ML-Based Telecommunication Site Deployment Optimization
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Solution Overview
Problem
Current telecommunications systems face challenges in autonomously identifying and deploying solutions to improve service quality and coverage in specific geographic areas, as they lack efficient methods to analyze and prioritize precise site locations for new deployments.
Innovation Solution
A machine learning-based system that utilizes telecommunication service data, infrastructure information, and geographic data to identify new site deployment locations, filter relevant data sets, generate clusters of impaired areas, and determine optimal solution types and locations for deployment, such as tilt, low-band, lease, or small cell solutions, without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional manual methods are used to identify and prioritize site locations for new deployments, then deployment accuracy and precision can be maintained, but the process requires significant user intervention and time consumption
Solution Approach 1:
The system enables autonomous self-service by automatically analyzing telecommunication service data, generating clusters of impaired areas, identifying candidate deployment locations, and prioritizing them without human intervention. The machine learning model independently performs the entire workflow from data reception to solution recommendation, eliminating the need for manual analysis while maintaining high precision through algorithmic cluster generation and buffer area calculations.
2Area of stationary object
If comprehensive telecommunication service data is analyzed across the entire geographic area, then complete coverage of potential deployment locations is achieved, but data processing time and computational resources increase significantly
Solution Approach 1:
The system segments the geographic area into manageable clusters of impaired service areas using machine learning algorithms. By dividing the large geographic space into smaller cluster units with defined buffer areas, the system can process and analyze each segment independently, reducing overall computational complexity and processing time while maintaining comprehensive coverage of the entire service area.
Solution Approach 2:
Instead of analyzing every possible location in the entire geographic area, the system focuses computational resources on generating clusters of impaired areas and analyzing only the buffer areas surrounding these clusters. This partial action approach concentrates analysis on the most relevant regions where deployment is actually needed, significantly reducing processing time while maintaining effectiveness.
3Productivity
If buffer areas are defined for each new site deployment location to exclude overlapping regions, then deployment optimization is improved, but data set filtering complexity increases
Solution Approach 1:
The system introduces buffer areas as intermediary zones around each candidate deployment location. These buffer areas serve as mediators to automatically exclude overlapping regions and prevent redundant deployments. By using buffer areas as an intermediary filtering mechanism, the system simplifies the overall process of identifying unique deployment locations while maintaining high productivity in deployment prioritization.
Data Source
AI summary
Aspects herein provide a system, media, and methods for/of an application that utilizes and leverages machine learning techniques, infrastructure information (e.g., existing and planned cell sites, lease agreement sites, fiber-optic networks, geographic landmarks), and collected telecommunication data, to accurately identify and determine specific solutions and specific locations for those solutions to be deployed in a geographic area. In embodiments, the application autonomously identifies an optimized specific solution type for various clusters of poor-service coverages areas within the geographic areas. The application also determines a precise location for deployment of each optimized specific solution type for each cluster.