User Equipment ML Model Reporting for Beam Failure Detection
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Solution Overview
Problem
Current wireless communication networks lack a mechanism to detect performance issues with machine learning models deployed at User Equipment (UE), leading to incorrect model outputs that can result in poor communication performance, such as beam failure detections and reduced throughput.
Innovation Solution
A system where the UE monitors the performance of its machine learning models and reports performance indicators to the network, enabling the network to take corrective actions such as stopping the use of the faulty model, switching to a non-ML algorithm, or retraining the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are deployed at UE for optimizing wireless communication, then communication performance is improved, but reliability deteriorates due to undetected model performance issues
Solution Approach 1:
The patent implements a feedback mechanism where the UE monitors the performance of its ML models by comparing expected outputs with actual outputs, detects performance degradation, and reports back to the network. This feedback loop enables the network to identify and address model performance issues, resolving the contradiction between using ML for improved communication and ensuring reliable model operation.
2Reliability
If ML model performance monitoring is implemented at UE, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent enables the UE to perform self-monitoring of its ML model performance by automatically comparing expected outputs with actual outputs and detecting performance degradation without requiring complex external monitoring infrastructure. The UE serves itself by implementing the monitoring and detection functions locally, reducing the need for additional network-side complexity while improving reliability.
3Productivity
If ML models are used for communication optimization, then productivity is improved, but harmful factors increase due to beam failure detections
Solution Approach 1:
The patent implements preliminary anti-action by monitoring ML model performance and detecting degradation before it leads to harmful outcomes such as beam failure detections. By identifying performance issues early through the monitoring mechanism and reporting to the network, corrective actions can be taken preemptively to prevent the ML model from generating harmful effects, thus maintaining both productivity and system reliability.
Data Source
AI summary
The present disclosure describes a method performed by a user equipment (UE) for reporting the performance of at least one machine-learning (ML) model to a cellular telecommunications network. Some exemplary embodiments include the UE utilizing at least one ML model, generating one or more reports or reportable information of a performance of the at least one ML model, and reporting the one or more reports or reportable information to a network. Associated devices and systems are also provided herein.