Wireless Access Point Performance Optimization via Statistical Ranking
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Solution Overview
Problem
Current network monitoring tools require time-consuming processes to interpret large data sets from multiple access points, making it difficult for WLAN administrators to identify and address performance degradation issues proactively, leading to reactive adjustments that may already impact customer experience.
Innovation Solution
A system that uses a statistical ranking of independent variables to optimize access point performance through real-time data aggregation, predictive modeling, and proactive adjustment of controllable parameters, enabling swift identification and optimization of underperforming access points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current network monitoring tools are used to interpret large data sets from multiple access points, then comprehensive performance data can be collected, but the process becomes time-consuming and reactive rather than proactive
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing performance data from multiple access points in real-time, establishing baseline performance metrics and detecting anomalies before they become critical issues. This proactive approach allows the system to identify and address performance degradation before it impacts customer experience, rather than waiting for manual interpretation of large data sets.
Solution Approach 2:
The patent replaces the manual mechanical process of interpreting large data sets with automated electronic systems including data collection modules, analysis engines, and reporting systems. These automated systems continuously monitor performance metrics, apply analytical algorithms, and generate insights without human intervention, dramatically reducing the time required to identify performance issues while maintaining comprehensive data analysis.
2Reliability
If reactive adjustments are made to access point performance, then issues can be addressed after they occur, but customer experience is already impacted by the degradation
Solution Approach 1:
The system implements continuous feedback loops by monitoring performance data from multiple access points, analyzing trends and anomalies, and automatically generating adjustment recommendations. This real-time feedback mechanism enables the system to detect performance degradation early and initiate corrective actions before customer experience is significantly impacted, transforming reactive operations into a proactive, responsive system that maintains network reliability.
Solution Approach 2:
The patent enables self-service capabilities through automated analysis and adjustment systems that can independently identify performance issues and implement corrective measures without manual intervention. The system autonomously collects data, analyzes performance metrics, determines appropriate adjustments, and applies optimizations to access points, making the network self-managing and highly responsive to performance issues while maintaining ease of operation.
3Productivity
If traditional monitoring approaches are used, then existing tools can be leveraged, but proactive optimization and load forecasting capabilities are limited
Solution Approach 1:
The patent implements a universal system architecture that performs multiple functions including data collection, real-time analysis, performance optimization, load forecasting, and adjustment implementation across multiple access points. This multi-functional system consolidates various monitoring and optimization tasks into a single integrated platform, improving network optimization efficiency without proportionally increasing complexity, as the system handles diverse functions through unified processes and data structures.
Data Source
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AI summary
In an example, a performance of an access point in a wireless network is optimized based on a statistical ranking of independent variables. A device analyzer may calculate a dependent variable for the performance of the access point and independent variables that impact the dependent variable from a set of independent variables based on real-time access point data received from a plurality of access points. A predictive modeler may generate a model to forecast the performance of the access point and to determine an impact ranking for the independent variables from the dependent and independent variables. The impact ranking may sequence the independent variables according to their impact on the dependent variable. Accordingly, a configuration circuit may adjust a controllable parameter of the access points according to the impact ranking.