Robust Automatic Modulation Classification Technique for Fading Channels via Deep Neural Networkopen access
- Authors
- Lee, Jung Hwan; Kim, Jaekyum; Kim, Byeoungdo; Yoon, Dongweon; Choi, Jun Won
- Issue Date
- Sep-2017
- Publisher
- MDPI
- Keywords
- automatic modulation classification; deep neural network; fading channel; feature selection; feature extraction
- Citation
- ENTROPY, v.19, no.9
- Indexed
- SCIE
SCOPUS
- Journal Title
- ENTROPY
- Volume
- 19
- Number
- 9
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/19424
- DOI
- 10.3390/e19090454
- ISSN
- 1099-4300
- Abstract
- In this paper, we propose a deep neural network (DNN)-based automatic modulation classification (AMC) for digital communications. While conventional AMC techniques perform well for additive white Gaussian noise (AWGN) channels, classification accuracy degrades for fading channels where the amplitude and phase of channel gain change in time. The key contributions of this paper are in two phases. First, we analyze the effectiveness of a variety of statistical features for AMC task in fading channels. We reveal that the features that are shown to be effective for fading channels are different from those known to be good for AWGN channels. Second, we introduce a new enhanced AMC technique based on DNN method. We use the extensive and diverse set of statistical features found in our study for the DNN-based classifier. The fully connected feedforward network with four hidden layers are trained to classify the modulation class for several fading scenarios. Numerical evaluation shows that the proposed technique offers significant performance gain over the existing AMC methods in fading channels.
- Files in This Item
-
- 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/19424)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.