Wireless QoS Anomaly Detection for Congestion and Misconfiguration
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Solution Overview
Problem
Existing wireless communication networks face challenges in efficiently detecting and resolving QoS violations, which can lead to degraded Quality of Experience (QoE) and Service Level Agreement (SLA) issues due to incorrect implementation of packet forwarding behaviors, particularly during congestion situations.
Innovation Solution
A method and system for detecting anomalies in wireless communication networks by analyzing measured QoS levels against defined traffic forwarding policies, identifying violations, and classifying them as either congestion or misconfigurations, enabling automatic adjustments or capacity increases to resolve these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If packet forwarding behavior is controlled using node-specific parameters and priority-based forwarding, then QoS targets can be met for high-priority traffic during congestion, but the system cannot fulfill the requirements of all QoS Flows and complex tracking mechanisms are required
Solution Approach 1:
The patent extracts the anomaly detection functionality from complex QoS tracking mechanisms and implements it as a separate AI/ML-based module. This allows the core packet forwarding system to remain simple while a dedicated component handles the complex analysis of QoS violations and anomaly detection, resolving the contradiction between reliable QoS monitoring and system complexity
Solution Approach 2:
The patent introduces an AI/ML-based intermediary layer between the packet forwarding system and the anomaly detection system. This intermediary processes QoS measurement data and uses machine learning models to detect anomalies, simplifying the overall system architecture while maintaining high reliability in QoS target fulfillment through intelligent analysis
2Loss of information
If QoS violations are tracked using existing mechanisms, then violation events can be monitored, but the information provided is complex and difficult to utilize in resolving problems
Solution Approach 1:
The patent implements a feedback mechanism where the AI/ML-based anomaly detection system continuously monitors QoS violations and provides actionable insights back to the network management system. The system analyzes patterns in violation data and generates specific recommendations for resolving anomalies, transforming complex raw data into easily interpretable feedback that guides problem resolution
Solution Approach 2:
The patent changes the parameters of information presentation by using AI/ML algorithms to transform raw QoS violation data into meaningful anomaly scores and classifications. Instead of providing raw tracking data, the system outputs processed information including anomaly types, severity levels, and root cause indicators, making the information much easier to utilize for problem resolution
Data Source
AI summary
A node of the wireless communication network receives data indicative of a measured QoS, level of data traffic assigned to different configured traffic forwarding policies each defining one or more target parameters. Based on the data and the target parameters, the node detects violations of the traffic forwarding policies. Based on the detected violations for each the traffic forwarding policies, the node detects at least one anomaly in the wireless communication network.