Predictive Conditional Mobility for Reliable High-Frequency Handover
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Solution Overview
Problem
In high-frequency wireless communication environments, such as those utilizing the THz band, mobility failures due to early, late, or incorrect cell changes lead to low reliability and high latency, hindering the achievement of high data rates.
Innovation Solution
A wireless device utilizes AI/ML to predict future cell quality by deriving predictive measurement results, enabling conditional mobility based on both current and predictive measurement results to ensure guaranteed cell quality during handover.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conditional mobility is executed frequently in high-frequency coverage to maintain connectivity, then service continuity is improved, but mobility failures increase leading to low reliability and high latency
Solution Approach 1:
The system performs preliminary actions by configuring multiple candidate target cells in advance and deriving predictive measurement results for future time points before mobility execution is needed. This allows the UE to have pre-evaluated target cells ready, reducing the time required during actual mobility execution and improving reliability by selecting from pre-validated candidates.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring present measurement results and comparing them with predictive measurement results. The UE evaluates whether current conditions meet the criteria for conditional mobility to candidate cells, using this feedback to make informed decisions about when and where to execute mobility, thereby improving reliability while optimizing timing.
2Reliability
If AI/ML-based measurement prediction is implemented to improve mobility accuracy, then mobility robustness is enhanced, but device complexity increases
Solution Approach 1:
The system introduces an intermediary approach by using AI/ML models to derive predictive measurement results from available data. This intermediary processing layer translates complex mobility prediction requirements into actionable insights, enhancing mobility robustness while managing device complexity through specialized algorithms rather than brute-force methods.
Solution Approach 2:
The system applies parameter changes by transforming raw measurement data into predictive measurement results through AI/ML processing. This changes the state of the data from present observations to future predictions, enabling more accurate mobility decisions while the computational complexity is managed through efficient model implementation.
Data Source
AI summary
A method and apparatus for conditional mobility based on measurement prediction in a wireless communication system is provided. A wireless device receives, from a network, a configuration of a conditional mobility for a target cell including at least one condition. A wireless device acquires a present measurement result for the target cell at a first time point. A wireless device derives a predictive measurement result for the target cell for a second time point. A wireless device performs the conditional mobility to the target cell based on both the present measurement result and the predictive measurement result satisfying the at least one condition.