用视觉变压器直接识别带噪无线信号调制方式，省去降噪步骤。
NMformer: A Transformer for Noisy Modulation Classification in Wireless Communication
- 将信号转为星座图输入视觉变压器，直接分类不同信噪比下的调制类型。
- 微调后在高信噪比下准确率提升4.67%，低信噪比任务也优于基础模型。
- 无需额外降噪，对未见类别仍表现良好，适合实际通信场景部署。
调制分类在无线通信中极具挑战，因信号常受多种环境噪声干扰。现有方法需额外降噪步骤，增加计算开销。本文提出基于视觉变压器（ViT）的NMformer模型，直接预测不同信噪比（SNR）下的无线信号调制图像。通过将调制信号生成二维星座图，保留信号特征。模型在106,800张调制图像上训练基础分类器，并仅用3,000张图像进行特定任务微调。采用分布内与分布外两种预测设置，结果显示：微调后模型在高信噪比分布内分类准确率提升4.67%；低信噪比任务准确率亦高于基础模型；且对分布外未见数据预测表现更优。大量实验验证了NMformer在广泛信噪比范围内的有效性。
原文摘要 · Abstract (English)
Modulation classification is a very challenging task since the signals intertwine with various ambient noises. Methods are required that can classify them without adding extra steps like denoising, which introduces computational complexity. In this study, we propose a vision transformer (ViT) based model named NMformer to predict the channel modulation images with different noise levels in wireless communication. Since ViTs are most effective for RGB images, we generated constellation diagrams from the modulated signals. The diagrams provide the information from the signals in a 2-D representation form. We trained NMformer on 106, 800 modulation images to build the base classifier and only used 3, 000 images to fine-tune for specific tasks. Our proposed model has two different kinds of prediction setups: in-distribution and out-of-distribution. Our model achieves 4.67% higher accuracy than the base classifier when finetuned and tested on high signal-to-noise ratios (SNRs) in-distribution classes. Moreover, the fine-tuned low SNR task achieves a higher accuracy than the base classifier. The fine-tuned classifier becomes much more effective than the base classifier by achieving higher accuracy when predicted, even on unseen data from out-of-distribution classes. Extensive experiments show the effectiveness of NMformer for a wide range of SNRs.
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