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Deep Learning-Based Modulation Classification Leveraging Dual-Type Image
- Cho, Yunseol;
- Kim, Hanvit;
- Park, Hyunwoo;
- Park, Jiyeon;
- Ji, Younggun;
- ... Kim, Sunwoo;
- 외 4명
Citations
SCOPUS
1초록
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.
키워드
convolutional neural network; Modulation classification; overlapped unknown signal; Convolutional neural networks; Deep learning
- 제목
- Deep Learning-Based Modulation Classification Leveraging Dual-Type Image
- 저자
- Cho, Yunseol; Kim, Hanvit; Park, Hyunwoo; Park, Jiyeon; Ji, Younggun; Ju, Hyungjun; Choi, Jaekark; Im, Sanghun; Kim, Kihun; Kim, Sunwoo
- 발행일
- 2025-01
- 유형
- Conference paper
- 저널명
- International Conference on ICT Convergence
- 페이지
- 1298 ~ 1301