Population-Based Network Drive Testing for Coverage Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional drive test methodologies for 5G networks are cumbersome and resource-intensive, particularly when testing for coverage of a certain percentage of the population, as they require comprehensive grid-based testing across large areas.
Innovation Solution
A computer-implemented method for drive testing that utilizes population distribution data, such as census data, to determine geographic units, mobile test patterns, and stationary test locations, optimizing routes and locations to minimize resource use while ensuring statistically significant network performance evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional grid-based drive test methodology is used to test network coverage comprehensively, then network coverage statistics can be determined accurately, but the testing process becomes cumbersome and resource-intensive
Solution Approach 1:
The patent divides the service area into multiple geographic units (e.g., census tracts, blocks) based on population distribution data. Instead of implementing a comprehensive grid-based test pattern across the entire area, the system segments the testing scope to focus on specific units that represent the population distribution, thereby reducing testing complexity while maintaining statistical accuracy for coverage requirements.
Solution Approach 2:
The patent applies different test patterns and locations based on the characteristics of each geographic unit. By analyzing population density and distribution within each unit, the system determines optimized test patterns (mobile, stationary, or a combination) that are locally appropriate, replacing the uniform grid-based approach with differentiated testing strategies that reduce overall resource consumption.
2Reliability
If comprehensive grid-based testing is implemented to demonstrate coverage requirements, then regulatory compliance can be verified, but resource consumption and network load increase significantly
Solution Approach 1:
The patent uses population distribution data (such as census information) as a proxy model to determine test locations and patterns. Instead of physically testing every possible location to verify compliance, the system creates a representative sample of geographic units that mirrors the population distribution, thereby verifying regulatory requirements with reduced resource consumption while maintaining statistical validity.
Solution Approach 2:
The patent changes the parameters for determining test locations from a uniform grid-based approach to a population-density-based approach. By using parameters such as population density, geographic unit boundaries, and distance to population centers, the system dynamically adjusts test patterns to focus resources on areas that matter most for compliance verification, reducing overall resource consumption while maintaining reliability.
3Measurement precision
If drive testing is performed to meet coverage percentage requirements, then network performance can be evaluated, but the testing process becomes time-consuming
Solution Approach 1:
The patent performs preliminary analysis of population distribution data and geographic characteristics before actually conducting the drive tests. By pre-determining the optimal geographic units, test patterns, and locations based on population density and distribution models, the system eliminates time-consuming on-the-fly decisions during testing, thereby reducing overall testing time while maintaining performance evaluation accuracy.
Solution Approach 2:
The patent implements partial testing by selecting a representative sample of geographic units rather than testing the entire service area comprehensively. By strategically choosing geographic units that represent the population distribution (using parameters like population density and centroid distance), the system achieves sufficient statistical validity for performance evaluation with significantly reduced testing time and resource consumption.
Data Source
AI summary
Technologies for population distribution-based drive testing are disclosed. An example method includes determining geographic units for drive testing based on a proposed coverage of the communications network and a population distribution of potential users of the communications network, and for each geographic unit: determining a mobile test pattern and a set of stationary test locations for drive testing, and causing performance of drive testing in accordance with the mobile test pattern and set of stationary test locations.