This letter proposes a cost-efficient convolutional neural network (CNN) for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems. The network architecture is designed with several specific convolutional blocks to concurrently learn the spatiotemporal signal correlations via different asymmetric convolution kernels. Additionally, these blocks are associated with skip connections to preserve more initially residual information at multi-scale feature maps and prevent the vanishing gradient problem. In the experiments, MCNet reaches the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.

中文翻译：

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MCNet：用于稳健自动调制分类的高效 CNN 架构

这封信提出了一种经济高效的卷积神经网络 (CNN)，用于为现代通信系统的认知无线电服务部署的稳健自动调制分类 (AMC)。网络架构设计有几个特定的​​卷积块，通过不同的非对称卷积核同时学习时空信号相关性。此外，这些块与跳过连接相关联，以在多尺度特征图上保留更多初始残差信息并防止梯度消失问题。在实验中，MCNet 在著名的 DeepSig 数据集上以 20 dB SNR 达到了 93.59% 的整体 24 调制分类率。