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W3213410040 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>$24times 1$ </tex-math></inline-formula>. The features extracted by SBCNN are converted into heat map images, which showed RGB images in the scale range of −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." @default.
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W3213410040 date "2022-02-01" @default.
W3213410040 modified "2023-10-14" @default.
W3213410040 title "A Hybrid Deep Learning Model for Automatic Modulation Classification" @default.
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W3213410040 doi "https://doi.org/10.1109/lwc.2021.3126821" @default.
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