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.

翻译：调制分类是一项极具挑战性的任务，因为信号与各种环境噪声交织在一起。需要能够在不增加去噪等额外步骤（这会引入计算复杂度）的情况下对它们进行分类的方法。在本研究中，我们提出了一种基于视觉Transformer（ViT）的模型，命名为NMformer，用于预测无线通信中不同噪声水平的信道调制图像。由于ViT对RGB图像最为有效，我们从调制信号生成了星座图。这些图表以二维表示形式提供了信号中的信息。我们在106,800张调制图像上训练NMformer以构建基础分类器，并仅使用3,000张图像对特定任务进行微调。我们提出的模型有两种不同的预测设置：分布内和分布外。当在分布内类别的高信噪比（SNR）下进行微调和测试时，我们的模型比基础分类器实现了4.67%的准确率提升。此外，微调后的低SNNR任务的准确率也高于基础分类器。通过实现更高的预测准确率，即使在来自分布外类别的未见数据上，微调后的分类器也变得比基础分类器有效得多。大量实验证明了NMformer在广泛SNR范围内的有效性。