5G User Plane Prediction Using Control Plane Causality
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Solution Overview
Problem
Traditional machine learning techniques fail to capture relationships between control plane information and user plane metrics in dynamic cellular network environments, leading to suboptimal performance optimization, network traffic management, and user experience enhancement.
Innovation Solution
A system utilizing control plane features and Granger causality for predicting user plane metrics, incorporating sensors, cloud computing servers, and controllers to adapt to dynamic wireless conditions, employing Granger causality tests, prediction models, and non-uniform upsampling to reduce data input and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning techniques are used for feature selection, then the system can process data, but it fails to capture relationships between control plane information and user plane metrics in dynamic environments
Solution Approach 1:
The system dynamically adapts to changing network conditions by continuously monitoring control plane information and updating predictions in real-time. The machine learning model processes streaming data from control plane messages (RRC, NAS) and adjusts predictions based on current network state, making the system adaptable to dynamic environments while maintaining reliable predictions.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing predicted user plane metrics with actual measurements and using control plane information to refine predictions. The control plane data provides feedback about network conditions that helps correct and improve prediction accuracy over time, capturing the relationships between control and user plane metrics.
2Reliability
If comprehensive control plane data is collected and processed, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system extracts only the most relevant features from control plane information for prediction purposes. Instead of processing all control plane data, the system identifies and extracts key features from control plane messages (RRC, NAS, mobility management) that have the strongest correlation with user plane metrics, reducing computational complexity while maintaining prediction reliability.
Solution Approach 2:
The system segments the control plane data processing into distinct modules: message parsing (RRC, NAS), feature extraction, Granger causality testing, and prediction generation. This segmentation allows each component to handle specific tasks efficiently, reducing overall system complexity while comprehensively analyzing control plane information for accurate predictions.
3Measurement precision
If Granger causality tests are performed on all control plane features, then feature selection accuracy improves, but computational burden increases
Solution Approach 1:
The system performs Granger causality tests on a selected subset of control plane features rather than all possible features. By identifying and testing only those features that show preliminary correlation with user plane metrics, the system achieves sufficient feature selection precision without the excessive computational burden of testing all control plane features comprehensively.
Data Source
AI summary
A system for optimizing performance, managing network traffic, and enhancing user experience includes a host device a cloud computing server and controllers. The controllers execute control logic including a prediction application (PA) that obtains sensor data from the sensors, sends control and user plane data to infrastructure, and to the cloud computing servers, and accesses prior knowledge data stored within memory of the cloud computing servers. Additional control logic performs a Granger causality test on the user and control plane data, and utilizes a prediction model to generate a prediction from fused user and control plane data. A prediction verifier is applied to the prediction from the prediction model, and the PA enables the host device to adapt to dynamic wireless communications network conditions.