Stratified User Equipment Sampling for Unbiased Analytics
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Solution Overview
Problem
The existing 3GPP networks lack a-priori knowledge about the distribution of user equipment (UE) populations, leading to non-representative sampling and biased analytics due to the use of simple random sampling techniques, which can result in skewed or incorrect statistical and analytic results.
Innovation Solution
Introduce a partition criteria attribute for event reporting information to enable stratified sampling by grouping UEs based on characteristics such as Type Allocation Code (TAC), Application ID, and UE communication information, ensuring that each sub-population receives a proportional sampling ratio, thereby selecting a representative subset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple random sampling is used to collect UE data, then the data collection process is simple and fast, but the sampling results are biased and non-representative of the actual UE population distribution
Solution Approach 1:
The patent applies segmentation by dividing the UE population into distinct sub-populations or strata based on specific criteria (e.g., device type, service type, mobility pattern). This segmentation allows each subgroup to be sampled separately, ensuring that the overall sample accurately reflects the heterogeneous composition of the total UE population, thereby resolving the bias inherent in simple random sampling.
Solution Approach 2:
The patent changes the sampling parameter from uniform random selection to stratified selection with proportional allocation. By introducing stratification variables and adjusting the sampling methodology to account for population distribution characteristics, the system transforms the sampling process to achieve representativeness while managing complexity through structured parameter definition.
2Measurement precision
If stratified sampling with partition criteria is implemented, then representative UE subsets are obtained, but the complexity of data collection and processing increases
Solution Approach 1:
The patent applies preliminary action by pre-defining partition criteria and strata classifications before the actual data collection process. The network function establishes the stratification framework in advance, including the definition of sub-population categories and their corresponding characteristics. This preliminary setup enables the sampling process to proceed systematically without requiring complex real-time decisions, thereby reducing implementation complexity while maintaining accuracy.
Data Source
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AI summary
Systems, methods, apparatuses, and computer program products that enable representative user equipment (UE) sampling for UE-related analytics services are provided.