URL : https://ieeexplore.ieee.org/document/9610127

Authors : Seung-Hwan Kim; Chang-Bae Moon; Jae-Woo Kim; Dong-Seong Kim

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 24×1 . The features extracted by SBCNN are converted into heat map images, which showed RGB images in the scale range of −30 to +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%, 7.99%, and 4.63% higher at signal-to-noise ratio (SNR) 10 dB, and 3.26%, 6.4%, and 4.13% higher at SNR 0 dB as compared to conventional models: ECNN, SCGNet, and LCNN, respectively.