상세 보기
Deep neural network-based blind modulation classification for fading channels
- Lee, Junghwan;
- Kim, Byeoungdo;
- Kim, Jaekyum;
- Yoon, Dongweon;
- Choi, Jun Won
Citations
SCOPUS
32초록
In this paper, we propose high performance blind modulation classification (BMC) technique based on deep neural network (DNN) for fading channels. First, we provide the large and diverse set of the features that exhibit statistical relevance to modulation class in fading channels. Then, we use those features to train the DNN to classify the modulation class. Owing to the capability of DNN to learn the complex structure in high dimensional feature space, the proposed scheme achieves the excellent classification accuracy using a number of features in challenging fading environments. Numerical evaluation demonstrates the superiority of the proposed technique over the existing BMC methods.
키워드
Blind modulation classification; Cumulant; Deep neural network; Feature selection; Statistical feature; Classification (of information); Fading channels; Feature extraction; Modulation; Numerical methods; Blind modulation classifications; Classification accuracy; Complex structure; Cumulants; Fading environment; High-dimensional feature space; Statistical features; Deep neural networks
- 제목
- Deep neural network-based blind modulation classification for fading channels
- 저자
- Lee, Junghwan; Kim, Byeoungdo; Kim, Jaekyum; Yoon, Dongweon; Choi, Jun Won
- 발행일
- 2017-12
- 유형
- Conference Paper
- 저널명
- International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
- 권
- 2017-December
- 페이지
- 551 ~ 554