Hierarchical ML Model IDs for Scalable Wireless Network Management
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Solution Overview
Problem
Current wireless communication networks lack a standardized framework for efficiently identifying and managing machine learning models, particularly in 5G networks, which is crucial for performance monitoring and fine-tuning, and existing solutions are not scalable for future use cases.
Innovation Solution
A hierarchical system for machine learning model identification and lifecycle management is developed, incorporating a unique identification system that includes hierarchical levels and sub-IDs to support various collaboration levels and model formats, ensuring compatibility with both current and future network functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a standardized framework for machine learning model identification is implemented, then model management efficiency and network performance monitoring are improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the model identification into hierarchical levels (Level 1: Model Family, Level 2: Model Type, Level 3: Model Version). This segmented approach allows efficient model management through standardized identifiers while reducing the complexity of handling diverse models by organizing them in a structured hierarchy.
Solution Approach 2:
The patent implements universality through the creation of a universal identification framework that can accommodate multiple model types, formats, and collaboration levels (UE-side, Network-side, Joint) within a single standardized system. This multi-functional framework improves management efficiency across different scenarios without requiring separate management systems for each model type.
2Adaptability or versatility
If a hierarchical identification system with multiple levels is implemented, then adaptability to future use cases is improved, but identification process complexity increases
Solution Approach 1:
The hierarchical identification system segments model identification into manageable levels (Model Family, Model Type, Model Version), making the complex task of identifying diverse models more systematic and less complex. Each level handles specific aspects of model differentiation, allowing the system to adapt to future use cases while maintaining a manageable identification process.
Solution Approach 2:
The patent adds dimensional structure to model identification by introducing hierarchical levels that organize models across multiple dimensions (family, type, version). This dimensional approach enhances adaptability to future use cases by providing a scalable structure while simplifying the identification process through systematic categorization rather than complex flat structures.
3Reliability
If standardized model identification is implemented across different collaboration levels, then network performance monitoring is improved, but signaling overhead increases
Solution Approach 1:
The patent segments the identification information into essential hierarchical levels that provide sufficient information for performance monitoring without excessive detail. By segmenting identification into Model Family, Model Type, and Version levels, the system achieves reliable monitoring while minimizing signaling overhead compared to requiring complete model descriptions at all collaboration levels.
Data Source
AI summary
Aspects of the subject disclosure may be directed to, for example, a method including determining one or more hierarchical levels associated with one or more machine learning (ML) models deployed in wireless communication networks, and generating one or more identifications (IDs) of the one or more ML models based on the one or more hierarchical levels. The one or more IDs of the one or more ML models indicate a network function associated with the one or more IDs, a ML model structure, a ML model delivery format, or a combination thereof. Other embodiments are disclosed.