RRC Signaling for AI Model Distribution in Wireless Networks
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Solution Overview
Problem
Wireless communication systems face challenges in efficiently managing and updating artificial intelligence (AI) and machine learning (ML) models across user equipment (UE) and network nodes, particularly in ensuring that necessary models are available and transferred effectively via radio resource control (RRC) signaling.
Innovation Solution
The method involves UE and network nodes communicating to update available model information and transfer AI/ML models using RRC signaling, allowing for the transfer of models between nodes and enabling the network to manage model availability and distribution efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI/ML models are transferred via RRC signaling between network nodes and UE, then model availability and distribution efficiency are improved, but signaling overhead and network resource consumption increase
Solution Approach 1:
The network node pre-configures the UE with model information including model identifiers, supported formats, and availability status before actual model transfer. This preliminary action allows the UE to prepare for efficient model reception and reduces the need for repeated signaling exchanges during model distribution operations.
Solution Approach 2:
The patent introduces an RRC signaling mechanism as an intermediary layer between the network node's model repository and the UE's model buffer. This intermediary handles model metadata transmission, transfer status reporting, and coordination of model delivery, thereby managing signaling overhead systematically while maintaining distribution efficiency.
2Adaptability or versatility
If multiple AI/ML models are stored and managed in UE context, then model versatility and system capabilities are improved, but UE memory consumption and processing complexity increase
Solution Approach 1:
The patent segments the AI/ML model management system into distinct functional components: model storage buffer, model selection logic, model transfer handler, and model execution engine within the UE. This segmentation allows each component to operate independently with defined interfaces, reducing overall processing complexity while supporting multiple models simultaneously.
Solution Approach 2:
The UE dynamically manages its model portfolio by automatically selecting which models to retain in memory based on current network conditions, service requirements, and available resources. Models can be dynamically transferred from the network, updated, or released as needed, providing versatility without permanent storage of all possible models.
3Reliability
If AI/ML model transfers are initiated and managed by network nodes, then centralized control and model security are improved, but network node complexity and control signaling overhead increase
Solution Approach 1:
The patent extracts the complex model management functions from the network node and relocates them to the UE, including model selection, transfer initiation, and local model updates. The network node retains only essential control functions such as model repository management and security policy enforcement, thereby reducing network node complexity while maintaining centralized security control.
Solution Approach 2:
The UE provides feedback to the network node regarding model transfer status, local model availability, and performance metrics. This feedback mechanism enables the network node to make informed decisions about model distribution without requiring complex real-time analysis, simplifying network node operations while ensuring secure and reliable model management.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may communicate with one of a first network node or a second network node to update available model information in a UE context at one or more of the first network node or the second network node. The UE may receive, from one of the first network node or the second network node, a model transfer via radio resource control (RRC) signaling. Numerous other aspects are described.