Wireless AI Context Validity Identifiers for Private Model Sharing
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Solution Overview
Problem
Existing wireless communications systems face challenges in creating AI models that are generalizable across different scenarios, configurations, and conditions of various nodes and vendors, leading to resource-intensive and complex solutions that compromise privacy and cause significant signaling overhead.
Innovation Solution
Implementations provide identifiers for node conditions to enable information exchange among nodes during AI model life cycle management phases, ensuring privacy and consistency, applicable to one-sided and two-sided models, and manage visibility and consistency of context information across nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models are trained to be generalizable across different scenarios, configurations, and conditions, then model versatility improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent segments context information into distinct types (network context, UE context, validity criteria) and manages each type separately through standardized identifiers. This segmentation allows the AI model to handle diverse scenarios without requiring a single complex generalization mechanism, thereby improving versatility while controlling system complexity.
Solution Approach 2:
The patent creates universal identifier structures that can represent multiple types of context information across different vendors and scenarios. These standardized identifiers serve multiple functions: they identify context types, track validity, and enable information exchange between nodes. This multi-functionality allows a single framework to handle diverse AI scenarios without increasing proportional complexity.
2Measurement precision
If comprehensive context information is exchanged among nodes for AI model training, then model accuracy improves, but signaling overhead increases
Solution Approach 1:
The patent extracts only the essential context information needed for AI model training by representing complex context data through compact standardized identifiers. Instead of exchanging entire context datasets, nodes exchange condensed identifier representations that capture the essential context types and validity criteria, thereby maintaining model accuracy while significantly reducing signaling overhead.
Solution Approach 2:
The patent transforms context information from detailed descriptive data into standardized identifier parameters. This parameter transformation changes the form of context information from verbose textual descriptions to structured, compact identifier representations, enabling efficient exchange that maintains information quality while minimizing signaling overhead.
3Adaptability or versatility
If node-specific context information is shared across the network, then AI model personalization improves, but privacy concerns increase
Solution Approach 1:
The patent introduces standardized identifiers as intermediaries between node-specific context information and the network. Instead of directly sharing sensitive raw context data, nodes exchange anonymized identifier representations that preserve the essential characteristics needed for model personalization while removing directly identifiable information. This intermediary layer enables personalization while protecting privacy.
Solution Approach 2:
The patent creates simplified copy representations of context information in the form of standardized identifiers. These identifier copies capture the essential features of node-specific context needed for AI model training and personalization, while being abstracted enough to not reveal sensitive underlying data. The copy preserves functional information while protecting privacy-sensitive details.
Data Source
AI summary
Various aspects of the present disclosure relate to artificial intelligence in wireless communications. An apparatus, such as a UE, receives an indication of one or more validity criterion for a portion of network context information, and stores the indication of the one or more validity criterion for the portion of the network context information as part of learning model data.