ML Neural Network Models for Wireless Network Issue Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for troubleshooting issues in wireless networks are resource-intensive, time-consuming, and cumbersome, requiring significant computing, networking, and staffing resources to analyze large volumes of interference data, making it difficult to identify and resolve issues efficiently.
Innovation Solution
An analysis platform utilizing machine learning and neural network models processes input data to cleanse, extract, and label interference patterns, associating labels with physical resource block images to identify potential issues, compressing data into arrays for probability scoring, and selecting the most probable issues for action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to analyze wireless network interference data, then measurement precision can be maintained through expert review, but productivity is severely reduced due to the time-consuming nature of manual analysis of large data volumes
Solution Approach 1:
The patent replaces the mechanical manual analysis system with an automated image processing and machine learning system. The system captures network interference data, converts it to visual images, and uses automated algorithms to identify issues, eliminating the need for manual review while maintaining or improving accuracy through consistent application of detection criteria across all data points.
Solution Approach 2:
The patent introduces an intermediary image processing layer between raw network data and final issue identification. By converting complex numerical interference data into visual images with distinct patterns, the system creates an intermediate representation that enables more efficient and accurate automated analysis while preserving the essential characteristics needed for precise issue detection.
2Reliability
If comprehensive analysis of all interference data is performed manually, then reliability of issue detection is improved, but loss of time increases significantly due to the volume of data requiring review
Solution Approach 1:
The system replaces time-consuming manual analysis with automated image processing and machine learning algorithms that can rapidly evaluate all interference data comprehensively. The automated system maintains reliability by applying consistent detection logic across the entire dataset without the time constraints that limit manual review scope and depth.
Solution Approach 2:
The patent performs preliminary data processing and transformation into visual images before the actual issue detection step. This preliminary action organizes and pre-processes the data in a way that enables faster and more reliable subsequent analysis, allowing comprehensive review without proportional time increase.
3Ease of operation
If manual troubleshooting processes are used, then ease of operation is maintained through straightforward procedures, but device complexity increases due to the need for multiple computing and networking resources
Solution Approach 1:
The patent merges multiple separate functions (data capture, image conversion, pattern recognition, issue identification) into an integrated automated system. This consolidation simplifies the operational process by providing a unified workflow while efficiently managing computing resources through coordinated operation of the combined functions rather than separate manual operations.
Solution Approach 2:
The system enables self-service automated troubleshooting by capturing, processing, and analyzing network interference data without requiring manual intervention. The automated image processing and machine learning algorithms independently perform the complete troubleshooting sequence, reducing operational complexity while utilizing computing resources more efficiently through automated resource management.
Data Source
AI summary
A device may receive input data associated with a wireless network, and may extract data from the input data to generate extracted data. The device may create PRB images based on the extracted data, and may process the PRB images, with a first model, to associate labels with each of the PRB images. The device may process the labels and the PRB images, with a second model, to identify potential issues associated with the PRB images, and may process data identifying the potential issues associated with the PRB images, with a third model, to compress the data identifying the potential issues into an array. The device may process the array, with a fourth model, to determine probability scores associated with the potential issues, and may select a potential issue with a greatest probability score as a detected issue. The device may perform actions based on the detected issue.