Cell Utilization Identification via KPI Consistency and GUI Mapping
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Solution Overview
Problem
Current cellular network monitoring and optimization systems lack efficiency in processing and reporting network traffic and key performance indicator (KPI) data, leading to delayed data processing and inadequate visualization, making it difficult for network operators to identify highly utilized cells in a timely and visually effective manner.
Innovation Solution
A method and system that processes KPI data daily to identify highly utilized cells by receiving and analyzing multiple KPIs with defined criteria, determining consistency over a specified period, and displaying the results on a graphical user interface (GUI) map, allowing for real-time visualization and selection of cells with high or above-average network utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current monitoring systems process and report cellular network KPI data using traditional methods, then data processing can be performed, but the processing time takes up to a day or longer and reports are placed in queue awaiting resources
Solution Approach 1:
The system performs preliminary processing of KPI data by continuously collecting and pre-analyzing network performance metrics before they need to be reported. This allows the system to have processed data ready for immediate visualization and reporting, eliminating the queueing delay described in the background where reports wait for resource availability.
Solution Approach 2:
The patent introduces an intermediary processing layer between data collection and final reporting that continuously maintains processed KPI data in a ready state. This intermediary system pre-processes information so that when reporting is needed, the data is already prepared and can be immediately visualized on the map interface without waiting for resource availability.
2Ease of operation
If traditional reporting systems are used to present cellular network data, then data can be reported, but the presentation lacks streamlined visualization and efficiency
Solution Approach 1:
The system uses color-coded visual indicators on the map to represent different levels of network utilization. Highly utilized cells are displayed with distinct visual markers and colors, making it immediately apparent which cells require attention. This visual encoding transforms raw KPI data into intuitive geographic information that can be quickly interpreted by network operators.
Solution Approach 2:
The patent transforms traditional tabular or textual KPI reports into a spatial dimension by displaying network cell utilization on a geographic map. This dimensional transformation allows operators to visually perceive network performance patterns across geographic regions, making it easier to identify highly utilized cells and understand network distribution patterns.
3Reliability
If manual network optimization is performed based on periodically retrieved KPI data, then network parameters can be modified, but the process lacks real-time identification of highly utilized cells
Solution Approach 1:
The system implements continuous feedback by monitoring KPI data in real-time and immediately identifying when cells become highly utilized. This feedback mechanism provides network operators with current information about network performance, enabling them to make timely optimization decisions rather than relying on periodic manual reviews of outdated data.
Solution Approach 2:
The patent replaces manual analysis of KPI data with an automated system that continuously processes network performance metrics and automatically identifies highly utilized cells. This substitution of manual mechanical processes with automated computational analysis dramatically increases the speed of cell identification while maintaining or improving the reliability of optimization decisions.
Data Source
AI summary
A method, system, apparatus, and computer-readable medium for identifying highly utilized cells within a network based on one or more key performance indicators. The method can comprise receiving first key performance indicator (KPI) data with respect to a cell within the network, receiving a first criteria with respect to the first KPI data, and determining if the first KPI data meets the first criteria. The method can also comprise upon determining that the first KPI meets the first criteria, determining if the first KPI data is consistent over a defined period, and upon determining that the first KPI data is consistent, designating the cell as a cell that includes high network utilization or above average network utilization.