ML-Based Wireless Network Interoperability Testing Feedback Loop

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Solution Overview

Problem

Conventional network testing solutions for cellular networks, such as 5G NR, are inadequate due to reliance on manual operations, leading to increased error likelihood, reduced interoperability, capacity, and security, and lack of real-time updates and automated configuration adjustments.

Innovation Solution

A network testing system utilizing machine learning to automate testing processes, provide real-time updates, and recommend configuration changes based on test failure analysis, using AI and ML components to enhance testing efficiency and accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual operations are used for network testing, then testing processes can be performed with existing tools, but error likelihood increases and testing accuracy decreases

Engineering Contradiction:

Improvetesting accuracyVSAvoidmanual operation level

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The testing system performs self-diagnosis and self-configuration through machine learning models that automatically analyze test failures, identify root causes, and recommend configuration changes without requiring manual intervention. The system serves itself by autonomously improving its testing capabilities based on learned patterns from test data.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical testing operations are replaced with an intelligent software-based system that uses machine learning algorithms, neural networks, and automated analysis tools to perform testing, diagnose issues, and optimize network configurations, thereby eliminating human error and improving reliability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If conventional testing methods are used, then existing testing infrastructure can be maintained, but interoperability and security are reduced

Engineering Contradiction:

Improvenetwork interoperabilityVSAvoidtesting system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The testing system is designed with multi-functional capabilities that can test various network protocols, devices, and configurations through a single unified platform. The machine learning models are trained on diverse test data to recognize patterns across different network types, enabling the system to adapt to new interoperability scenarios without requiring separate specialized tools for each case.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If manual testing processes are used, then testing can be performed with current resources, but testing speed and productivity are reduced

Engineering Contradiction:

Improvetesting speedVSAvoidautomated configuration adjustment

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The system implements continuous feedback loops where test results are automatically analyzed by machine learning models, which then generate real-time recommendations for configuration adjustments. This closed-loop feedback mechanism enables rapid iterative testing and optimization, significantly increasing testing speed compared to manual processes where feedback cycles are much slower.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The machine learning models are pre-trained on extensive test data and historical network configurations, enabling them to predict potential issues and suggest optimal configurations before actual testing occurs. This preliminary analysis accelerates the testing process by avoiding trial-and-error approaches and directly guiding testing toward critical scenarios.

Inventive Principle:

Principle #10Preliminary action

4Reliability

If real-time updates and automated adjustments are not implemented, then system complexity remains low, but testing reliability and user experience are reduced

Engineering Contradiction:

Improvenetwork performance reliabilityVSAvoidtesting system architecture

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The testing system autonomously monitors its own performance, automatically updates test configurations based on learned patterns, and adjusts testing parameters in real-time without external intervention. This self-service capability enhances reliability by ensuring consistent, error-free testing while the automated nature of these functions manages system complexity through intelligent automation rather than manual processes.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260006468A1Network testing using machine learning

Publication Date: 2026.01.01 BOOST SUBSCRIBERCO LLC

AI summary

Implementations are described herein for network testing using machine learning. In some implementations, a testing system receives a user input regarding a test of a wireless communication system. The testing system receives a user input regarding a test of a wireless communication system, the user input indicating to test an interoperability between an interface in the wireless communication system and one or more components in the wireless communication system. The testing system determines, based on the user input, one or more parameters for the test of the wireless communication system. The testing system generates, based on performing the test of the wireless communication system using the one or more parameters, an output that indicates the interoperability between the interface and the one or more components.