TY - JOUR
T1 - Modulation Classification Based on Signal Constellation Diagrams and Deep Learning
AU - Peng, Shengliang
AU - Jiang, Hanyu
AU - Wang, Huaxia
AU - Alwageed, Hathal
AU - Zhou, Yu
AU - Sebdani, Marjan Mazrouei
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification.
AB - Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification.
KW - Convolutional neural network (CNN)
KW - data conversion
KW - deep learning (DL)
KW - modulation classification
UR - http://www.scopus.com/inward/record.url?scp=85050605304&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050605304&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2018.2850703
DO - 10.1109/TNNLS.2018.2850703
M3 - Article
C2 - 30047904
AN - SCOPUS:85050605304
SN - 2162-237X
VL - 30
SP - 718
EP - 727
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 3
M1 - 8418751
ER -