ML-Based User Plane Traffic Analysis for Network Parameter Tuning
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Solution Overview
Problem
Modern cellular networks face challenges in diagnosing and resolving issues such as packet loss, latency, and quality of service degradation due to the complexity and scale of GTP-U tunneling, which obscures the direct visibility of data packet paths, leading to poor performance in real-time applications.
Innovation Solution
A machine learning model analyzes historical and real-time user plane network traffic to identify conditions indicative of network issues, enabling adjustments to network parameters like timers, thresholds, resource allocation, and traffic rerouting to prevent these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If GTP-U tunneling is used to transport user plane traffic, then network flexibility and scalability are improved, but visibility of data packet paths deteriorates making diagnosis difficult
Solution Approach 1:
The patent introduces machine learning models as intermediaries between network traffic and diagnostic systems. These models analyze traffic patterns, packet metadata, and network state information to infer path conditions and diagnose issues without requiring direct visibility into encapsulated GTP-U tunnel paths. The ML models act as mediators that translate opaque tunneling traffic into actionable diagnostic insights.
2Measurement precision
If network parameters are adjusted manually to resolve issues, then diagnostic precision can be maintained, but response time and productivity deteriorate
Solution Approach 1:
The patent implements self-service through automated machine learning models that continuously monitor network traffic, automatically detect anomalies, diagnose root causes, and recommend or execute parameter adjustments without human intervention. The system serves itself by using ML algorithms to perform what would traditionally require manual network engineering expertise, thereby maintaining diagnostic precision while dramatically improving response time.
Solution Approach 2:
The patent establishes feedback loops where ML models continuously analyze network traffic outcomes, compare expected versus actual behavior, and automatically adjust network parameters based on learned patterns. This closed-loop feedback system enables rapid iterative optimization of network performance while maintaining precise diagnostic capabilities through continuous learning from network responses.
3Ease of manufacture
If traditional network monitoring methods are used, then implementation simplicity is maintained, but ability to detect and resolve issues in complex tunneling environments deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/network monitoring methods with machine learning-based detection systems. Instead of relying on simple packet capture and rule-based analysis, the system uses ML models trained on network traffic patterns to automatically detect anomalies, predict failures, and diagnose issues in GTP-U tunneling environments. This substitution maintains ease of deployment through standardized ML pipelines while significantly improving reliability in complex tunneling scenarios.
Data Source
AI summary
A method includes executing a machine learning model on a computing system, the computing system operating on a centralized node of a wireless communication network. The method further includes monitoring, using the machine learning model, user plane network traffic associated with a user plane tunnel in the wireless communication network. The method further includes detecting, using the machine learning model, an occurrence of one or more conditions in the user plane network traffic indicative of one or more network issues. The method further includes adjusting, by the computing system, one or more network parameters of the wireless communication network based on detecting the occurrence of the one or more conditions in the user plane network traffic.