Coverage Area Estimation Using A-Priori Statistical Data
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Solution Overview
Problem
Crowd-sourced positioning systems face challenges in accurately estimating the coverage area of communication nodes with limited sample data, leading to inconsistent and inaccurate positioning results.
Innovation Solution
The use of a-priori information, statistically combined from multiple communication nodes meeting similar criteria, to estimate the coverage area of communication nodes with few or no samples, thereby improving positioning accuracy and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If coverage area is estimated using only available sample data for each communication node, then the system requires minimal data storage and processing, but the estimation becomes inaccurate and inconsistent when sample data is limited
Solution Approach 1:
The system pre-computes and stores statistical values (mean, median, mode) of coverage areas for communication nodes grouped by criteria such as technology type, frequency band, and geographic location. These pre-computed statistical values serve as a-priori information that can be immediately applied when a communication node has limited or no sample data, eliminating the need to wait for sufficient samples to accumulate before making an accurate estimation.
Solution Approach 2:
The patent introduces statistical aggregates (mean, median, mode of coverage areas) as intermediary representations that bridge the gap between individual communication nodes with limited data and the overall population of communication nodes. These statistical values act as mediators that enable accurate coverage area estimation even when direct measurements from the specific node are insufficient, by transferring information from the broader population to the individual node.
2Measurement precision
If different a-priori information is stored for communication nodes meeting different criteria, then the system can provide more accurate estimations for diverse node types, but the system complexity and data storage requirements increase
Solution Approach 1:
The system segments the population of communication nodes into distinct groups based on multiple criteria including technology type (cellular, WLAN, Bluetooth), frequency band, geographic location, and network operator. Each segment maintains its own statistical values for coverage area estimation. This segmentation allows the system to apply appropriate a-priori information to each group while keeping the complexity manageable through structured organization of data by homogeneous categories.
Solution Approach 2:
The patent applies local quality by storing and using different a-priori statistical values for different segments of communication nodes. Instead of using a single uniform estimation approach for all nodes, the system tailors the a-priori information to match the specific characteristics of each node type and segment, thereby improving estimation accuracy for heterogeneous networks while organizing complexity through localized, category-specific data structures.
3Reliability
If the system uses a-priori information to estimate coverage area, then accuracy improves for nodes with limited samples, but the system requires additional data collection, storage, and processing infrastructure
Solution Approach 1:
The system performs preliminary computation of statistical values (mean, median, mode) for coverage areas of communication nodes grouped by various criteria and stores these pre-computed values in a database. This preliminary action shifts the computational burden from real-time processing during positioning operations to offline batch processing during data collection and model building phases, thereby improving positioning reliability without significantly increasing operational processing complexity.
Solution Approach 2:
The system implements feedback mechanisms where the accuracy and reliability of coverage area estimations using a-priori information are continuously monitored and evaluated. This feedback information is used to refine and update the statistical models and a-priori values over time, improving positioning reliability while allowing the system to learn from actual performance and adjust processing requirements accordingly.
Data Source
AI summary
A system obtains information on positions stored for a communication node and criteria that are met by the node. The system selects a-priori information on a coverage area size that is stored for nodes meeting the criteria. Different a-priori information is stored for nodes meeting different criteria. The system estimates a value of at least one parameter representing a coverage area of the node based on the information on the positions and the selected a-priori information. The value of the at least one parameter is stored. For generating the a-priori information, the system may extract from a memory information indicating a size of a coverage area for each of a plurality of communication nodes, compute a statistical value based on information indicating a size of a coverage area that is extracted for a plurality of nodes meeting the same criteria, and provide the computed statistical value as a-priori information.