Conditional Handover Using Predicted Future QoS
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Solution Overview
Problem
Current stochastic channel models in radio communication, particularly in cellular handover technology, fail to accurately predict Quality of Service (QoS) variations over time and space, leading to service interruptions and management overhead due to unpredictable channel degradation during handovers.
Innovation Solution
A method that involves receiving conditional handover execution conditions, determining future QoS using machine-trained models, and evaluating handover conditions to anticipate and prepare for potential handovers, thereby reducing service interruptions and management overhead by transmitting pre-trigger indications and reevaluation requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If periodic channel monitoring is performed to assess channel quality, then channel quality assessment accuracy is improved, but service interruption time increases
Solution Approach 1:
The system performs preliminary channel quality assessment and predicts future QoS conditions before handover is actually needed. By using machine-trained models to forecast channel degradation, the system prepares handover conditions in advance, allowing the handover to be executed smoothly without service interruption when the predicted degradation occurs.
2Loss of time
If conditional handover execution conditions are set to reduce handover actions, then service interruptions are reduced, but handover decision accuracy deteriorates
Solution Approach 1:
The system uses feedback from machine-trained models that continuously learn from actual channel behavior patterns. The predicted future QoS values are compared with actual measurements, and the model parameters are adjusted accordingly. This feedback mechanism ensures that handover decisions remain accurate even when using predictive conditions, as the model adapts to real-world channel characteristics.
3Reliability
If multiple candidate cells are prepared for handover to increase robustness, then handover reliability is improved, but resource allocation overhead increases
Solution Approach 1:
Instead of uniformly preparing multiple candidate cells for all handover scenarios, the system uses machine-trained models to predict which specific candidate cells are most likely to be suitable based on local channel conditions and historical patterns. Resources are allocated selectively to the most promising candidates identified by the prediction model, rather than preparing all possible candidates, thus reducing overhead while maintaining reliability.
Data Source
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AI summary
There is provided a method for radio communication that comprises: receiving (102) at least one conditional handover execution condition for conducting a conditional handover; determining (104) at least one future QoS that characterizes a quality of at least one radio channel between a radio terminal and a radio access node for at least one future time instant; and evaluating (106) the at least one handover condition based at least on the at least one future QoS.