Radio Link Problem Prediction Using UE Context
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Solution Overview
Problem
Traditional radio link failure (RLF) recovery procedures in mobile communications are slow, leading to prolonged service interruptions due to delayed reestablishment, as they rely on timers or maximum random access attempts, which can result in extended downtime before resolving radio link issues.
Innovation Solution
Implementing a method for radio link problem prediction using user equipment (UE) context information, such as cell measurement results, channel state, and historical data, to generate prediction results and trigger early recovery procedures, including handovers or reestablishments, based on machine learning or AI algorithms to anticipate and prevent RLFs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RLF recovery procedure using timer T310 or maximum RA attempts is used, then the system maintains simplicity and reliability, but the service interruption time is prolonged
Solution Approach 1:
The system performs preliminary detection of radio link problems before actual failure occurs by monitoring downlink synchronization state and uplink transmission status. When prediction indicators show RLF is likely to occur, the UE proactively triggers handover or reestablishment procedures in advance, rather than waiting for timer T310 to expire or maximum RA attempts to be reached. This preliminary action significantly reduces service interruption time while maintaining recovery reliability.
2Loss of time
If early recovery procedure is implemented using prediction algorithms, then the service interruption time is reduced, but the device complexity increases
Solution Approach 1:
The UE autonomously performs radio link problem prediction by evaluating its own context information including downlink synchronization state, uplink transmission status, and historical failure patterns. The device self-determines when to trigger early recovery procedures without requiring complex network-side prediction algorithms. This self-service approach enables early recovery while minimizing the increase in device complexity, as the prediction logic leverages existing measurement and reporting mechanisms.
3Device complexity
If traditional RLF detection method waiting for timer expiration is used, then the detection mechanism remains simple, but the detection timing is delayed
Solution Approach 1:
The system implements continuous feedback monitoring of radio link status through downlink synchronization checks and uplink transmission outcomes. This feedback mechanism provides real-time information about deteriorating link conditions, enabling the UE to detect potential RLF events before traditional timers expire. The feedback loop uses existing measurement resources to assess link quality trends, achieving earlier detection without substantially increasing mechanism complexity.
Data Source
AI summary
Various solutions for radio link (RL) problem prediction with respect to user equipment and network node in mobile communications are described. An apparatus may perform an RL problem prediction according to an apparatus context to generate a prediction result before a radio link failure (RLF) occurs. The apparatus may determine whether to perform RL early recovery based on the prediction result.