UE ML Performance Reporting for Wireless Model Updates
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Solution Overview
Problem
Existing wireless communications systems lack efficient methods for monitoring and updating machine learning models, particularly in devices like user equipment (UE), which hinders performance optimization and adaptability.
Innovation Solution
Implementing a system where user equipment (UE) receives control signals for event triggers, monitors performance parameters of machine learning models by comparing input and output data, and reports these parameters to a network entity or server, enabling dynamic updates or model changes based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are implemented in wireless communication devices, then performance optimization and adaptability are improved, but monitoring and updating capabilities are insufficient
Solution Approach 1:
The patent implements a feedback mechanism where the UE monitors ML model performance using ground truth data and compares actual outputs with expected outputs. Performance parameters are reported to the network entity, which triggers model updates when performance degradation is detected, creating a closed-loop system that maintains model reliability while enabling adaptability
Solution Approach 2:
The network entity pre-configures the UE with ground truth data and performance thresholds before the UE deploys the ML model. This preliminary preparation enables the UE to immediately begin monitoring and reporting without requiring real-time setup, ensuring reliable performance tracking from the start of model operation
2Measurement precision
If continuous monitoring of machine learning model performance is implemented, then model accuracy is maintained, but signaling overhead and energy consumption increase
Solution Approach 1:
Instead of continuous monitoring, the patent implements periodic performance reporting triggered by specific events such as performance threshold violations or ground truth data availability. The UE monitors model performance continuously but only reports when predefined conditions are met, reducing energy consumption while maintaining measurement precision through event-driven sampling
Solution Approach 2:
The system dynamically adjusts monitoring parameters such as reporting thresholds and ground truth data update intervals based on network conditions and model performance. When performance is stable, monitoring intensity is reduced; when degradation is detected, monitoring frequency increases, optimizing the balance between measurement precision and energy consumption
3Productivity
If event-triggered reporting is used for machine learning model performance, then reporting efficiency is improved, but real-time detection capability is reduced
Solution Approach 1:
The network entity pre-configures the UE with performance thresholds and trigger conditions before deployment. By anticipating when reporting will be needed and preparing the criteria in advance, the system achieves efficient event-triggered reporting without sacrificing detection speed, as the UE is already prepared to immediately report when predefined conditions are met
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. The method may include a user equipment (UE) may receive a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. Further, the UE may receive one or more signals indicating input data for monitoring a performance of the machine learning model by the UE. Upon detecting the event trigger, the UE may transmit a report comprising the performance parameter.