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Deep Learning-Based Modulation Classification Leveraging Dual-Type Image
- Authors
- Cho, Yunseol; Kim, Hanvit; Park, Hyunwoo; Park, Jiyeon; Ji, Younggun; Ju, Hyungjun; Choi, Jaekark; Im, Sanghun; Kim, Kihun; Kim, Sunwoo
- Issue Date
- Jan-2025
- Publisher
- IEEE Computer Society
- Keywords
- convolutional neural network; Modulation classification; overlapped unknown signal
- Citation
- International Conference on ICT Convergence, pp 1298 - 1301
- Pages
- 4
- Indexed
- SCOPUS
- Journal Title
- International Conference on ICT Convergence
- Start Page
- 1298
- End Page
- 1301
- ISSN
- 2162-1233
2162-1241
- Abstract
- In this paper, we present an automatic modulation classification (AMC) algorithm for identifying overlapped signals. The proposed algorithm leverages dual-type images as deep learning input data, which is composed of spectrogram and signal images. The dual-type images have the features of both individual images, such as frequency change over time and information on amplitude and phase. We improve modulation classification performance by reflecting various features of a single signal. The simulation results show that the proposed algorithm has accurate classification performance compared to single-type input images, especially in analog modulation classification.
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Related Researcher
- Kim, Sunwoo
- COLLEGE OF ENGINEERING (SCHOOL OF ELECTRONIC ENGINEERING)