic associated with the first RAT not satisfying a threshold. The device may collect, based on the network node being associated with the score, device data associated with the UEs. The device may determine a network issue associated with the network node based on the device data. The device may determine a root cause for the network issue. The server may transmit an indication of the root cause.