Cell Capacity Saturation Forecasting Using Abnormal Pattern Detection
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Solution Overview
Problem
Current methods for forecasting cell capacity saturation in wireless communication systems are inaccurate due to their reliance on uniform rules, failing to account for abnormal patterns caused by specific events within cells.
Innovation Solution
The implementation of an AI module that detects and categorizes abnormal patterns in cell resource utilization data to forecast cell saturation, enabling informed decisions on deploying new cells based on increasing or decreasing patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform rules are used for forecasting cell capacity saturation, then the forecasting process is simple, but the accuracy of cell saturation forecast deteriorates due to inability to account for abnormal patterns
Solution Approach 1:
The patent segments the cell resource utilization data into different patterns (normal patterns and abnormal patterns) based on specific events. By dividing the data into distinct categories, the system can apply different forecasting approaches for each pattern type, thereby improving forecast accuracy while maintaining manageable complexity through systematic classification.
Solution Approach 2:
The patent applies different forecasting rules and parameters depending on the local characteristics of each data pattern. For abnormal patterns caused by specific events, the system uses event-specific forecasting parameters, while for normal patterns, it uses standard parameters. This localized approach ensures that each pattern is forecasted with appropriate precision without requiring complete redesign of the entire forecasting system.
2Measurement precision
If AI module with abnormal pattern detection is implemented, then the cell saturation forecast accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent performs preliminary classification of cell resource utilization data into different patterns before applying forecasting algorithms. By pre-identifying abnormal patterns and their associated events, the system prepares the data in advance for more accurate forecasting. This preliminary action reduces the complexity of the main forecasting process by organizing data into manageable categories beforehand.
Solution Approach 2:
The patent introduces an intermediary classification layer between data collection and forecasting that identifies and categorizes abnormal patterns. This intermediary component acts as a mediator that translates raw complex data into structured pattern categories, making the subsequent forecasting process more accurate and manageable without requiring the entire system to be overly complex.
3Measurement precision
If per-cell resource utilization monitoring is performed, then the management precision of base stations is improved, but the loss of time and computational resources increases
Solution Approach 1:
The patent extracts and focuses monitoring efforts on key performance indicators and specific abnormal patterns rather than analyzing all possible parameters continuously. By taking out only the most relevant data elements for forecasting purposes, the system maintains high management precision while reducing the time and computational resources required for processing.
Solution Approach 2:
The patent applies monitoring and analysis at appropriate levels of detail - using full precision only when abnormal patterns are detected, while using simplified monitoring for normal conditions. This partial action approach ensures that computational resources are concentrated where they provide the most value, balancing precision requirements with resource constraints.
Data Source
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AI summary
The present disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). A method and an apparatus for forecasting capacity saturations of cells in a wireless communication system based on per-cell resource utilizations and determining whether to deploy a new cell based on a forecast result are provided. The method includes acquiring first data related to a resource utilization of a cell from the cell, deducing a certain pattern corresponding to a predetermined time period from the first data, acquiring second data by converting the first data based on a shape of an abnormal pattern corresponding to the certain pattern among multiple preconfigured abnormal patterns, and forecasting whether the cell capacity is saturated based on the second data.