Network Feature Tester Using Clustered Device Selection
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Solution Overview
Problem
Current methods for testing new features in wireless carrier networks are limited in scope and can introduce bias, as they often rely on controlled environments or specific locations, failing to accurately isolate the impact of the feature on user devices and network performance across the entire coverage area.
Innovation Solution
A system that collects data on user habits and device characteristics to select a representative group of users and devices, clusters them based on usage patterns, and deploys the new feature to specific clusters, allowing for comprehensive testing by comparing user experience with and without the feature across similar conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If testing is performed in a controlled environment or at predetermined locations, then the testing process is simplified and easier to manage, but the testing scope is limited and bias is introduced
Solution Approach 1:
The patent segments the network coverage area into multiple geographic clusters and selects specific clusters for testing based on user device characteristics. This segmentation allows the testing to be distributed across multiple locations rather than confined to a single controlled environment, thereby expanding testing scope while maintaining manageable complexity through structured cluster selection.
Solution Approach 2:
The patent applies local quality by selecting testing clusters based on specific user device characteristics and usage patterns. Different geographic clusters are chosen to represent different user segments, ensuring that the testing reflects diverse real-world conditions rather than a uniform controlled environment, thus improving measurement precision.
2Measurement precision
If the testing scope is expanded to include the entire coverage area, then the measurement accuracy improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by collecting user device characteristics and usage patterns before selecting testing clusters. This advance preparation allows the system to pre-identify suitable geographic clusters that represent different user segments, simplifying the subsequent testing process and reducing the complexity of real-time data processing during the actual feature testing.
Solution Approach 2:
The patent introduces geographic clusters as intermediaries between the network feature and user devices. Instead of directly testing across the entire coverage area, the system uses selected geographic clusters as intermediate testing zones, which simplifies the testing architecture while still capturing diverse user experiences across different locations.
3Measurement precision
If user data is collected to train a model for selecting testing groups, then the representativeness of the testing sample improves, but the loss of time for data collection and model training increases
Solution Approach 1:
The patent performs preliminary data collection and model training before the actual feature testing begins. User device characteristics and usage patterns are gathered in advance, and the machine learning model is trained beforehand to identify suitable testing clusters. This preliminary action ensures that the testing sample is highly representative while minimizing time loss during the actual testing phase.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a network feature tester are disclosed. In one aspect, a method includes the actions of selecting computing devices that are configured to communicate with a wireless carrier network based on the computing devices having a common characteristic. The actions further include identifying a subset of the computing devices that are likely to communicate with a same base station of the wireless carrier network during a first period of time and a second period of time. The actions further include determining first monitoring data during the first period of time. The actions further include adjusting a characteristic of the same base station. The actions further include determining second monitoring data during the second period of time. The actions further include determining an effect that adjusting the characteristic has on the wireless carrier network.