5G Handover Target Selection Using Load and Success Metrics
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Solution Overview
Problem
Current 5G handover target cell selection in wireless networks primarily relies on signal quality metrics, which may not lead to optimal outcomes when multiple neighbor cells have similar signal quality, as other critical factors like target cell load and historical handover success rates are not considered.
Innovation Solution
Incorporating additional metrics such as neighbor load score and historic handover success rate into the handover target cell selection process, using a combination of signal score, success rate score, and neighbor load score to determine the best handover target cell, with configurable weight factors to prioritize based on recent and older success rates and resource load data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If handover target cell selection relies solely on signal quality metrics, then the selection process is simple and fast, but handover performance deteriorates when multiple neighbor cells have similar signal quality
Solution Approach 1:
The patent extends the handover selection criteria by introducing additional parameters beyond signal quality, specifically neighbor load score and historic handover success rate. This transforms the selection process from relying on a single parameter to evaluating multiple parameters, thereby improving handover performance in scenarios where signal quality metrics are similar across multiple cells.
Solution Approach 2:
The patent creates a composite evaluation framework that combines multiple metrics (signal score, success rate score, neighbor load score) into a unified handover decision. This composite approach allows the system to consider both signal quality and network conditions simultaneously, resolving the contradiction between simplicity and performance.
2Reliability
If additional metrics like neighbor load and historic success rate are considered, then handover reliability improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing neighbor load scores and historic handover success rates before actual handover decisions are needed. This allows the additional metrics to be readily available during handover evaluation without requiring complex real-time computations, thus improving reliability while managing processing complexity.
Solution Approach 2:
The patent implements feedback mechanisms where historic handover success rates are continuously updated based on past performance data, and neighbor load information is dynamically reflected in the selection process. This feedback loop enables the system to learn from past decisions and adapt future handover choices, improving reliability through data-driven optimizations.
3Adaptability or versatility
If configurable weight factors are introduced to prioritize metrics, then adaptability to different network conditions improves, but system configuration complexity increases
Solution Approach 1:
The patent introduces dynamic configurability through weight factors that can be adjusted based on different network conditions and operator requirements. This allows the system to adapt its behavior dynamically by modifying the relative importance of different metrics, thereby achieving versatility without requiring complete system redesign for different scenarios.
Solution Approach 2:
The configurable weight factors represent a parameter change approach where the system's evaluation priorities can be modified by adjusting numerical weights rather than changing the fundamental selection logic. This allows adaptability to different network conditions while maintaining a relatively simple and stable system architecture.
Data Source
AI summary
Improved techniques for selecting a handover target cell are described herein, for example in connection with a 5G disaggregated radio network. For example, in addition to evaluating signal quality metrics that are reported by the user equipment subject to the handover, the disclosed techniques can also evaluate additional metrics. For example, a load associated with potential target cells can be considered as neighbor load can cause handover issues. As another example, a history of success rates associated with a handover to the target cell and/or from the serving cell can be considered as well since handover failures can sometimes result due to issues that can be surfaced by the success rate history.