AI Model Management in Mobile Communication Systems
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Solution Overview
Problem
Current mobile communication systems face challenges in efficiently managing artificial neural network models for wireless communication, particularly in reducing signal transmission load and quickly reporting model status information, especially when terminals change or update their supported models due to battery consumption or heat conditions.
Innovation Solution
A method and apparatus for managing artificial neural network models in a mobile communication system, where a terminal reports required network configurations and model-specific status information in two stages, allowing the base station to determine which models to activate or deactivate based on load and performance indicators, thereby reducing redundant information transmission and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If terminals report all model status information frequently, then the network can maintain up-to-date model information, but the signal transmission load increases
Solution Approach 1:
The patent extracts only the essential model status information that needs to be reported to the network, filtering out redundant data. The terminal identifies and reports only the minimum necessary model status parameters to maintain network awareness while minimizing transmission overhead and energy consumption.
Solution Approach 2:
Instead of reporting complete model status information continuously, the terminal applies partial reporting by transmitting only the necessary portions of model status data at optimized intervals. This partial action approach reduces transmission frequency and data volume while still providing the network with sufficient information to manage model activations effectively.
2Measurement precision
If terminals report detailed model information, then the network can make accurate model activation decisions, but the reporting complexity increases
Solution Approach 1:
The patent segments model status information into distinct, organized categories or fields, allowing the terminal to report structured data that is easier to process. By dividing the reporting into manageable segments with clear formats, the system achieves accurate model activation decisions while reducing the complexity of information transmission and processing.
3Adaptability or versatility
If the network activates multiple AI/ML models, then the terminal can support diverse wireless communication functions, but the device resource consumption increases
Solution Approach 1:
The patent implements dynamic model activation where the terminal and network continuously assess current conditions such as battery status, heat generation, and communication requirements. Based on these dynamic conditions, the system adjusts which AI/ML models are active, enabling the terminal to support diverse functions when resources are abundant while reducing model activation during high resource consumption states.
Data Source
AI summary
A method of a communication node may comprise: transmitting required network configurations for applying each of artificial neural network models to a network node; and transmitting a status report of the first model including a model identifier field and a model information field for each of the artificial neural network models to the network node to activate at least one artificial neural network model among the artificial neural network models, wherein each of the required network configurations includes a configuration identifier and network configuration information.