Node-Specific Neural Network Configuration for AI Communication Learning
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Solution Overview
Problem
Existing wireless communication systems have surplus computing capabilities that are not effectively utilized, limiting the flexibility and efficiency of communication nodes in artificial intelligence (AI) learning tasks.
Innovation Solution
A communication method that configures neural network models based on local information of communication nodes, allowing nodes to participate in AI learning using tailored models that adapt to their specific resources and capabilities, thereby enhancing flexibility and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If communication nodes use standardized neural network models for AI learning, then model compatibility is improved, but adaptability to local information and processing efficiency deteriorate
Solution Approach 1:
The patent applies local quality by configuring neural network models differently for different communication nodes based on their local information characteristics. Each node receives configuration information tailored to its specific local conditions, allowing the model to adapt to local requirements without requiring complete model redesign at each node. This resolves the contradiction by enabling adaptability through localized model adjustments rather than full customization.
Solution Approach 2:
The patent implements dynamics by making the neural network model configuration flexible and adjustable based on local information. The model parameters and structure can be dynamically configured for each node according to its specific requirements, rather than being fixed. This dynamic configuration capability allows the system to adapt to varying local conditions while maintaining overall system coherence.
2Productivity
If communication nodes use customized neural network models for AI learning, then processing efficiency is improved, but model compatibility and system flexibility deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the model configuration into manageable components that can be independently adjusted for each node. Instead of requiring complete model customization, the system segments the configuration into key parameters and structures that can be selectively adapted. This allows each node to optimize its processing efficiency through customized configurations while maintaining compatibility with the overall system architecture.
Solution Approach 2:
The patent utilizes parameter changes by allowing flexible adjustment of model parameters for each node based on local information. The system can change parameters such as model depth, width, and other configuration details to optimize processing efficiency for each specific node's requirements. This parameter-level customization enables high processing efficiency while maintaining system flexibility through standardized configuration interfaces.
3Ease of manufacture
If communication nodes use randomized neural network models for AI learning, then implementation simplicity is improved, but processing efficiency and local adaptability deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-configuring the neural network model structure and parameters based on local information before the actual AI learning process begins. The system performs preliminary configuration of model parameters tailored to each node's local characteristics, so that when the learning process starts, the model is already optimized for that specific node's requirements. This eliminates the need for random initialization and provides both simplicity and high processing efficiency.
Data Source
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AI summary
This application provides a communication method and a related device, so that a computing capability of a communication node can be applied to artificial intelligence (artificial intelligence, AI) learning, and implementation flexibility of different nodes can be increased. In the method, the first node receives first information, where the first information indicates N pieces of configuration information, the N pieces of configuration information are used to configure resources of N model parameters, and N is a positive integer; and the first node receives a first model parameter based on first configuration information in the N pieces of configuration information, where the first model parameter is used to determine a first neural network model corresponding to local information.