Wireless AI Context Validity Signaling for Privacy and Low Overhead
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Solution Overview
Problem
Existing wireless communication 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 support mobility scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are trained with comprehensive node context information to improve generalizability, then model accuracy improves, but signaling overhead and system complexity increase
Solution Approach 1:
The patent segments node context information into multiple dimensions including vendor information, device type, hardware specifications, software versions, and operational parameters. Each dimension is independently identified and managed, allowing the system to handle complexity through modular organization rather than monolithic data structures
Solution Approach 2:
The patent introduces context information identifiers as intermediary elements that mediate between the AI model training process and the actual node context data. These identifiers enable the model to reference and differentiate various node conditions without requiring direct transmission or storage of complete context datasets, thereby reducing signaling overhead while maintaining training accuracy
2Reliability
If detailed node context information is exchanged to improve AI model training, then model performance improves, but privacy concerns increase
Solution Approach 1:
The patent extracts and separates sensitive context information dimensions from the overall node context data. By identifying and isolating privacy-sensitive attributes (such as specific hardware identifiers or vendor-specific details), the system can manage and protect these elements separately while still utilizing necessary context information for AI model training
Solution Approach 2:
Context information identifiers serve as privacy-protecting intermediaries that allow the AI model to learn from node context patterns without exposing actual sensitive data. The identifiers capture the essential characteristics needed for model generalization while masking or abstracting the underlying sensitive information
3Measurement precision
If comprehensive context information is collected for AI model life cycle management, then model accuracy improves, but signaling overhead increases
Solution Approach 1:
The patent creates simplified representations (copies) of node context information in the form of context information identifiers. These identifier copies contain the essential distinguishing features of node contexts needed for AI model training and life cycle management, enabling the system to work with compact data structures that reduce signaling requirements while preserving the necessary information for model accuracy
Solution Approach 2:
The patent segments context information into hierarchical levels, with identifiers providing high-level categorization and detailed context information available only when needed. This segmentation allows the system to transmit and process only the essential identifier information during most AI model operations, reducing signaling overhead while maintaining the capability to access detailed context information when required for model training or validation
Data Source
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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.