用生成模型合成逼真射频数据，提升真实场景下通信系统训练效果。
ReFormer: Generating Radio Fakes for Data Augmentation
- 基于变换器的自回归生成，通过离散化射频信号表示实现高效合成。
- 生成数据在星座图、分类准确率等指标上接近真实数据，精度与召回表现良好。
- 支持约束生成和跨系统数据迁移，适合信道建模与系统训练场景。
我们提出 ReFormer，一种生成式人工智能（GAI）模型，可高效生成与训练数据统计特性相似或经修改的射频（RF）数据，用于增强真实实验中采集的数据集。针对此类应用，适应性与可扩展性至关重要。ReFormer 采用基于变换器的自回归生成架构，训练于射频信号的离散表示。通过提示（prompt）控制，该模型可生成满足特定条件的信号，尤其适用于信道估计与建模训练；也可利用源系统的数据生成目标系统的训练数据。我们评估了不同变换器结构及其他设计选择对生成质量的影响，使用精度、召回率、分类准确率及信号星座图等指标进行衡量。
原文摘要 · Abstract (English)
We present ReFormer, a generative AI (GAI) model that can efficiently generate synthetic radio-frequency (RF) data, or RF fakes, statistically similar to the data it was trained on, or with modified statistics, in order to augment datasets collected in real-world experiments. For applications like this, adaptability and scalability are important issues. This is why ReFormer leverages transformer-based autoregressive generation, trained on learned discrete representations of RF signals. By using prompts, such GAI can be made to generate the data which complies with specific constraints or conditions, particularly useful for training channel estimation and modeling. It may also leverage the data from a source system to generate training data for a target system. We show how different transformer architectures and other design choices affect the quality of generated RF fakes, evaluated using metrics such as precision and recall, classification accuracy and signal constellation diagrams.
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