WLAN Color Map CNN Application Performance Prediction
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Solution Overview
Problem
Current methods for managing wireless networks struggle with real-time troubleshooting and predicting application performance issues, particularly in identifying the root causes of poor Quality of Experience (QoE) for mobile users, due to the complexity of networking architectures and limited tools for holistic analysis.
Innovation Solution
A system and method that gather and analyze WLAN data, including RF, transmission, and QoS data, to generate color maps and apply convolutional neural network processing for predicting application scores, identifying operational characteristics contributing to performance issues, and re-training statistical models to improve network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network management methods are used, then device complexity is reduced, but measurement precision and troubleshooting capability deteriorate
Solution Approach 1:
The patent introduces color maps as an intermediary visualization tool that translates complex network data into intuitive visual representations. These color maps serve as a mediator between raw network metrics and human analysis, enabling precise troubleshooting without requiring direct interpretation of complex raw data. The visual intermediary layer preserves measurement precision while abstracting away system complexity.
Solution Approach 2:
The patent replaces traditional manual troubleshooting methods with automated convolutional neural network processing. The CNN automatically analyzes color map patterns and identifies performance issues, substituting human mechanical analysis with intelligent automated systems. This substitution enhances measurement precision through consistent algorithmic analysis while reducing the operational complexity burden on network administrators.
2Measurement precision
If comprehensive WLAN data collection is implemented, then prediction accuracy improves, but loss of time and processing overhead increase
Solution Approach 1:
The patent implements preliminary action by continuously collecting and preprocessing WLAN data in the background before actual prediction events occur. Historical data is gathered and organized into color maps in advance, so when prediction is needed, the processed visual representations are already ready for immediate CNN analysis. This preliminary data preparation maintains high prediction accuracy while minimizing real-time processing delays.
Solution Approach 2:
The patent substitutes time-consuming manual data analysis with automated CNN processing of pre-generated color maps. The convolutional neural network rapidly analyzes the visual patterns in color maps to predict application scores, replacing slow traditional analytical methods with fast intelligent processing. This substitution maintains measurement precision while dramatically reducing the time loss associated with data processing.
3Productivity
If real-time analysis is implemented, then productivity improves, but use of energy and computational resources increase
Solution Approach 1:
The patent segments the troubleshooting process into distinct phases: data collection, color map generation, and CNN analysis. By dividing the workflow into discrete segments, the system can perform intensive computational tasks (color map generation and CNN analysis) only when necessary, rather than continuously. This segmentation maintains high troubleshooting productivity while reducing overall energy consumption by avoiding constant full-system processing.
Solution Approach 2:
The patent replaces energy-intensive traditional analysis methods with optimized CNN processing that operates on compact color map representations. Instead of analyzing raw high-volume network data, the system processes condensed visual summaries, significantly reducing computational energy requirements. The substitution maintains productivity through intelligent automated analysis while lowering the energy burden compared to conventional real-time processing approaches.
Data Source
AI summary
An example method is provided in one example embodiment and may include gathering current wireless local area network (WLAN) data for a WLAN, wherein the WLAN data comprises network data, Radio Frequency (RF) data, and transmission data for a plurality of user equipment (UE) operating within the WLAN; generating a plurality of color maps; merging the plurality of color maps to generate a combined color map; and calculating a predicted application score for at least one UE operating within the WLAN based, at least in part, on application of the combined color map to a trained statistical model that represents linking relationships between the WLAN data gathered for the WLAN and a plurality of possible application scores for the plurality of UE. The plurality of color maps can include an RF color map, a transmission color map, and a Quality of Service color map.