ML Model Grouping Configuration for Wireless UEs
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Solution Overview
Problem
Current wireless communication systems, particularly in 5G NR, face challenges in efficiently managing and switching between different machine learning (ML) models for various tasks and conditions, leading to increased signaling overhead and complexity.
Innovation Solution
The implementation of a method that allocates ML models into baseline model groups (BMG) and specific model groups (SMG) based on tasks and conditions, allowing for dynamic switching between these groups to optimize performance and reduce signaling overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple ML models are configured for different tasks and conditions, then adaptability and performance are improved, but signaling overhead and system complexity increase
Solution Approach 1:
The patent segments ML models into two distinct groups: Baseline Model Groups (BMGs) that handle common tasks and Specific Model Groups (SMGs) that handle task-specific or condition-specific scenarios. This segmentation allows the system to manage multiple models efficiently by organizing them hierarchically, reducing the complexity of model selection and switching while maintaining high adaptability across different tasks and conditions.
2Productivity
If ML models are dynamically switched based on tasks and conditions, then performance optimization is achieved, but control and management complexity increases
Solution Approach 1:
The patent implements dynamic model switching mechanisms that allow the system to automatically select appropriate ML models based on current tasks and conditions. The framework enables flexible transitions between BMGs and SMGs, optimizing performance for different scenarios while providing structured control mechanisms to manage the complexity of dynamic model selection and switching.
Data Source
AI summary
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for ML model grouping techniques. A UE may receive a configuration for one or more ML models, which may be based on a capability of the UE. The configuration may be associated with at least one of a task or a condition of at least one procedure of the UE. The one or more ML models may be switchable at the UE based on the condition of the at least one procedure of the UE. The UE may allocate the one or more ML models to at least one of a BMG or an SMG for switching between the one or more ML models based on the at least one of the task or the condition of the at least one procedure of the UE.