Wireless Cell Location Correction Using Network Usage Data
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Solution Overview
Problem
Existing datasets mapping the locations of wireless equipment in cellular networks, such as cell towers, often contain inaccuracies or missing data due to issues like data write-in errors, software bugs, and hardware positioning, which hinder the ability to determine accurate user demographics and network infrastructure decisions.
Innovation Solution
Utilizing machine learning models trained on network usage data from multiple users to identify incorrect or missing locations of wireless cells, and generate accurate estimates by analyzing sequences of cell connections and user movements, thereby updating the dataset with corrected location information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing datasets mapping wireless equipment locations are used, then data availability is maintained, but location accuracy deteriorates due to write-in errors, software bugs, and hardware positioning issues
Solution Approach 1:
The system uses machine learning models to analyze network usage data and provide feedback on the accuracy of location information in existing datasets. The models identify incorrect or missing locations by comparing expected connection patterns against actual data, then correct these errors iteratively to improve overall location accuracy while maintaining data completeness
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw network usage data and location datasets. These models process and interpret the data to identify and correct location errors, acting as a mediator that transforms inaccurate raw data into reliable location information without losing completeness
2Measurement precision
If machine learning models are used to identify and correct location errors, then location accuracy improves, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically identifying and correcting its own location errors using machine learning models trained on network usage data. The models autonomously detect incorrect locations in the dataset and generate corrections without requiring manual intervention, thereby improving accuracy while keeping the complexity manageable through automation
Solution Approach 2:
The patent changes the parameter of location accuracy by applying machine learning transformations to the location data. The models adjust location parameters based on patterns in network usage data, transforming inaccurate location information into accurate data while managing system complexity through algorithmic processing
Data Source
AI summary
A method includes receiving first data indicative of network usage of multiple users of a wireless network and receiving second data indicative of one or more locations of a set of wireless cells. The first data includes information representing sequences of wireless cells that are connected to by user devices of the multiple users. The method also includes identifying, using one or more machine learning models, a portion of the one or more locations that are likely to be incorrect based on the first data and the second data. The method also includes generating estimates of a revised location for each wireless cell corresponding to the identified portion of the one or more locations that are likely to be incorrect, wherein the estimates are generated by one or more additional machine learning models. The method also includes updating the second data to include the generated estimates.