Tracking Area Optimization via Dynamic Reconfiguration
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Solution Overview
Problem
Mobile networks face inefficiencies in managing tracking areas, leading to excessive paging and tracking area update signaling loads, which can be attributed to suboptimal configuration and size of tracking areas, resulting in resource wastage and performance issues.
Innovation Solution
A tracking area optimizer server is employed to select cells with high control signal loads, reconfigure tracking areas by splitting, combining, or reshaping them based on key performance indicators (KPIs) to optimize TAU and paging loads, using a processor and communication interface to manage and save reconfigured areas that meet predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tracking areas are configured with fixed size and composition, then network configuration is simple and stable, but signaling loads become excessive and resource wastage occurs
Solution Approach 1:
The patent implements dynamic tracking area optimization by periodically evaluating KPIs (paging load, TAU load, inter-TA handover rate) and automatically reconfiguring TA boundaries. The system adjusts TA composition and size based on real-time traffic patterns and user mobility, transforming static network configuration into a dynamic self-optimizing system that adapts to changing network conditions.
Solution Approach 2:
The optimization server autonomously performs TA reconfiguration without manual intervention. It automatically selects candidate TAs for optimization, evaluates their performance using KPIs, determines optimal reconfiguration strategies, and implements changes while monitoring results. This self-service capability eliminates the need for manual network operator intervention in routine optimization tasks.
2Device complexity
If tracking areas are made larger to reduce the number of TAs, then configuration complexity decreases, but paging load increases excessively
Solution Approach 1:
The patent segments large tracking areas into smaller sub-TAs based on user distribution patterns and mobility characteristics. By dividing oversized TAs into multiple smaller units, the system reduces paging load within each TA while maintaining manageable configuration complexity. This segmentation allows targeted optimization of high-traffic areas without unnecessarily fragmenting the entire network.
3Quantity of substance
If tracking areas are made smaller to reduce paging load, then paging efficiency improves, but the number of TAs increases and configuration complexity increases
Solution Approach 1:
The patent merges adjacent small TAs into larger optimized TAs when traffic patterns and user mobility indicate it would be beneficial. By dynamically combining small TAs based on real-time KPI evaluation, the system reduces the total number of TAs and configuration complexity while maintaining low paging loads. This merging capability allows the network to adapt to changing traffic conditions without manual reconfiguration.
4Manufacturing precision
If manual optimization of tracking areas is performed, then configuration can be precisely controlled, but optimization speed decreases and cannot keep pace with traffic changes
Solution Approach 1:
The patent implements continuous feedback loops where the optimization server periodically collects KPI data (paging load, TAU load, handover rates), evaluates current TA configurations, and automatically adjusts boundaries based on performance metrics. This closed-loop feedback system enables precise optimization control while maintaining fast response to traffic changes, eliminating the speed limitation of manual optimization.
5Productivity
If tracking area boundaries are frequently adjusted to optimize performance, then network efficiency improves, but network stability decreases and may cause signaling storms
Solution Approach 1:
The patent implements periodic optimization cycles where the system evaluates TA configurations at scheduled intervals rather than continuously reacting to every traffic fluctuation. By using threshold-based triggers and time-dependent evaluation, the system maintains TA stability during normal operations while periodically seeking optimization opportunities, preventing excessive reconfiguration and potential signaling storms.
Data Source
AI summary
In one embodiment, a method includes: selecting, based on control signal data, a cell to optimize in a tracking area (TA) of a mobile network, wherein the cell is associated with a control signal load that exceeds an associated control signal load threshold, wherein the load is determined according to control signal data and includes tracking area update load and/or paging load; reconfiguring the TA by: splitting the TA, combining the TA and another TA, adding another cell from another TA, and/or removing another cell; receiving, based on the reconfigured TA, updated control signal data associated with both the load and a second control signal load for cells affected by the reconfigured TA; and when the load is under the associated threshold and the second load is under an associated second control signal load threshold for the affected cells, saving the reconfigured TA for continued use in the network.