Wireless Network KQI-KPI Relationship Analysis for Autonomous QoS Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Network operators face challenges in autonomously diagnosing and resolving quality of service problems in wireless networks due to the lack of readily available subject matter experts, leading to variability in troubleshooting results based on individual skill and experience.
Innovation Solution
A method and system for determining time series relationships between key quality indicators (KQIs) and key performance indicators (KPIs) in wireless networks, which involves identifying discrete time intervals where KQI fails to meet quality criteria, calculating relationship indicators, and adjusting configuration parameters to improve higher-ranked KPIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subject matter experts are retained to analyze network diagnostic information, then quality of service problems can be identified and resolved, but the results vary based on individual skill and experience and experts are not always readily available
Solution Approach 1:
The system enables autonomous self-service by automatically collecting KQI and KPI data, determining relationships between indicators, identifying quality problems, and adjusting configuration parameters without requiring external expert intervention. The network management system performs diagnostic and optimization functions that previously required subject matter experts.
Solution Approach 2:
The patent replaces the mechanical system of human expert analysis with an automated computational system that uses algorithms to determine relationships between KQI and KPI indicators, identify quality problems, and adjust network parameters. This substitution eliminates variability based on individual expert skill and experience.
2Extent of automation
If autonomous techniques are used to diagnose quality of service problems, then expert availability and skill variability are eliminated, but the complexity of determining relationships between multiple indicators increases
Solution Approach 1:
The system segments the complex diagnostic process into distinct functional modules: data collection module that gathers KQI and KPI indicators, relationship determination module that analyzes correlations, problem identification module that detects quality issues, and parameter adjustment module that optimizes configuration. This segmentation manages complexity by handling each aspect separately.
Solution Approach 2:
The patent introduces relationship indicators as intermediary metrics that simplify the analysis between multiple KQI and KPI indicators. These relationship indicators serve as mediators that capture complex relationships in a standardized form, making the automated determination process more manageable and interpretable.
3Productivity
If configuration parameters are adjusted based on relationship indicators, then network performance can be optimized, but the precision of relationship measurement must be sufficiently accurate to guide effective adjustments
Solution Approach 1:
The system implements feedback by continuously monitoring KQI and KPI indicators, determining relationships between them, adjusting configuration parameters based on these relationships, and then re-evaluating the indicators to verify improvement. This closed-loop feedback ensures that parameter adjustments are guided by accurate relationship measurements and leads to measurable performance optimization.
Solution Approach 2:
The patent systematically changes configuration parameters based on determined relationship indicators between KQI and KPIs. By identifying which parameters have the strongest relationships with quality indicators, the system makes targeted parameter adjustments that maximize network performance improvement while ensuring measurement precision guides effective changes.
Data Source
AI summary
Quality of service problems in wireless networks can be identified, and in some cases resolved, autonomously using an optimization technique that adjusts parameters of the wireless network based on relationships between key quality indicators (KQIs) and key performance indicators (KPIs). A KQI may be used to gauge the quality of service/experience collectively observed by users/devices when communicating a particular type of traffic in a wireless network or wireless network area. A KPI may be any specific performance metric of a wireless network tending to have a causal or correlative relationship with a KQI. Different types of relationships between a KQI and KPIs may be used to evaluate a quality of service problem, including correlation coefficients, slopes of linear regression, hit-ratios, and others.