Hierarchical Network Performance Assessment Using Weighted KPIs
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Solution Overview
Problem
Existing wireless telecommunication networks face challenges in determining performance across complex geographic areas, leading to inefficiencies in service delivery and user satisfaction, particularly due to variations in signal strength, coverage, and speed.
Innovation Solution
A system and method that hierarchically subdivides geographic areas into units, clusters, regions, and larger areas, combining key performance indices (KPIs) weighted by population and visitor data to calculate a network score, which also considers user experience and competitor network performance to identify necessary upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the network performance assessment covers the entire geographic area uniformly, then the coverage is comprehensive, but the assessment accuracy is reduced due to varying population densities and network conditions in different regions
Solution Approach 1:
The geographic area is divided into multiple hierarchical levels (units, clusters, regions) with different granularities. Each level is assessed separately using appropriate weighting factors, allowing precise local assessment while maintaining comprehensive coverage. Units with higher population density receive greater weighting in the overall assessment.
2Reliability
If multiple key performance indices are collected from all units, then the assessment comprehensiveness is improved, but the data processing complexity increases significantly
Solution Approach 1:
The data collection and processing is organized hierarchically across multiple levels (units, clusters, regions). Each level processes its own KPIs independently using standardized weighting factors, reducing overall computational complexity while maintaining comprehensive assessment.
Solution Approach 2:
Different weighting factors are applied to KPIs based on the specific characteristics of each unit, cluster, and region. Population density, visitor data, and local network conditions are considered to assign appropriate weights, ensuring that each level's assessment reflects its unique characteristics rather than applying a uniform approach.
3Measurement precision
If population and visitor data are used to weight KPIs, then the user-centric performance measurement is improved, but the data collection requirements and system complexity increase
Solution Approach 1:
Population and visitor data are used to assign location-specific weighting factors to KPIs at each hierarchical level. Units with higher population density or visitor traffic receive greater weighting, ensuring that network performance assessment reflects actual user experience and demand patterns in different geographic areas.
Data Source
AI summary
The disclosed system defines a hierarchical subdivision of a geographic area including a unit, a cluster, a region, and an area, where the area includes multiple regions, the region includes multiple clusters, and the cluster includes multiple units. The system obtains KPIs associated with a unit and obtains a network score for the cluster by combining each KPI associated with each unit in the cluster. The system obtains a competitor network score associated with a competing network. Based on the competitor network score and the network score, the system can determine whether the cluster, the region, and the area are performing satisfactorily.