UE Machine Learning Model KPI Reporting for Wireless Feedback
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Solution Overview
Problem
Wireless communications systems face challenges in improving technical performance, including signal attenuation, efficiency, power consumption, reliability, and device compatibility, necessitating enhanced monitoring and feedback mechanisms for machine learning models.
Innovation Solution
Implementing methods for obtaining and transmitting key performance indicators (KPIs) and additional performance feedback for machine learning models in user equipment (UE) and network entities, enabling dynamic configuration and reporting to enhance model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are deployed in wireless communications systems, then technical performance and efficiency are improved, but monitoring and feedback mechanisms become more complex
Solution Approach 1:
The patent implements comprehensive feedback mechanisms where UEs report ML model performance metrics (accuracy, latency, resource consumption) to the network. The network entity receives this feedback and uses it to dynamically adjust model configurations, trigger retraining, or switch between multiple models. This closed-loop feedback system enables continuous optimization of ML model performance in the wireless network.
2Reliability
If performance monitoring of ML models is enhanced, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent divides the monitoring and feedback functionality into distinct segments: (1) UE-side monitoring of local ML model performance metrics, (2) Network-side aggregation and analysis of reported metrics, and (3) Centralized model management and retraining coordination. This segmentation distributes complexity across multiple entities, making the overall system more manageable while maintaining comprehensive reliability monitoring.
3Adaptability or versatility
If dynamic configuration and reporting of KPIs is implemented, then adaptability is improved, but use of energy increases
Solution Approach 1:
The patent implements dynamic configuration where the network entity can adjust which KPIs are monitored and reported based on current network conditions, model performance requirements, and resource availability. The reporting frequency and granularity can be dynamically modified, allowing the system to adapt between detailed monitoring (when resources permit) and minimal monitoring (when energy is constrained), thus balancing adaptability with energy consumption.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for wireless communications by a user equipment (UE), generally including obtaining a set of key performance indicators (KPIs) for a machine learning (ML) model running on the UE and transmitting, to an entity associated with the ML model, a report including an aggregation of the KPIs and additional performance feedback for the ML model.