Access Point Handover Parameter Self-Optimization
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Solution Overview
Problem
Wireless communication networks experience handover failures such as radio link failures and call drops due to sub-optimal handover parameter settings, leading to inefficient resource utilization and degraded user experience.
Innovation Solution
Access points in the network are equipped with self-optimization functions to automatically detect and adapt handover parameter settings, including time-to-trigger and Cell Individual Offsets, to prevent handover-related failures and reduce unnecessary handovers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If handover parameters are manually configured, then device complexity is reduced, but handover reliability deteriorates due to sub-optimal settings
Solution Approach 1:
The system implements self-service through automatic handover parameter optimization where the network autonomously detects handover failures, analyzes causes, and adjusts parameters without manual intervention. This resolves the contradiction by enabling the system to self-optimize for reliability while maintaining operational simplicity.
Solution Approach 2:
The patent implements feedback mechanisms where handover failure information is collected from UEs and network nodes, analyzed to identify optimization opportunities, and used to automatically adjust handover parameters. This closed-loop feedback system improves reliability while keeping the system manageable through automated decision-making.
2Reliability
If handover parameters are automatically adapted, then handover reliability improves, but device complexity increases due to self-optimization functions
Solution Approach 1:
Access points are equipped with self-service capabilities to autonomously detect handover failures, analyze failure causes, and adjust their own handover parameters. This distributes the optimization intelligence across network nodes rather than centralizing it, improving reliability while managing complexity through localized decision-making.
Solution Approach 2:
The self-optimization function is segmented into modular components: failure detection, cause analysis, and parameter adjustment. This segmentation allows each component to be independently implemented and optimized, reducing overall system complexity while achieving improved handover reliability through coordinated operation of the segments.
3Reliability
If handover failures are detected and parameters adapted, then handover reliability improves, but network resource utilization deteriorates due to unnecessary handovers
Solution Approach 1:
The system uses feedback from handover failure detection to learn and adapt parameters that prevent both failures and unnecessary handovers. By analyzing patterns in failure data, the system optimizes parameters to achieve reliable handovers only when necessary, improving call drop reduction while maintaining network resource efficiency through data-driven decision-making.
Data Source
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AI summary
Handover parameter settings are automatically adapted in access points in a system to improve handover performance. Reactive detection techniques are employed for identifying different types of handover-related failures and adapting handover parameters based on this detection. Messaging schemes are also employed for providing handover-related information to access points. Proactive detection techniques also may be used for identifying conditions that may lead to handover-related failures and then adapting handover parameters in an attempt to prevent such handover-related failures. Ping-ponging may be mitigated by adapting handover parameters based on analysis of access terminal visited cell history acquired by access points in the system. In addition, configurable parameters (e.g., timer values) may be used to detect handover-related failures.