Communication Node AI Configuration Across Learning Architectures
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies due to redundant computing capabilities in communication nodes, which are not optimally utilized for artificial intelligence tasks, leading to inflexible and complex implementations across different learning architectures.
Innovation Solution
A communication method and device that enable communication nodes to apply their computing capabilities to AI tasks within a learning system by exchanging AI configuration information between nodes with different learning architectures, allowing them to perform tasks based on their specific architectures, thereby improving flexibility and matching capabilities to requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different AI systems of a same learning system perform AI tasks based on a same learning architecture, then implementation complexity is reduced, but implementation flexibility is limited
Solution Approach 1:
The patent enables a single learning system to support multiple AI systems with different learning architectures (federated learning, centralized learning, decentralized learning) through a unified framework. The communication method allows nodes to participate in different AI systems with different architectures, making the system universal and adaptable to various requirements without requiring separate dedicated systems for each architecture.
Solution Approach 2:
The patent introduces dynamic configuration capabilities where nodes can be flexibly assigned to different AI systems and learning architectures based on task requirements. The system can dynamically adjust which nodes participate in which AI systems and switch between different learning architectures as needed, providing adaptability without permanent structural changes.
2Adaptability or versatility
If multiple learning architectures are provided in a learning system, then implementation flexibility is improved, but system complexity increases
Solution Approach 1:
The patent segments the learning system into independent AI systems, each with its own learning architecture, that can coexist within a unified framework. Each AI system operates semi-independently with its own nodes and configuration, allowing multiple architectures to be provided without creating a monolithic complex system. The segmentation enables manageable complexity through modular organization.
Solution Approach 2:
The patent introduces a unified communication framework and configuration management mechanism that acts as an intermediary between different AI systems with different learning architectures. This intermediary layer handles the complexity of coordinating multiple architectures, managing node assignments, and facilitating communication, thereby enabling flexibility without proportionally increasing overall system complexity.
3Productivity
If redundant computing capability in communication nodes is utilized for AI tasks, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent enables communication nodes to autonomously participate in AI tasks using their redundant computing capabilities. Nodes can self-configure and self-manage their participation in different AI systems based on their available resources and capabilities. This self-service approach allows efficient utilization of redundant computing power without requiring complex centralized resource allocation and management mechanisms.
Data Source
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AI summary
This application provides a communication method and a related device, to enable a computing capability of a communication node to be applied to an AI task in a learning system, and improve implementation flexibility of different AI systems of a same learning system. In the method, a first node determines first information and second information, where the first information indicates AI configuration information of a first AI system, the second information indicates AI configuration information of a second AI system, the first AI system and the second AI system belong to a same learning system, the first AI system and the second AI system include at least one same node, and a learning architecture of the first AI system is different from a learning architecture of the second AI system; and the first node sends the first information and the second information.