Journal
IEEE COMMUNICATIONS LETTERS
Volume 24, Issue 4, Pages 811-815Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2020.2968030
Keywords
Automatic modulation classification; deep learning; convolutional neural network; skip connection
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Funding
- National Research Foundation of Korea (NRF) through Creativity Challenge Researchbased Project [2019R1I1A1A01063781]
- Priority Research Centers Program through the National Research Foundation of Korea (NRF) - Ministry of Education, Science and Technology [2018R1A6A1A03024003]
- National Research Foundation of Korea [22A20152313375] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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