Wireless Fault Detection via Message Sequence Analysis
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Solution Overview
Problem
Existing wireless communication systems lack efficient methods for detecting faults, predicting adverse operating conditions, and automatically remediating issues in real-time, leading to suboptimal user experiences and delayed issue resolution.
Innovation Solution
The system monitors message sequences between wireless terminals and access points, analyzes these sequences using machine learning to identify patterns associated with poor service, and automatically takes corrective actions based on predefined clusters of message sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated fault detection and remediation systems are implemented, then service quality and fault response time are improved, but device complexity and implementation cost increase
Solution Approach 1:
The fault detection system is segmented into multiple independent components: message sequence monitoring module, pattern recognition module, corrective action module, and cluster management module. Each component performs a specific function, making the overall complex system manageable and maintainable while achieving reliable automated fault detection and remediation.
Solution Approach 2:
The system pre-establishes clusters of message sequences associated with poor service conditions and defines corrective actions for each cluster before actual faults occur. When a fault condition is detected, the system simply matches the current message sequence against predefined clusters and executes the corresponding pre-planned corrective action, enabling rapid response without complex real-time decision-making.
2Speed
If real-time monitoring of message sequences is performed, then fault detection speed is improved, but processing overhead and energy consumption increase
Solution Approach 1:
The system monitors only the essential message sequences between user equipment and access points that are indicative of service quality, rather than analyzing all network traffic. By focusing on partial but critical message sequences and their temporal patterns, the system achieves fast fault detection with minimal processing overhead and energy consumption.
3Measurement precision
If clusters of message sequences associated with poor service are pre-defined, then fault identification accuracy is improved, but initial system configuration time and complexity increase
Solution Approach 1:
The system pre-defines clusters of message sequences and their associated corrective actions during system initialization or offline analysis phases. This preliminary configuration enables rapid online fault identification by simple pattern matching, achieving high accuracy without consuming configuration time during actual operation. The trade-off is acceptable because the clustering can be performed offline using historical data.
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AI summary
Methods and apparatus for automatically identifying and correcting faults relating to poor communications service in a wireless system, e.g., in real time, are described. The methods are well suited for use in a system with a variety of access points, e.g., wireless and/or wired access points, which can be used to obtain access to the Internet or another network. Access points (APs), which have been configured to monitor in accordance with received monitoring configuration information, e.g. on a per access point interface basis, captures messages, store captured messages, and in collaboration with network monitoring apparatus which can be in an AP or external thereto, use message sequences to determine a remedial action to be automatically taken when poor service is likely as may be predicted based on the detected message sequence between a UE and one or more APs.