Geospatial Network Analytics Engine for Micro-Level Congestion
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Solution Overview
Problem
Telecommunications networks face challenges in analyzing network performance due to the complexity of data analysis tools, which often focus on macro-level analysis, failing to address micro-level issues of network congestion and customer experience degradation, especially in specific geographic areas.
Innovation Solution
A network analytics system that incorporates geospatial mapping and area of interest information to identify locations of interest, analyze network activity data, and deploy targeted solutions to improve network performance and customer experience, including adding spectrum, cell sites, and technology capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If macro-level network performance analysis is performed, then overall network coverage and general performance are improved, but micro-level network congestion and local customer experience issues are not addressed
Solution Approach 1:
The patent divides the network analysis into multiple levels by creating area of interest (AOI) hierarchies that segment the network into macro-level regions and micro-level locations. This segmentation allows simultaneous analysis of both overall network coverage and local congestion issues at different granularities, resolving the contradiction between macro and micro analysis capabilities.
Solution Approach 2:
The patent introduces a geospatial mapping dimension to network performance analysis by overlaying network data on geographic coordinates and creating visualizations that display both macro-level coverage and micro-level details. This additional spatial dimension enables precise localization of issues while maintaining overall network perspective.
2Loss of information
If comprehensive network data is collected to gain full perspective, then complete network performance understanding is achieved, but analysis complexity and cost increase
Solution Approach 1:
The patent applies local quality by collecting and analyzing network data at different granularities depending on the specific analytical need. Rather than uniformly processing all data at the highest resolution, the system selectively processes data at appropriate levels (macro or micro) based on the analysis objective, reducing overall complexity while maintaining comprehensive understanding.
Solution Approach 2:
The patent performs preliminary data aggregation and pre-processing at macro-level AOIs before drilling down to micro-level analysis. This preliminary action organizes and structures the data in advance, reducing the complexity of subsequent detailed analysis and making comprehensive network understanding more manageable.
3Measurement precision
If detailed micro-level network analysis is performed, then local network congestion and customer experience are improved, but overall network overview and macro-level performance are lost
Solution Approach 1:
The patent implements a hierarchical segmentation system where the network is divided into parent AOIs and child AOIs at multiple levels. This segmentation structure maintains the macro-level overview through parent nodes while enabling detailed micro-level analysis through child nodes, allowing simultaneous visibility of both overall coverage and local issues.
Solution Approach 2:
The patent uses a nested AOI hierarchy where smaller micro-level areas are contained within larger macro-level regions. This nesting structure allows the system to display and analyze network performance at multiple simultaneous levels, preventing loss of macro overview while enabling detailed micro analysis through the nested structure.
Data Source
AI summary
A system including a processor configured together and analyzing network activity data associated with a plurality of network service providers, gather geographic reference data that includes information about locale(s) of interest, create bins and use spatial matching to associate network performance parameters with hex bins associated with the locale(s) of interest. Generate, based at least in part on the geographic network data, a geospatial map overlay for incorporation in an interactive graphical user interface (GUI), the geospatial map overlay comprising a visual indication of one or more areas of interest and associated aggregated network performance data, and identify and deploy one or more measures to improve network performance and thus customer experiences for at least one or more of the identified locale associated with a bin.