Mobile Network Testing via Machine Learning Prediction
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Solution Overview
Problem
Current mobile network testing methods require extensive drive testing campaigns to gather statistically significant data, consuming significant time and resources, as they need to simulate various user behaviors to assess network quality effectively.
Innovation Solution
The implementation of deep learning models, specifically supervised machine learning using labeled training data, to predict the probability of binary test results, reducing the necessity for multiple tests and enabling faster data collection by training a machine learning model that can map new examples beyond initial training examples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If drive testing campaigns are run to gather statistically significant data, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by training a machine learning model offline using historical drive testing data. Once trained, the model can quickly predict network quality metrics without requiring new extensive drive testing campaigns, thus resolving the contradiction between statistical significance and testing time.
Solution Approach 2:
Instead of performing physical drive testing to gather statistically significant data, the system creates a virtual copy through machine learning predictions. The trained model replicates the function of extensive drive testing by predicting network quality metrics from limited input data, eliminating the need for time-consuming repeated tests.
2Reliability
If multiple test procedures are run to assess different service qualities, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The machine learning model is designed with multi-functionality to predict multiple service quality metrics (drop call rate, data service accessibility, video re-buffering, etc.) simultaneously from a single set of input features. This universal approach maintains comprehensive service quality assessment while eliminating the need to run separate test procedures for each metric.
Solution Approach 2:
The system merges multiple test procedures into a single integrated prediction process. Instead of running separate tests for different service qualities, the machine learning model combines multiple prediction tasks into one unified operation, where a single prediction can provide information about multiple network quality aspects.
3Measurement precision
If extensive drive testing campaigns are conducted, then data accuracy is improved, but device complexity increases
Solution Approach 1:
The system replaces the mechanical complexity of extensive drive testing equipment and procedures with a computational machine learning model. Instead of using specialized testing equipment to gather statistically significant data through physical testing, the solution uses software-based predictions that require minimal hardware intervention.
Solution Approach 2:
The machine learning model enables the network testing system to serve itself by automatically predicting service quality metrics without requiring external drive testing campaigns. The trained model can independently assess network quality using readily available network parameters, eliminating the need for complex external testing infrastructure.
Data Source
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AI summary
A method for mobile network testing is described wherein at least one testing device (12) is used that is configured to be connected to a mobile network. A predefined set of test procedures is run on the at least one testing device (12) in order to obtain binary test results assigned to at least one test parameter. The binary test results of the test procedures are evaluated via a machine learning model. The machine learning model is trained to predict the probability of at least one binary test result based on the binary test results obtained. Further, a test system (10), a method for mobile network testing as well as a prediction system (20) are described.