Wireless AI Context Identifiers for Low-Overhead Signaling
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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 nodes while maintaining privacy and reducing resource intensity and signaling overhead.
Innovation Solution
Implementing identifiers for node conditions to enable information exchange among nodes in various phases of AI model life cycle management, ensuring privacy and consistency, and managing 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 and configurations, then model versatility improves, but resource intensity and signaling overhead increase
Solution Approach 1:
The patent segments context information into multiple identifiers representing different scenarios, configurations, and conditions. Each identifier acts as a discrete token that can be independently managed and transmitted, allowing the AI model to handle diverse situations without requiring a single comprehensive resource-intensive model.
Solution Approach 2:
The patent changes the parameter representation from detailed context descriptions to compressed identifier tokens. This parameter transformation reduces the amount of data that needs to be processed and transmitted, thereby reducing resource intensity while maintaining the ability to represent multiple scenarios and configurations.
2Measurement precision
If detailed context information is exchanged among nodes for AI model training and inference, then model accuracy improves, but signaling overhead increases
Solution Approach 1:
The patent creates a simplified copy of context information in the form of identifier tokens that reference the actual detailed context. These identifier copies can be transmitted efficiently while preserving the essential information needed for AI model operations, reducing signaling overhead while maintaining accuracy.
Solution Approach 2:
The patent introduces identifier tokens as intermediary elements between the actual context information and the AI model processing. These intermediaries enable efficient information exchange by representing complex context data in a compact form that can be transmitted with lower signaling overhead.
3Stability of the object's composition
If node context information is made visible across the network for AI model consistency, then model consistency improves, but privacy is compromised
Solution Approach 1:
The patent extracts only the essential identifying features of node context information and represents them as anonymous identifier tokens. This extraction process removes sensitive details while retaining the information needed for model consistency, thereby maintaining stability without compromising privacy.
Solution Approach 2:
The patent applies different levels of information visibility to different parts of the network. Identifier tokens provide just enough local information for model consistency at each node without exposing global context details, creating a localized quality of information sharing that balances consistency and privacy.
Data Source
AI summary
Various aspects of the present disclosure relate to artificial intelligence in wireless communications. An apparatus, such as a user equipment (UE), receives one or more of: an indication, from a first network equipment (NE), of one or more validity criterion for a portion of network context information for a second network equipment, or identifiers for the portion of the network context information for the second network equipment.