A Hybrid Deep Learning Model for Automatic Modulation Classification

Abstract

Automatic modulation classification (AMC) is one of the major challenges for cognitive radio (CR), which can enhance the spectrum utilization efficiency. In this study, a hybrid signal and image-based deep learning model is designed for AMC in CR. A convolutional neural network (CNN) is applied in both the deep learning models. The signal-based CNN (SBCNN) is designed with the optimal filter size for the prediction accuracy, which is used as a pre-training deep learning network to extract features with size <inline-formula> <tex-math notation="LaTeX">$24\times 1$ </tex-math></inline-formula>. The features extracted by SBCNN are converted into heat map images, which showed RGB images in the scale range of &#x2212;30 to &#x002B;30. Finally, the images are utilized for training and testing the image-based CNN (IBCNN). The dataset used for the experiment is DeepSig: RADIOML2018.01A, which is the latest version. For the IBCNN, the prediction accuracy is 1.96&#x0025;, 7.99&#x0025;, and 4.63&#x0025; higher at signal-to-noise ratio (SNR) 10 dB, and 3.26&#x0025;, 6.4&#x0025;, and 4.13&#x0025; higher at SNR 0 dB as compared to conventional models: ECNN, SCGNet, and LCNN, respectively.