Modulation classification is an essential step of signal processing and has been regularly applied in the field of tele-communication. Since variations of frequency with respect to time remains a vital distinction among radio signals having different modulation formats, these variations can be used for feature extraction by converting 1-D radio signals into frequency domain. In this paper, we propose a scheme for Automatic Modulation Classification (AMC) using modern architectures of Convolutional Neural Networks (CNN), through generating spectrum images of eleven different modulation types. Additionally, we perform resolution transformation of spectrograms that results up to 99.61% of computational load reduction and 8x faster conversion from the received I/Q data. This proposed AMC is implemented on CPU and GPU, to recognize digital as well as analogue signal modulation schemes on signals. The performance is evaluated on existing CNN models including SqueezeNet, Resnet-50, InceptionResnet-V2, Inception-V3, VGG-16 and Densenet-201. Best results of 91.2% are achieved in presence of AWGN and other noise impairments in the signals, stating that the transformed spectrogram-based AMC has good classification accuracy as the spectral features are highly discriminant, and CNN based models have capability to extract these high-dimensional features. The spectrograms were created under different SNRs ranging from 5 to 30db with a step size of 5db to observe the experimental results at various SNR levels. The proposed methodology is efficient to be applied in wireless communication networks for real-time applications.

翻译：调制分类是信号处理中的关键步骤，在电信领域得到广泛应用。由于不同调制格式的无线电信号在频率随时间变化特性上存在显著差异，通过将一维无线电信号转换至频域，可利用这些差异进行特征提取。本文提出一种基于现代卷积神经网络（CNN）架构的自动调制分类（AMC）方案，通过生成十一种不同调制类型的频谱图像实现分类。此外，我们对谱图进行分辨率变换，使得接收I/Q数据的计算负载降低99.61%，转换速度提升8倍。所提出的AMC方案在CPU和GPU上实现，可识别数字与模拟信号调制格式。在现有CNN模型（包括SqueezeNet、ResNet-50、InceptionResNet-V2、Inception-V3、VGG-16和DenseNet-201）上评估性能。在存在加性高斯白噪声（AWGN）及其他噪声干扰的情况下，最佳分类准确率达91.2%，表明基于变换谱图的AMC具有良好分类精度，因为频谱特征具有高度判别性，且CNN模型能够提取这些高维特征。实验中谱图在信噪比（SNR）范围为5至30dB（步长5dB）的条件下生成，以观察不同SNR水平下的实验结果。该方法可高效应用于无线通信网络中的实时场景。