Network Graph Analysis for Telecommunications Deployment
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Solution Overview
Problem
Telecommunications networks often face inefficiencies due to ad hoc deployment of base stations as demand increases, leading to congestion and incomplete data regarding network connections, which can result in inefficient resource allocation and connectivity issues.
Innovation Solution
A network analysis platform that processes incomplete geospatial coordinate data to generate a network graph representation, prioritizes candidate segments and sites, and provides recommendations for efficient deployment and optimization of network resources, including the identification of missing connections and optimal site and segment placement using artificial intelligence and graph optimization algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If base stations are deployed ad hoc to meet increasing demand, then network capacity is improved, but network congestion and connectivity issues worsen
Solution Approach 1:
The system performs preliminary analysis and planning before actual network deployment by generating a network graph representation from incomplete geospatial data, identifying optimal segment traversal priorities, and providing deployment recommendations in advance to prevent congestion and connectivity issues before they occur
Solution Approach 2:
The system continuously analyzes network data including incomplete geospatial coordinate data, determines prioritization of candidate segments based on calculated scores, and provides feedback recommendations for optimal segment and site selection to improve network capacity while maintaining connectivity reliability
2Speed
If ad hoc deployment is used to quickly expand network coverage, then deployment speed is improved, but resource allocation efficiency worsens
Solution Approach 1:
The system performs preliminary optimization calculations by determining candidate site scores based on quantity of candidate segments to hub, determining candidate segment scores based on quantity of candidate sites connecting to hub, and establishing traversal priorities before deployment begins, enabling efficient resource allocation from the start
Solution Approach 2:
The system changes the approach from random ad hoc deployment to systematic deployment by introducing scored prioritization parameters for segments and sites, using the network graph representation to objectively determine deployment sequence and resource allocation based on calculated metrics rather than arbitrary decisions
3Loss of information
If complete network connection data is collected before deployment planning, then data completeness is improved, but deployment time increases
Solution Approach 1:
The system performs preliminary processing on incomplete geospatial coordinate data to generate a functional network graph representation without waiting for complete data, enabling deployment planning to proceed with available information while continuously updating as more data becomes available
Solution Approach 2:
The system accepts and processes partial data (incomplete geospatial coordinate data) rather than waiting for complete data, determining prioritization and providing recommendations based on the available subset of information, which is sufficient to guide initial deployment decisions
Data Source
AI summary
A device may obtain incomplete geospatial coordinate data associated with a telecommunications network. The device may generate a network graph representation of the telecommunications network based on the incomplete geospatial coordinate data. The device may determine, for candidate sites, a candidate site score based on a quantity of candidate segments to connect the candidate site to a candidate hub. The device may determine, for candidate segments, a candidate segment score based on a quantity of candidate sites that connect to a candidate hub via the candidate segment. The device may determine a prioritization of the candidate segments based on the candidate site scores and the candidate segment scores. The device may generate a recommendation for selecting or ordering the candidate segments. The device may provide the recommendation for display via a user interface.