5G RAN Root-Cause Detection for Autonomous UE Issue Resolution
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Solution Overview
Problem
Existing wireless networks face inefficiencies in identifying and resolving performance issues for user equipment (UE) without customer input, leading to time-consuming troubleshooting processes and network resource mismanagement.
Innovation Solution
A system that proactively monitors network and UE performance data, using a cloud application and machine learning models to dynamically identify and address performance issues by adjusting UE settings or network components, with a two-tier processing approach involving non-real-time and near-real-time frameworks within a RIC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional troubleshooting processes are used without customer input, then network resource management becomes inefficient, but the troubleshooting time becomes excessively long
Solution Approach 1:
The system proactively collects and analyzes performance data from multiple network components and UE devices before customers report issues. By performing preliminary monitoring and analysis, the system identifies potential problems early and prepares diagnostic information, eliminating the need for time-consuming customer interviews and manual troubleshooting steps after issues occur.
Solution Approach 2:
The system automatically monitors network performance, identifies issues, and generates diagnostic reports without requiring customer input or involvement. The automated system serves itself by collecting data from network elements, analyzing performance metrics, and producing troubleshooting information independently, thereby improving efficiency while reducing time loss.
2Measurement precision
If comprehensive performance data is collected from multiple sources, then issue identification accuracy improves, but system complexity increases
Solution Approach 1:
The system divides the complex data collection task into separate modules, each responsible for gathering specific types of performance data from different network components (RAN, core network, UE devices). This segmentation allows each module to focus on specific data sources and formats, managing complexity while maintaining comprehensive coverage for accurate issue identification.
Solution Approach 2:
The system introduces intermediary components that aggregate and normalize data from multiple heterogeneous sources before analysis. These intermediaries standardize the data format and filter relevant information, reducing the complexity of direct multi-source data integration while preserving the accuracy benefits of comprehensive data collection.
3Ease of operation
If automated performance monitoring is implemented, then customer involvement is minimized, but network resource consumption increases
Solution Approach 1:
The system implements automated monitoring that focuses on collecting only the essential performance metrics needed for issue identification, rather than comprehensively monitoring all possible network parameters. By selecting partial but sufficient data points, the system achieves effective customer-independent troubleshooting while conserving network resources.
Solution Approach 2:
The system dynamically adjusts the level and type of performance data collected based on network conditions and detected anomalies. During normal operation, minimal data is collected to conserve resources, while automated triggers increase monitoring intensity only when potential issues are detected, balancing customer independence with resource efficiency.
Data Source
AI summary
A method includes receiving a first set of performance data from a user equipment within a radio access network, the first set of performance data being associated with a triggering event and including a plurality of parameters associated with the UE connectivity to the RAN, receiving, at the one or more processing devices, from one or more network components within the RAN, a second set of performance data associated with the triggering event, the second set of performance data representing performance of the RAN network, identifying based on the first and second sets of performance data, a root cause for the triggering event, identifying an action to be performed by the UE to address the root cause, and transmitting, by the one or more processing devices, a signal configured to instruct an application on the UE to perform the action to address the identified root cause.