CSI Feedback Meta-Model Training via Synthetic Codebook Data
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Solution Overview
Problem
Current CSI feedback methods in 5G NR systems face challenges due to the lossy mapping process in codebook-based schemes and the difficulty of generalizing AI models across varying channel scenarios, especially with the increasing complexity of radio frequency environments.
Innovation Solution
A method is proposed that involves generating sample data based on a precoding matrix codebook to train an initial CSI feedback model, which then acquires a CSI feedback meta model. This meta model is used to train a target CSI feedback model, enabling efficient encoding and recovery of channel state information without the need for extensive CSI data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If codebook-based feedback scheme is used, then implementation simplicity is improved, but CSI feedback accuracy deteriorates due to lossy mapping
Solution Approach 1:
The patent creates synthetic CSI data by copying and transforming codebook entries through parameter modulation (e.g., changing SNR, channel characteristics) to generate diverse training samples without requiring actual channel measurements. This allows the AI model to learn from replicated channel states while maintaining the simplicity of codebook-based structure.
Solution Approach 2:
The patent replaces the mechanical codebook selection process with an AI-based neural network that processes CSI data non-linearly. The neural network substitutes the traditional codebook mapping mechanism, enabling more accurate CSI representation while maintaining system implementation simplicity.
2Measurement precision
If AI-based CSI feedback is used, then feedback accuracy is improved, but data acquisition difficulty worsens due to need for diverse channel data
Solution Approach 1:
The patent performs preliminary data generation by creating synthetic CSI datasets before the actual communication system operation. By pre-generating diverse channel state data through parameter modulation of codebook entries, the system avoids the need to collect real channel data during deployment, significantly reducing data acquisition difficulty.
Solution Approach 2:
The patent copies and transforms existing codebook entries to create synthetic training data. By replicating channel states through parameter modulation (SNR, fading, multipath) rather than measuring actual channels, the system generates diverse datasets without the complexity of real-world data collection.
3Speed
If meta learning is used for fast adaptation, then adaptation speed is improved, but training data requirement worsens due to need for huge diverse samples
Solution Approach 1:
The patent generates synthetic training data by copying and transforming codebook entries through parameter modulation. This creates artificial samples that mimic real channel conditions without requiring actual measurements, providing sufficient training data for meta-learning while avoiding the need for huge diverse real-world datasets.
Solution Approach 2:
The patent modifies parameters of codebook entries (such as SNR, channel fading characteristics, multipath components) to generate varied synthetic CSI data. By changing these parameters systematically, the system creates diverse training samples from a single codebook structure, reducing the quantity of real data needed for training.
Data Source
AI summary
Provided in embodiments of the application are model training methods and apparatuses. A model training method includes: a first device generating a plurality of pieces of sample data on the basis of a first codebook of a precoding matrix; the first device training an initial channel state information (CSI) feedback model on the basis of the plurality of pieces of sample data to obtain a CSI feedback meta-model. The CSI feedback meta-model is used for training a target CSI feedback model, and the target CSI feedback model is used for encoding CSI obtained by a signal receiving end and restore the encoded CSI at a signal transmitting end. Further provided in the embodiments of the application are a sample data generation method and apparatus.