Wireline and Wireless UE Testing for Automated Network Validation
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Solution Overview
Problem
Current mobile network testing is expensive, labor-intensive, and often misses critical variables, leading to inefficient data collection and limited assessment of user experience. Additionally, manual testing poses safety concerns and is not feasible for dynamic environments.
Innovation Solution
An automated network testing system that can command and control the deployment of test scenarios at scale, using user devices such as smartphones to execute tests over access networks, thereby characterizing network infrastructure and subscriber experience with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing is performed with testers dispatched to locations, then network functionality can be tested, but testing becomes expensive and labor-intensive
Solution Approach 1:
The system enables self-service testing by deploying automated testing agents on user devices that autonomously execute test scenarios and collect network performance data without requiring human testers to physically visit locations. The agents independently manage test execution, data collection, and reporting.
Solution Approach 2:
The patent replaces the mechanical system of manual testing with an automated electronic system. Physical dispatch of testers and manual data collection are substituted with automated software agents that remotely execute tests and collect performance metrics through electronic communication with network elements.
2Reliability
If testers physically travel to locations for visual inspection and device operation, then comprehensive testing can be performed, but safety concerns arise and cost increases
Solution Approach 1:
Testing agents deployed on user devices perform autonomous testing operations without human intervention. The agents self-manage test scenario execution, network interaction, and data collection, eliminating the need for testers to physically access potentially hazardous locations.
Solution Approach 2:
The testing agent acts as an intermediary between the tester and the remote network location. Instead of testers directly interacting with equipment at remote sites, the agent mediates all interactions, allowing comprehensive testing to be performed remotely without exposing humans to safety risks.
3Loss of time
If manual testing is used, then testing can be performed, but it is time-consuming and data collection is inefficient
Solution Approach 1:
The automated testing agents enable continuous testing operations without interruption. Multiple test scenarios can execute simultaneously across numerous user devices, and testing can continue uninterrupted during off-hours or peak network periods, maximizing data collection efficiency.
Solution Approach 2:
Test scenarios are pre-configured and deployed to user devices before actual testing begins. The system prepares test configurations, parameters, and scripts in advance, allowing immediate execution when conditions are met, thereby reducing overall testing time and enabling rapid data collection.
4Measurement precision
If extensive manual testing is conducted, then network issues can be identified, but the cost and complexity increase significantly
Solution Approach 1:
The testing agent is designed as a universal multi-functional platform that can execute diverse test scenarios across different network types and configurations. A single agent architecture handles multiple testing functions including performance measurement, fault detection, and capability assessment, reducing overall system complexity.
Solution Approach 2:
The testing system is segmented into independent modular components: test scenario definitions, execution engines, data collection modules, and analysis functions. This segmentation allows flexible configuration of test suites and enables parallel execution of multiple tests, improving precision while managing complexity through modularity.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, determining that one or more tests are to be executed for a core network of a telecommunications system, based on the determining, transmitting one or more test initiation commands to a set of user equipment (UEs) of a plurality of UEs communicatively coupled to the core network, wherein the one or more test initiation commands cause the set of UEs to execute the one or more tests, obtaining, from the set of UEs, results associated with the one or more tests, analyzing the results based on one or more machine learning (ML) models to identify a network issue, and responsive to the analyzing, performing one or more actions to address the network issue. Other embodiments are disclosed.