TY - GEN
T1 - Deep learning based automatic modulation classification for varying SNR environment
AU - Xie, Xiaojuan
AU - Ni, Yanqin
AU - Peng, Shengliang
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - Automatic modulation classification (AMC) is a crucial task for various communications applications. Deep learning (DL) based classifier is emerging as a prevalent choice for AMC. Previous research on DL based AMC usually assumes an environment of fixed signal to noise ratio (SNR). This paper considers DL based AMC for varying SNR environment. Two algorithms, including M2M4-aided algorithm and multi-label DL based algorithm, are proposed to combat the varying SNR. The former utilizes an M2M4 estimator to estimate SNR, according to which a proper trained DL model can be selected for AMC. The latter exploits multi-label DL to train a model, with which SNR scenario and modulation type can be inferred simultaneously. Experiment results show that the performance of both algorithms is fairly close to that of DL based AMC under fixed SNR environment.
AB - Automatic modulation classification (AMC) is a crucial task for various communications applications. Deep learning (DL) based classifier is emerging as a prevalent choice for AMC. Previous research on DL based AMC usually assumes an environment of fixed signal to noise ratio (SNR). This paper considers DL based AMC for varying SNR environment. Two algorithms, including M2M4-aided algorithm and multi-label DL based algorithm, are proposed to combat the varying SNR. The former utilizes an M2M4 estimator to estimate SNR, according to which a proper trained DL model can be selected for AMC. The latter exploits multi-label DL to train a model, with which SNR scenario and modulation type can be inferred simultaneously. Experiment results show that the performance of both algorithms is fairly close to that of DL based AMC under fixed SNR environment.
UR - http://www.scopus.com/inward/record.url?scp=85070369675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070369675&partnerID=8YFLogxK
U2 - 10.1109/WOCC.2019.8770611
DO - 10.1109/WOCC.2019.8770611
M3 - Conference contribution
AN - SCOPUS:85070369675
T3 - 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings
BT - 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings
T2 - 28th Wireless and Optical Communications Conference, WOCC 2019
Y2 - 9 May 2019 through 10 May 2019
ER -