AI Node Selection for Next-Hop Model Training in Communication Networks
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Solution Overview
Problem
There is a lack of extensive research on how to perform AI-based processing on a functional feature of a network node to implement model training and inference between network nodes in a communication network, which are not addressed by existing technologies, particularly in complex scenarios.
Innovation Solution
A communication method and apparatus that enables AI-based processing between network nodes by allowing a first node to send a request for node information, determine a next-hop node, and transmit an updated model, utilizing a node selection mechanism that reduces management and control complexity and is flexible.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a node selection mechanism is implemented for model training in wireless networks, then model training efficiency and flexibility are improved, but management and control complexity increases
Solution Approach 1:
The patent implements a self-organizing node selection mechanism where network nodes autonomously determine their roles and relationships for model training without centralized control. Each node independently selects its next-hop node based on local information exchange, eliminating the need for complex centralized management while maintaining training efficiency
Solution Approach 2:
The patent divides the model training process into discrete node selection stages, where each node independently performs selection based on exchanged information. This segmentation allows the system to achieve complex coordinated behavior through simple local decisions, reducing overall management complexity
2Loss of time
If node information is exchanged between neighboring nodes, then processing delay is reduced, but the range of node selection is limited
Solution Approach 1:
The patent segments the node selection process into multiple stages: first exchanging information with neighboring nodes for quick local selection, then optionally expanding to non-neighboring nodes if needed. This staged approach achieves fast processing through local exchange while maintaining flexibility for broader selection when required
Solution Approach 2:
The patent implements a dynamic node selection mechanism where the selection range adapts based on network conditions and training requirements. Nodes can dynamically adjust whether to select from neighboring nodes only or expand to include non-neighboring nodes, balancing processing speed and selection flexibility
3Reliability
If a control node is involved in node selection, then node selection success rate is improved, but processing delay increases
Solution Approach 1:
The patent introduces a control node as an intermediary that assists node selection only when necessary. The control node receives requests from nodes that cannot independently select, providing guidance to improve success rate while minimizing its involvement to reduce processing delay
Solution Approach 2:
The patent implements partial involvement of the control node in the selection process. Instead of the control node managing all selections, it intervenes only when nodes fail to find suitable next-hop nodes through independent selection, achieving improved success rate with minimal additional delay
Data Source
AI summary
A communication method and apparatus, relating to the field of communication technologies, wherein the method includes: A first node sends a first request, where the first request is used to request node information of one or more nodes, and the one or more nodes are nodes in a node set associated with the first node; the first node obtains the node information of the one or more nodes, determines a next-hop node of the first node from the one or more nodes based on the node information, and sends an updated first model of the first node to the next-hop node.