Model Training Configuration for Autonomous Network AI Optimization
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Solution Overview
Problem
Existing communication systems face challenges in autonomously generating models that meet the diverse and stringent requirements of complex communication scenarios, particularly in self-optimizing systems, due to varying implementation methods and benchmarks among manufacturers, and the need for human intervention in model training.
Innovation Solution
A method and device for obtaining model training configuration information to guide the training of AI models, including parameters, data source constraints, and performance requirements, enabling flexible and autonomous model training across communication networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional model training methods are used with manual intervention, then model training can be performed, but the complexity of operation increases and automation is reduced
Solution Approach 1:
The system enables autonomous model training by allowing the communication system itself to automatically generate training configuration information and train models without external human intervention. The network device autonomously performs model training based on pre-configured parameters and communication system indicators, making the system self-sufficient in the model training process.
Solution Approach 2:
The patent applies preliminary action by pre-configuring model training parameters, data source constraints, and performance requirements before the actual model training process. This preparation work is done in advance to enable the autonomous training process to proceed automatically without requiring manual setup during operation.
2Stability of the object's composition
If standardized model training is implemented, then consistency across manufacturers is improved, but adaptability to diverse communication scenarios may be reduced
Solution Approach 1:
The system dynamically adjusts model training configuration based on specific communication scenarios while maintaining a standardized framework. The training configuration information can be adapted to different scenarios (e.g., different network conditions, service types) within the structured parameter体系, allowing both consistency and flexibility.
Solution Approach 2:
The patent applies local quality by allowing scenario-specific parameters to be customized within the overall standardized training framework. Different communication scenarios can have tailored training parameters and performance indicators while following the same fundamental training process and structure, ensuring both consistency and adaptability.
3Reliability
If comprehensive model training configuration is provided, then model performance meets communication requirements, but information complexity increases
Solution Approach 1:
The training configuration information is segmented into distinct, organized parameters including data source constraints, model performance indicators, training hyperparameters, and scenario-specific settings. This segmentation allows comprehensive configuration to be managed through structured modules rather than a monolithic complex system.
Solution Approach 2:
The patent creates a universal model training configuration framework that can accommodate multiple communication scenarios and model types through a standardized parameter structure. This multi-functional framework reduces complexity by providing a single adaptable system rather than separate configurations for each scenario.
Data Source
AI summary
An information configuration method includes: obtaining, by a first device, model training configuration information, where the model training configuration information is used to indicate information related to training of a first model.