Wireless Terminal Fault Detection via Network Node Analysis
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Solution Overview
Problem
Existing methods for detecting faulty wireless terminals in communication networks rely on self-detection by terminals or solely on transmission power, leading to inaccurate identification and potential false positives, which can negatively impact user experience and network performance.
Innovation Solution
A method and apparatus that detect service incidents and calculate performance metrics over an observation time period to identify faulty wireless terminals, without requiring terminals to self-detect, by analyzing data from various network sources and determining a wireless terminal as faulty if the performance metric exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wireless terminals self-detect their faulty status, then faulty terminals can be identified, but false positives occur and user experience deteriorates
Solution Approach 1:
The patent introduces network nodes (MME, serving GPRS support node, mobility management entity) as intermediaries between wireless terminals and the detection system. These intermediaries collect service incident data from multiple sources including base stations and core network elements, enabling objective fault detection without relying on terminal self-reporting. This mediator approach resolves the contradiction by providing third-party verification that reduces false positives while maintaining accurate faulty terminal identification.
2Productivity
If detection is based solely on transmission power, then faulty terminals can be detected, but identification accuracy decreases due to inability to distinguish from network problems
Solution Approach 1:
The patent segments the detection process into multiple independent data sources and analysis dimensions. Instead of relying on a single transmission power metric, the system collects service incident data from multiple network nodes including base stations, core network elements, and mobility management entities. Each source provides independent measurements that are aggregated and analyzed separately, allowing the system to maintain fast detection while improving identification accuracy through multi-dimensional analysis that can distinguish terminal faults from network problems.
3Measurement precision
If multiple data sources are monitored continuously, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements a universal detection framework where a single network node performs multiple functions: collecting service incident data from various sources, analyzing multiple parameters simultaneously, and generating faulty terminal identification results. The system uses a unified service incident data structure that can accommodate different data types from base stations, core network elements, and mobility management entities, reducing overall system complexity while maintaining high identification accuracy through multi-source monitoring.
Data Source
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AI summary
One or more embodiments of a method and apparatus taught herein provide for the detection of faulty wireless terminals in a wireless communication network (10; 30). According to an exemplary method (100), an occurrence of one or more service incidents occurring for a wireless terminal (14; 34) during an observation time period is detected (102). For each service incident, a service quality score indicative of the severity of incident is determined (104). A performance metric is calculated responsive to the service quality scores of the wireless terminal (14; 34) occurring during the observation time period (106). The wireless terminal (14; 34) is determined to be faulty if the performance metric exceeds a service quality threshold (108). A corresponding network node (60) operable to implement the method is also disclosed.