Automated Wireless Network Area Identification via Graph Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for planning cellular network infrastructure are labor-intensive and time-consuming, requiring manual analysis of various data sets to identify optimal locations for equipment placement, which can lead to sub-optimal coverage and increased costs for telecom carriers.
Innovation Solution
A three-stage algorithm that involves data aggregation, binning, weighting, and graph analysis to automatically identify geographic clusters of interest based on metrics such as signal strength and population density, allowing for more efficient resource allocation and network planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to identify optimal equipment locations, then network planners can analyze various data sets, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-based system that uses graph algorithms and data processing to identify geographic areas of interest. The system automatically processes network performance data, geo-spatial data, and census data to generate area identifiers without human intervention in the analysis process.
Solution Approach 2:
The system enables self-service by allowing network planners to input data sets and automatically receive identified areas of interest through the automated processing system. The computer-based apparatus performs the entire analysis workflow independently, from data ingestion to area identification and output generation.
2Reliability
If manual analysis methods are used to identify optimal equipment locations, then network planners can evaluate coverage and interference, but the process becomes labor-intensive
Solution Approach 1:
The patent replaces complex manual evaluation processes with automated computer-based analysis that systematically assesses network coverage and interference patterns. The graph-based algorithm objectively evaluates multiple data sets simultaneously, eliminating subjectivity and reducing operational complexity while improving reliability.
Solution Approach 2:
The system introduces an intermediary automated processing layer between raw data and decision-making. The computer-based apparatus acts as a mediator that transforms multiple complex data sets into simplified area identifiers and recommendations, making the analysis process easier to operate while maintaining high reliability.
3Productivity
If automated algorithms are used to identify geographic clusters, then the process becomes more efficient, but the system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct processing stages: data ingestion, graph construction, algorithmic processing, and result generation. By dividing the workflow into modular components, the system achieves high productivity while managing complexity through structured organization of the automated process.
Solution Approach 2:
The computer-based apparatus performs multiple functions within a single integrated system: data processing, graph algorithm execution, area identification, and output generation. This multi-functionality increases productivity by consolidating operations while the modular architecture manages the inherent system complexity.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, identifying geographic clusters that are similar based on metrics and geo-spatial association. Embodiments of the disclosure are directed to operations that include obtaining data corresponding to a communication network, applying a first algorithm to the data to generate a plurality of bins, generating a respective score for each bin of the plurality of bins, applying a second algorithm, based on the respective scores, to generate a plurality of clusters, and generating a graph for each cluster of the plurality of clusters, wherein vertices of each graph are represented by the bins of the cluster, and wherein edges of each graph connect the bins of the cluster to adjacent bins of the cluster. Other embodiments are disclosed.