AI Communication Model Online Training Under Real-World Drift
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Solution Overview
Problem
Current communication methods struggle with the generalization of artificial intelligence models trained offline, as real-world systems present complex and unstable environments that differ from simulated data, leading to poor performance.
Innovation Solution
Implement an online training strategy for AI models in communication systems, using indication information to adapt the training process to real-time data, adjusting frequency and parameters based on communication environment and device capabilities to enhance model adaptability and reduce resource overheads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If offline training is used for AI models, then training stability is improved, but model adaptability to real-world environments deteriorates
Solution Approach 1:
The patent implements dynamic online training that allows the AI model to continuously adapt to changing real-world communication environments. The model transitions from static offline training to dynamic online learning, enabling it to update its parameters in real-time based on actual deployment conditions, thus resolving the contradiction between training stability and model adaptability.
Solution Approach 2:
The AI model performs self-training in the deployed communication system by automatically learning from real-world data. The model serves itself by continuously improving its own performance through online training on actual communication data, eliminating the need for external retraining and enhancing adaptability to real environments.
2Adaptability or versatility
If online training is implemented without strategy indication, then model adaptability is improved, but resource consumption increases
Solution Approach 1:
The patent introduces indication information that dynamically adjusts training parameters such as training frequency, batch size, and learning rate based on device capabilities and communication conditions. This parameter optimization enables online training to proceed with reduced resource consumption while maintaining model adaptability improvements.
Solution Approach 2:
The system performs partial online training by selectively updating only certain model parameters or performing training at optimized frequencies rather than continuous full training. This partial action approach achieves sufficient adaptability improvement while significantly reducing computational and energy resources consumed.
3Measurement precision
If frequent online training is performed, then model accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the training process into manageable components guided by indication information, dividing frequent training into structured phases or batches. This segmentation reduces the complexity burden by organizing training operations systematically while still achieving frequent updates for maintaining high model accuracy.
Solution Approach 2:
The system implements periodic online training with optimized intervals indicated by indication information, performing training at strategically determined frequencies rather than continuously. This periodic approach maintains model accuracy by ensuring regular updates while reducing overall training complexity and resource burden compared to constant training.
Data Source
AI summary
Provided are a communication method and a communication device. The method comprises: a first communication device receiving first indication information; and on the basis of the first indication information, the first communication device performing online training on a first model used for communication, wherein the first indication information is used for indicating an online training policy for the first model, and the online training policy, which is indicated by means of the first indication information, can be used for indicating how an online training process is performed.