Dynamic Handover Thresholds for 5G Mobility and QoS Stability
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Solution Overview
Problem
Existing handover mechanisms in 5G networks face challenges due to rapidly changing radio conditions, leading to undesirable handover triggers that result in call drops, QoS issues, and inefficient serving cell coverage, particularly for mid to high mobility users.
Innovation Solution
Implementing a dynamic handover trigger service using artificial intelligence and machine learning (AI/ML) to optimize handover trigger thresholds based on uplink metrics such as SINR, packet delay, packet loss, doppler shift, end device mobility, and service type, allowing for timely and efficient handovers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed handover trigger thresholds are used, then handover decisions are simple to make, but handover performance deteriorates under rapidly changing radio conditions
Solution Approach 1:
The patent implements dynamic handover trigger thresholds that adapt to changing radio conditions and device mobility patterns. The network device continuously updates threshold values based on observed handover outcomes and current channel conditions, transforming the static threshold mechanism into a dynamic system that maintains reliability while keeping the decision process automated and manageable.
Solution Approach 2:
The system incorporates feedback mechanisms where the network device monitors handover performance metrics and uses this information to adjust future handover trigger thresholds. This closed-loop feedback enables the system to learn from past decisions and improve handover reliability without requiring complex manual intervention.
2Speed
If handover triggers are sensitive, then handover timing is timely, but excessive handovers occur causing call drops
Solution Approach 1:
The patent dynamically adjusts handover trigger parameters such as threshold values and hysteresis margins based on observed radio condition stability and device mobility. By changing these parameters adaptively, the system achieves timely handovers when needed while preventing excessive handovers that would cause call drops, thus resolving the contradiction between speed and reliability.
3Reliability
If handover triggers are conservative, then call drops are reduced, but handover timing is delayed
Solution Approach 1:
The system dynamically adjusts the conservatism level of handover triggers based on real-time conditions. When radio conditions are stable, more conservative thresholds prevent unnecessary handovers. When conditions change rapidly or device mobility increases, the system becomes less conservative to ensure timely handovers, thus dynamically balancing reliability and speed.
4Reliability
If AI/ML based dynamic thresholds are implemented, then handover performance is improved, but system complexity increases
Solution Approach 1:
The patent implements AI/ML models that operate autonomously within the network device, automatically learning optimal handover thresholds from observed data without requiring external training or manual configuration. This self-service capability improves handover performance while limiting complexity growth, as the system adapts itself rather than requiring complex external management infrastructure.
Data Source
AI summary
A method, a device, and a non-transitory computer-readable storage medium are described in relation to a dynamic handover trigger service. The dynamic handover trigger service may store optimized and adjustable levels of handover threshold information for use in triggering a handover for end devices. The dynamic handover trigger service may include measurement and calculation of uplink radio condition values, end device capabilities and mobility, and application service information for comparison to the handover threshold information and enable handover decisions.