Channel State Adjustment for Predictive Radio Link Failure Prevention
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Solution Overview
Problem
In communication systems, radio link failures (RLF) lead to prolonged service interruptions due to lengthy connection re-establishment processes, necessitating a solution to reduce this downtime.
Innovation Solution
Implementing a model trained on RLF-associated data to predict RLF events, allowing for proactive adjustments in channel states through methods like cell handover or mobile mode changes based on accurate RLF prediction information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If connection re-establishment process is performed after RLF event, then service continuity is restored, but service interruption time increases
Solution Approach 1:
The patent applies preliminary action by performing channel state adjustment before RLF events actually occur. The system proactively modifies channel states based on prediction information, preventing RLF events from happening in the first place, thereby avoiding the time-consuming connection re-establishment process while maintaining service continuity
2Reliability
If channel state adjustment is performed proactively based on prediction, then RLF occurrence is reduced, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary element - the prediction information - that mediates between the current channel state and the adjustment decision. This intermediary allows the system to make informed proactive adjustments without requiring complex real-time analysis, thereby reducing RLF occurrence while managing system complexity through the use of prediction-based guidance
Data Source
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AI summary
The present application relates to a method for adjusting channel state, a terminal device, a network device, a chip, a computer-readable storage medium, a computer program product and a computer program. The method includes: performing, by a terminal device, a first processing in response to that RLF prediction information meets a first condition. The RLF prediction information is obtained by processing RLF associated data related to a current moment using a first model; the first model is obtained by training based on RLF associated data related to a first RLF event; and the first processing is used to adjust the channel state of the terminal device. The present application may effectively reduce the number of RLF events, thereby reducing the service interruption time caused by RLF.