Mobile Network Handover Drop Prediction Mechanism
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Solution Overview
Problem
Existing mobile network handover procedures face challenges in predicting and preventing session drops due to uncertainties in radio signal quality and interference, leading to inefficient handover decisions and user experience issues.
Innovation Solution
Implementing a drop prediction mechanism using a Machine Learning algorithm within a network node to estimate the likelihood of session drops, allowing for dynamic adjustment of handover thresholds based on real-time radio conditions and providing drop prediction reports to optimize handover decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional handover threshold-based decisions are used, then handover procedures are simple to implement, but session drops occur due to uncertainties in radio signal quality and interference
Solution Approach 1:
The system performs preliminary actions by requesting and processing drop prediction reports before making handover decisions. The network node obtains predictions about potential session drops and uses this information in advance to proactively initiate handover procedures, preventing session drops before they occur rather than reacting after problems arise.
Solution Approach 2:
A drop prediction report serves as an intermediary element between the traditional threshold-based handover mechanism and the actual handover decision. This intermediary provides additional predictive information about session drop risks, enabling more informed decisions that balance reliability improvements with controlled complexity through structured information flow.
2Measurement precision
If handover thresholds are adjusted dynamically based on real-time conditions, then handover decisions become more accurate, but the complexity of threshold management increases
Solution Approach 1:
The system implements feedback by continuously monitoring real-time radio conditions and drop prediction outcomes, then using this information to dynamically adjust handover thresholds. The network node adapts thresholds based on observed patterns and prediction accuracy, creating a closed-loop system that improves measurement precision while managing operational complexity through automated adaptation rather than manual configuration.
Solution Approach 2:
Handover thresholds transition from static pre-configured values to dynamic parameters that automatically adapt to changing radio conditions and prediction results. The system employs dynamics by allowing thresholds to vary over time based on real-time inputs, improving accuracy while reducing operational burden through self-adjusting mechanisms.
3Loss of information
If more measurement reports are requested from communication devices, then handover decisions are better informed, but network signaling overhead increases
Solution Approach 1:
The system applies partial action by selectively requesting measurement reports only when necessary, rather than continuously or universally. Drop prediction reports are requested based on specific conditions and risk assessments, obtaining sufficient information to make informed handover decisions without the excessive overhead of universal continuous reporting, thus balancing information completeness with resource conservation.
Data Source
AI summary
A method for performing handover decisions for established radio connections in a mobile network is provided where the method comprise: requesting (6:10) a drop prediction report from a communication device, indicating whether or not an established radio connection is predicted to drop; receiving (6:20) a requested drop prediction report associated with the radio connection; determining (6:40), based on the content of the report, whether a first condition has been fulfilled; configuring a first threshold value specifying when a measurement report is to be provided from the communication device using the radio connection for which the drop prediction report has been received, in case the first condition has been fulfilled, and transmitting (6:60) the configured threshold value or an indication of the configured threshold value to the communication device.