Radio spectrum monitoring in contested environments motivates the need for reliable automatic signal classification technology. Prior work highlights deep learning as a promising approach, but existing models depend on brute-force Doppler augmentation to achieve real-world generalization, which undermines both training efficiency and interpretability. In this paper, we propose a convolutional neural network (CNN) architecture with complex-valued layers that exploits convolutional shift equivariance in the frequency domain. To establish provable frequency bin shift invariance, we use adaptive polyphase sampling (APS) as pooling layers followed by a global average pooling layer at the end of the network. Using a synthetic dataset of common interference signals, experimental results demonstrate that unlike a vanilla CNN, our model maintains consistent classification accuracy with and without random Doppler shifts despite being trained on no Doppler-shifted examples. Overall, our method establishes an invariance-driven framework for signal classification that offers provable robustness against real-world effects.

翻译：在对抗环境下的无线电频谱监测，推动了对可靠自动信号分类技术的需求。先前的研究强调深度学习是一种有前景的方法，但现有模型依赖暴力多普勒增强来实现现实世界的泛化，这既损害了训练效率又削弱了可解释性。在本文中，我们提出了一种具有复数层卷积神经网络（CNN）架构，该架构利用频域中的卷积移位等变性。为了建立可证明的频率仓移位不变性，我们使用自适应多相采样（APS）作为池化层，并在网络末端接一个全局平均池化层。使用常见干扰信号的合成数据集，实验结果表明，与普通CNN不同，我们的模型在有无随机多普勒频移的情况下均能保持一致的分类准确率，尽管训练时未使用任何多普勒频移样本。总体而言，我们的方法建立了一个面向信号分类的不变性驱动框架，提供了针对现实世界效应的可证明鲁棒性。