Deep neural network-based blind modulation classification for fading channels
- Authors
- Lee, Junghwan; Kim, Byeoungdo; Kim, Jaekyum; Yoon, Dongweon; Choi, Jun Won
- Issue Date
- Dec-2017
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Blind modulation classification; Cumulant; Deep neural network; Feature selection; Statistical feature
- Citation
- International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017, v.2017-December, pp.551 - 554
- Indexed
- SCOPUS
- Journal Title
- International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
- Volume
- 2017-December
- Start Page
- 551
- End Page
- 554
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/18564
- DOI
- 10.1109/ICTC.2017.8191038
- Abstract
- 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.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 전기공학전공 > 1. Journal Articles
- 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/18564)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.