Customized Location Area Paging Algorithm
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Solution Overview
Problem
Existing wireless communication systems lack the ability to define customized location areas for mobile devices based on time, date, and duration of communication, leading to inefficient use of network resources during paging attempts.
Innovation Solution
The system monitors cell registration data, uses a predictive algorithm to determine customized location areas (CLAs) based on frequency, time, date, and duration of communication, and pages only the relevant cells associated with these CLAs, allowing for more precise and efficient location tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system pages all LAs associated with the MSC when the MS does not respond to the first attempt, then the MS can be located, but network resources (BSS and MSC resources and interfaces) are significantly consumed
Solution Approach 1:
The system performs preliminary actions by monitoring and storing cell registration data before paging is needed. It builds predictive models of MS location patterns in advance, so when paging is required, the system can immediately query the predictive database rather than performing broad searches, thereby reducing real-time network resource consumption while maintaining high paging success rates
Solution Approach 2:
The invention changes the parameter of location area definition from static operator-defined LAs to dynamic predictive CLAs based on time, date, and duration parameters. By analyzing historical registration data with these parameters, the system identifies patterns and creates time-specific customized location areas, allowing precise targeting of MS locations without paging entire MSC-wide LAs, thus reducing network resource usage
2Loss of energy
If the system defines location areas based on roaming habits and predetermined cell groupings, then the paging area is reduced, but the system cannot account for time, date, and duration variations in subscriber behavior
Solution Approach 1:
The system transitions from static location area definitions to dynamic customized location areas that adapt to time-based patterns. By continuously monitoring registration data with time, date, and duration parameters, the system generates updated CLAs that reflect changing subscriber behaviors at different times, making the paging system adaptable rather than fixed
Solution Approach 2:
The invention adds time and duration dimensions to the traditional spatial cell grouping approach. Instead of only considering which cells a subscriber frequents, the system now analyzes when and how long the subscriber remains in cells, creating multi-dimensional location patterns that capture temporal behavior variations across different days and times
3Reliability
If the system performs multiple paging attempts in bordering LAs, then the MS can be located even if initially missed, but the use of BSS and MSC resources increases significantly
Solution Approach 1:
The system performs preliminary analysis of MS location patterns and stores predictive CLAs in advance. When paging is needed, it queries the predictive database to immediately identify the most likely location area based on current time and historical patterns, eliminating the need for multiple sequential paging attempts across bordering LAs and improving resource efficiency
Data Source
AI summary
Methods for determining a customized location area (CLA) for a subscriber in a cellular network are provided herein. One such method includes the steps of monitoring the cell to which a mobile station registers; storing data corresponding to the cell within a database; determining at least one CLA based upon the data, the CLA each including at least one cell; storing said at least one CLA; utilizing a predictive algorithm to determine which CLA the mobile station is currently located within, if at least two CLAs are stored; and paging each of the cells associated with the appropriate CLA. Other methods are also provided.