Channel Prediction Model Overfitting for Low-Complexity CSI Feedback
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Solution Overview
Problem
Current channel prediction methods, such as Kalman filters, are complex, require long on-line training times, and have poor generalization properties, making them inefficient for channel information prediction in communication systems.
Innovation Solution
A terminal device obtains model configuration information for a first prediction model and receives overfitting configuration information from a network device to re-train the model, allowing for specialized channel information prediction with reduced complexity and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional channel prediction methods like Kalman filters are used, then channel prediction can be performed, but the system complexity increases and training time extends
Solution Approach 1:
The patent transforms the channel prediction approach by changing the fundamental parameters from traditional filter-based methods to AI/ML model-based methods. This involves shifting from continuous filtering operations to discrete model inference, fundamentally altering how channel state information is predicted while maintaining accuracy
Solution Approach 2:
The patent replaces the mechanical/mathematical Kalman filter system with an AI/ML-based prediction system. This substitution eliminates the need for complex recursive calculations and training procedures associated with traditional filters, reducing system complexity while preserving prediction reliability
2Reliability
If traditional channel prediction methods like Kalman filters are used, then channel prediction can be performed, but the training time increases
Solution Approach 1:
The patent applies preliminary action by pre-training AI/ML models offline using historical channel data before deployment. This allows the model to be ready for immediate use without requiring lengthy online training, thus reducing training time loss while maintaining prediction accuracy
Solution Approach 2:
The patent employs lightweight AI/ML models that can be quickly deployed and replaced without extensive training. These models are designed to be computationally efficient and can be rapidly instantiated, reducing the time penalty associated with training while maintaining acceptable accuracy levels
3Reliability
If traditional channel prediction methods are used, then channel prediction can be performed, but the generalization performance deteriorates
Solution Approach 1:
The patent enhances generalization performance by designing AI/ML models with universal applicability across different channel conditions and terminal devices. The models are trained on diverse datasets encompassing various scenarios, enabling them to adapt to new conditions without retraining, thus improving both reliability and adaptability
4Productivity
If channel prediction is performed to improve throughput, then terminal device throughput increases, but feedback overhead increases
Solution Approach 1:
The patent extracts only the essential channel state information needed for accurate prediction, avoiding transmission of redundant data. By using AI/ML models to predict channel conditions from minimal input data, the system reduces feedback overhead while maintaining throughput improvement benefits
Data Source
AI summary
Embodiments of the present disclosure disclose devices, methods and apparatuses for channel prediction. A terminal device obtains model configuration information. The model configuration information indicates a first prediction model for channel prediction. The terminal device receives overfitting configuration information from a network device. The overfitting configuration information indicates configuration on reference signals for overfitting by the first prediction model. The terminal device reports channel information predicted based on the first prediction model and the overfitting configuration information.