Dynamic Neural Network Configuration for Communication Nodes
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Solution Overview
Problem
In traditional mobile communication systems, network optimization and data processing are resource-intensive and inefficient due to manual deployment and limited data utilization, especially with the addition of new child nodes and the need for updated neural network models.
Innovation Solution
A communication system configuration method that dynamically configures child node neural network models based on acquired characteristic information, including height, antenna configuration, and historical data, using a master node to select and update models for optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If only predetermined default settings are adopted for new child nodes, then configuration simplicity is maintained, but targeted optimal configuration cannot be realized
Solution Approach 1:
The patent implements dynamic configuration by automatically acquiring characteristic information from child nodes and selecting appropriate neural network models based on these characteristics. The system transitions from static default settings to dynamic, characteristic-based configuration, allowing the master node to adaptively match child nodes with suitable models rather than applying uniform defaults to all nodes
Solution Approach 2:
The system changes configuration parameters from fixed default values to variable parameters derived from child node characteristic information. By acquiring and analyzing characteristics such as node type, function, and operational parameters, the system selects neural network models with appropriate parameters tailored to each child node's specific requirements
2Ease of manufacture
If only local data of a single child node is used for training, then training simplicity is maintained, but best model optimization and model sharing cannot be realized
Solution Approach 1:
The patent merges training data from multiple child nodes into a centralized training process. The master node collects characteristic information and training data from multiple child nodes, combines these datasets, and performs unified model training. This pooling approach overcomes the limitation of single-node local data while enabling model sharing across the network
Solution Approach 2:
The trained neural network model at the master node serves multiple child nodes simultaneously. By training on aggregated data from multiple nodes and then distributing the optimized model back to them, the system achieves a universal solution that improves model performance across all participating child nodes rather than maintaining isolated single-node models
3Speed
If only the latest data is used for training, then training speed is maintained, but historical training data cannot be utilized to improve accuracy
Solution Approach 1:
The system performs preliminary data collection and aggregation at the master node before training begins. Historical characteristic information from multiple child nodes is gathered and prepared in advance, creating a comprehensive training dataset that combines both historical and recent data, thus preparing the foundation for accurate model training without sacrificing speed
Data Source
AI summary
The present disclosure relates to a communication system based on a neural network model, and a configuration method therefor. The communication system includes at least one master node and multiple child nodes that are in communication connection with the master node, and a child node neural network model is configured in each of the multiple child nodes. The configuration method for the communication system includes: obtaining feature information of the multiple child nodes; and dynamically configuring the child node neural network models on the basis of the obtained feature information.