TY - GEN
T1 - Distributed automatic modulation classification based on cyclic feature via compressive sensing
AU - Zhou, Lei
AU - Man, Hong
PY - 2013
Y1 - 2013
N2 - Automatic modulation classification (AMC) is an important component in cognitive radio and many efforts have been made to improve the AMC's successful classification rate, especially when the environment is noisy. The cyclic feature has excellent resiliency to noise, so it has been frequently adopted as the feature for AMC. In this paper, in order to enhance the reliability of the system, we propose a distributed AMC scheme based on compressive sensing by taking advantage of the sparse property of cyclic feature map. A novel method based on compressive sensing principle for capturing the prominent peaks of the feature map is introduced, which is able to achieve good modulation recognition performance at sub-Nyquist rates. And a novel neural network fusion strategy is proposed for better cooperation. It is shown that the proposed distributed approach improves the classification rate compared with the single radio and can reduce the required samples compared with traditional sampling scheme.
AB - Automatic modulation classification (AMC) is an important component in cognitive radio and many efforts have been made to improve the AMC's successful classification rate, especially when the environment is noisy. The cyclic feature has excellent resiliency to noise, so it has been frequently adopted as the feature for AMC. In this paper, in order to enhance the reliability of the system, we propose a distributed AMC scheme based on compressive sensing by taking advantage of the sparse property of cyclic feature map. A novel method based on compressive sensing principle for capturing the prominent peaks of the feature map is introduced, which is able to achieve good modulation recognition performance at sub-Nyquist rates. And a novel neural network fusion strategy is proposed for better cooperation. It is shown that the proposed distributed approach improves the classification rate compared with the single radio and can reduce the required samples compared with traditional sampling scheme.
KW - Cognitive radio
KW - Compressive sensing
KW - Cyclostationarity
KW - MLP
KW - Modulation classification
KW - Neural network
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=84897722988&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897722988&partnerID=8YFLogxK
U2 - 10.1109/MILCOM.2013.16
DO - 10.1109/MILCOM.2013.16
M3 - Conference contribution
AN - SCOPUS:84897722988
SN - 9780769551241
T3 - Proceedings - IEEE Military Communications Conference MILCOM
SP - 40
EP - 45
BT - Proceedings - 2013 IEEE Military Communications Conference, MILCOM 2013
T2 - 2013 IEEE Military Communications Conference, MILCOM 2013
Y2 - 18 November 2013 through 20 November 2013
ER -