Conditional Handover Using Future QoS Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing radio communication systems face challenges in accurately predicting handover events due to stochastic channel models, leading to service interruptions and management overhead, particularly in resource-constrained scenarios.
Innovation Solution
Implementing a method for conditional handover that includes determining future QoS using machine-trained models and pre-trigger indications to manage handovers proactively, reducing unnecessary handover actions and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If periodic monitoring of the channel is performed to assess channel quality, then channel quality assessment accuracy is improved, but service interruptions and management overhead increase
Solution Approach 1:
The system performs preliminary actions by predicting future QoS values before actual handover is needed. Machine learning models forecast channel quality at future time instants, allowing the network to proactively identify deteriorating channels and initiate handover preparations in advance, thus avoiding service interruptions while maintaining accurate channel assessment.
Solution Approach 2:
The system implements feedback mechanisms where predicted QoS values are continuously fed back into the handover decision-making process. The network monitors predicted channel quality and adjusts handover timing and resource allocation based on these predictions, creating a closed-loop system that improves service continuity while optimizing monitoring efficiency.
2Ease of operation
If handover conditions are evaluated based on current QoS only, then decision simplicity is improved, but handover timing accuracy deteriorates
Solution Approach 1:
The system performs preliminary QoS predictions for future time instants before handover execution is required. By forecasting channel quality ahead of time, the network can make more accurate handover timing decisions while maintaining the simplicity of rule-based evaluation logic. The machine learning models handle the complex prediction work, leaving the decision logic relatively simple.
Solution Approach 2:
The system introduces dynamic elements by evaluating QoS at multiple future time instants rather than a single static point. This allows the handover decision to adapt to predicted channel variations over time, improving timing accuracy while the underlying evaluation mechanism remains based on straightforward comparisons of predicted QoS values against handover thresholds.
3Reliability
If multiple candidate cells are prepared for handover to increase robustness, then handover reliability is improved, but resource consumption increases
Solution Approach 1:
The system performs preliminary assessment of multiple candidate cells by predicting their future QoS values. Instead of preparing all possible candidate cells, the network uses predicted QoS to identify and prioritize the most promising candidates, preparing resources only for cells likely to become optimal. This reduces resource consumption while maintaining robustness through selective multi-cell preparation.
Solution Approach 2:
The system dynamically changes the set of active candidate cells based on predicted QoS parameters. As channel conditions evolve, the network adjusts which cells remain in the candidate list, adding or removing cells based on future QoS predictions. This parameter-driven approach maintains handover robustness with multiple candidates while optimizing resource usage by keeping only the most relevant candidates active.
Data Source
AI summary
A method for radio communication. The method includes: receiving at least one conditional handover execution condition for conducting a conditional handover; determining 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 the at least one handover condition based at least on the at least one future QoS.