Translation Node for Distributed AI Model Deployment in 5G Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network architectures are inadequate for supporting distributed AI and Distributed Deep Neural Networks (DDNN) deployment, particularly in 5G mobile networks, due to limitations in computation resources and privacy concerns, which require efficient data management and reduced impact on legacy operations.
Innovation Solution
A method is introduced to operate a translation node that receives application service information and translates Model Deployment Map (MDM) information into network Quality of Service (QoS) parameters, enabling efficient DDNN deployment across the communication network with reduced impact on legacy operations, and a core network node that acquires and transmits a distributed AI model with cloud, edge, and local model portions for dynamic deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed AI models are deployed across multiple network nodes, then computation resource utilization is improved and privacy concerns are addressed, but network architecture complexity increases and legacy system compatibility deteriorates
Solution Approach 1:
The AI model is segmented into multiple components distributed across different network nodes: anchor nodes host full models, while remote nodes host smaller sub-networks. This segmentation enables efficient resource utilization across the network while maintaining manageable complexity at each individual node through the use of standardized interface definitions for model components.
Solution Approach 2:
A translation node is introduced as an intermediary that converts Model Deployment Map (MDM) information into network Quality of Service (QoS) parameters. This intermediary layer simplifies the network architecture by providing a standardized translation mechanism between model deployment requirements and network resource allocation, reducing the complexity of direct node-to-node coordination.
2Object-affected harmful factors
If distributed AI deployment is implemented, then privacy protection is improved by keeping data local, but impact on legacy operations increases due to architecture changes
Solution Approach 1:
The network is segmented into anchor nodes and remote nodes with clearly defined roles. Remote nodes process local data using deployed model sub-networks, ensuring privacy protection by keeping data local. The segmentation allows legacy operations to continue at anchor nodes while new distributed AI functionality is added at remote nodes, minimizing disruption to existing systems.
Solution Approach 2:
The system design provides multi-functionality by enabling nodes to serve both traditional network functions and distributed AI processing functions. The standardized interface definitions allow model components to be universally deployed across different node types, enabling legacy operations and new AI functionality to coexist without requiring complete system replacement.
3Productivity
If Model Deployment Map information is translated into network QoS parameters, then DDNN deployment efficiency is improved, but translation node complexity increases
Solution Approach 1:
The translation node performs self-service by automatically converting MDM information into QoS parameters using standardized interface definitions. This automated translation process improves deployment efficiency by eliminating manual configuration steps. The node manages its own complexity through systematic conversion rules that map model deployment requirements directly to network QoS parameters without requiring complex external coordination.
Data Source
AI summary
In the present disclosure, methods of operating a translation node in a communication network are discussed. The translation node receives Application service information for an application service, a distributed Artificial Intelligence AI model for the application service, and Model Deployment Map MDM information for the application service, translates the MDM information for the application service into network Quality of Service QoS parameters for the application service, and provides the distributed AI model with the network QoS parameters for distribution to at least one other node of the communication network. Related methods of operating SMF and NDWAF nodes are also discussed.