Base Grid KPI Mapping for Real-Time Network Coverage Analysis
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Solution Overview
Problem
Traditional network planning methods rely on static assumptions and historical data, failing to adapt to dynamic changes in network requirements, leading to suboptimal designs and inefficiencies in network infrastructure scaling, resource allocation, and user needs.
Innovation Solution
A system and method for determining the operative status of a base grid by analyzing data samples from user equipment, grouping them by IMSI and map levels, and aggregating RF parameters to generate key performance indicators (KPIs) for network analysis, enabling real-time monitoring and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional network planning methods use static assumptions and historical data, then network design simplicity is maintained, but adaptability to dynamic network requirements deteriorates
Solution Approach 1:
The patent implements dynamic network planning by continuously collecting real-time data from user equipment and automatically updating base grid configurations. The system transitions from static historical data to dynamic real-time data processing, enabling the network to adapt to changing requirements through automated algorithms that adjust network parameters based on current conditions.
Solution Approach 2:
The system establishes a feedback loop where network performance data is continuously collected from user equipment, analyzed against operational criteria, and used to automatically adjust network configurations. This closed-loop feedback mechanism enables the network to learn from actual performance and dynamically optimize itself without manual intervention.
2Measurement precision
If traditional network planning relies on coarse-grained spatial models, then analysis scope is expanded, but measurement precision of localized variations deteriorates
Solution Approach 1:
The patent divides the network coverage area into discrete base grids of varying sizes. Each base grid is further segmented into smaller units for detailed analysis. This hierarchical segmentation allows the system to maintain broad geographic coverage while simultaneously achieving high measurement precision within each localized grid through granular data collection and analysis.
Solution Approach 2:
The system applies different levels of analysis granularity to different geographic regions based on network density and importance. Urban areas with high user density receive finer-grained analysis with smaller base grids, while rural areas use coarser grids. This local quality approach optimizes measurement precision where needed while maintaining overall coverage efficiency.
3Measurement precision
If real-time data processing from multiple user equipments is implemented, then network monitoring accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent combines data from multiple user equipments within each base grid to generate aggregated network status indicators. By merging individual device measurements into collective grid-level metrics, the system achieves high monitoring accuracy through ensemble data while reducing processing complexity through data aggregation and consolidation at the grid level.
Solution Approach 2:
User equipment automatically provides network measurement data without requiring manual collection. The system leverages the inherent capabilities of user devices to self-generate network status information, reducing the burden on centralized processing systems. This self-service data collection mechanism improves monitoring accuracy while minimizing additional processing complexity.
4Adaptability or versatility
If network infrastructure is designed for future scalability, then adaptability to growth is improved, but initial device complexity increases
Solution Approach 1:
The patent implements a modular base grid architecture where the network is divided into independent, standardized grid units. Each base grid is designed as a self-contained module that can be easily replicated and combined. This segmentation enables straightforward scalability - new grids can be added to accommodate growth without redesigning the entire network, reducing initial design complexity while maintaining future scalability.
Solution Approach 2:
The base grid design incorporates universal interfaces and standardized configurations that can serve multiple functions and scale across different network densities. The same grid structure can accommodate varying numbers of user equipments and support different service types, providing a multi-functional platform that simplifies initial design while enabling future expansion without increasing complexity.
Data Source
AI summary
The present disclosure provides a system (108) and a method (300) for base grid creation for analyzing geographical locations The system (108) generates a base grid where an aggregation of data, specifically on international mobile subscriber identity (IMSI) levels provides a real time health status of each IMSI within the base grid. The system (108) generates IMSI level identification and plotting where a real time issue with a specific user may be identified. The system (108) summarizes data at different levels of aggregation and reduces a number of data points to be processed and displayed. The system (108) improves performance and scalability, allowing users to analyze and visualize large volumes of data efficiently.