AI Model Online Learning in Wireless Communication
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Solution Overview
Problem
Current AI models in wireless communication systems primarily focus on offline learning, leading to low prediction accuracy and an inability to meet dynamic communication requirements due to their inability to adapt to changing environments.
Innovation Solution
A data collection method where communication devices exchange information to enable online learning of AI models, allowing for continuous data collection and iterative training, improving prediction accuracy and system performance by adapting to real-time data in a dynamic environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI models focus on offline learning and deployment, then device complexity is reduced, but prediction accuracy deteriorates and the system cannot adapt to dynamic communication requirements
Solution Approach 1:
The patent transitions AI models from static offline learning to dynamic online learning, where models continuously adapt to changing communication environments through real-time data collection and iterative training, enabling the system to maintain high prediction accuracy in dynamic conditions
Solution Approach 2:
The patent implements continuous data collection and iterative model training operations, where the AI model continuously learns from new data without interruption, ensuring sustained improvement in prediction accuracy while adapting to evolving communication requirements
2Adaptability or versatility
If AI models are trained offline only, then ease of operation is improved, but adaptability to changing environments deteriorates
Solution Approach 1:
The patent enables AI models to perform self-updates through online learning, where the model automatically collects data, trains itself, and improves without requiring manual retraining or intervention, thereby achieving high adaptability while maintaining operational simplicity
Solution Approach 2:
The patent implements continuous feedback loops where the AI model receives performance feedback from real-time communication data, automatically adjusts its parameters through iterative training, and improves its adaptability to changing environments while operating autonomously
Data Source
AI summary
A data collection method includes receiving, by a first communication device, first information from a second communication device; and performing, by the first communication device, data collection based on the first information, and training an artificial intelligence (AI) model based on collected data.