GAN-Based Synthetic Data for Channel Estimation
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Solution Overview
Problem
The complexity of wireless channel environments in modern communication systems makes it difficult to collect and model the large quantities of data required for effective channel estimation, especially as communication systems move to higher frequency bands and more complex scenarios, leading to challenges in traditional data acquisition methods.
Innovation Solution
A method using a Generative Adversarial Network (GAN) to generate reference signal sample data and channel information sample data, allowing for the training of channel estimation models with a small quantity of real data, thereby reducing the need for extensive data collection and modeling processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data collection methods are used to gather large quantities of real reference signals and channel information, then the training data quantity increases, but the difficulty and labor overhead of data acquisition increases significantly
Solution Approach 1:
The patent uses a generative model trained on a small set of real reference signals and channel information to generate synthetic training data that copies the statistical characteristics and distribution patterns of real data. This allows obtaining large quantities of training samples without the labor-intensive process of collecting equivalent amounts of real data, directly resolving the contradiction between data quantity and acquisition complexity
Solution Approach 2:
The patent performs preliminary training of the generative model using a small dataset of real reference signals and channel information. Once trained, the generative model can independently generate unlimited synthetic training data without requiring further real data collection. This preliminary action eliminates the need for continuous data acquisition efforts while maintaining large-scale training capabilities
2Measurement precision
If more real reference signals and channel information are collected for training, then the accuracy of channel estimation improves, but the time and resources required for data collection increase
Solution Approach 1:
The generative model creates synthetic reference signals and channel information that replicate the statistical properties, correlation structures, and distribution characteristics of real wireless channel data. This copying approach provides sufficient training accuracy for channel estimation models without requiring extensive real data collection, thereby reducing data collection time while maintaining estimation precision
Solution Approach 2:
The patent transforms the data acquisition approach by changing from collecting physical real-world data to generating synthetic data through a trained generative model. This parameter change in the data source methodology maintains the essential statistical parameters needed for accurate channel estimation while eliminating the time-consuming data collection process
3Reliability
If a large amount of real data is used for training the channel estimation model, then the model performance improves, but the labor overhead and complexity of data preparation increases
Solution Approach 1:
The generative model produces synthetic training data that copies the essential statistical characteristics and distribution patterns of real wireless channel data. This approach achieves reliable model performance by providing sufficient training samples with appropriate data distribution, while dramatically simplifying data preparation by eliminating manual data collection and processing efforts
Solution Approach 2:
The generative model, once trained on a small real dataset, becomes self-sufficient for generating unlimited synthetic training data without requiring further human intervention or real data collection. This self-service capability maintains high model performance through continuous access to diverse training samples while eliminating ongoing data preparation labor
Data Source
AI summary
A method for acquiring data, an electronic device and a chip are provided. The method for acquiring data includes that: reference signal sample data and channel information sample data that are generated by a generative model are acquired. Herein, the generative model is obtained through training based on a Generative Adversarial Network (GAN), real reference signals and real channel information, and the reference signal sample data and the channel information sample data are used for training a channel estimation model.