Mobility Failure Classification via MCG Logging
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Solution Overview
Problem
Current 5G networks face challenges in classifying mobility failure types, such as Too Late Handover, Too Early Handover, and Handover to Wrong Cell, due to lack of information in MCG Failure Information and deletion of RLF-Reports after successful recovery, preventing network optimization of handover parameters.
Innovation Solution
Methods are introduced for network nodes to classify mobility failures based on MCG Failure Information and UE history data, including logging additional information in MCG Failure Information and RLF reports to identify failure types and outcomes, enabling detection and classification of Too Late, Too Early, and Wrong Cell handovers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RLF-Reports are deleted after successful recovery, then system resources are freed, but mobility failure classification information is lost
Solution Approach 1:
The patent extracts only the essential mobility failure classification information (failure type, timing, location) from the complete RLF report and stores it separately in mobility failure information. This allows the system to retain critical classification data while freeing up resources by not storing the entire detailed report permanently.
Solution Approach 2:
The patent performs preliminary classification of RLF events into mobility failure types (too early handover, too late handover, wrong cell handover) before the RLF report is deleted. This preliminary action ensures that classification information is captured and stored while the report is still available, preventing information loss.
2Productivity
If MCG Failure Information is used without additional logging, then signaling overhead is reduced, but failure classification capability is insufficient
Solution Approach 1:
The patent implements partial logging of MCG failure information by selectively recording only the parameters necessary for mobility failure classification (failure type, timing, location) rather than logging all possible RLF report data. This partial action provides sufficient information for handover optimization while minimizing signaling overhead.
3Measurement precision
If detailed failure information is logged continuously, then classification accuracy is improved, but signaling overhead increases
Solution Approach 1:
The patent applies local quality by enhancing MCG failure information with specific classification details (failure type, timing, location) only at the points where failures occur, rather than continuously logging all parameters. This localized enhancement provides high measurement precision for failure detection while minimizing overall signaling overhead.
Data Source
AI summary
Methods and apparatuses for classifying mobility failure. A method performed by a wireless device comprises detecting a failure event in a MCG, wherein the failure event is at least one of a RLF and a HOF. The method further comprises logging MCG Failure Information associated with the failure event subsequent to detection of the failure event, wherein the MCG Failure Information comprises an elapsed time between time of reception of a last RRC reconfiguration message associated with the MCG and time of MCG connection failure. The RRC reconfiguration message including reconfiguration with sync.