TY - GEN
T1 - Wavelet cyclic feature based automatic modulation recognition using nonuniform compressive samples
AU - Zhou, Lei
AU - Man, Hong
PY - 2013
Y1 - 2013
N2 - Cyclic spectrum feature is one of the most popular features used in automatic modulation recognition (AMR) due to its excellent resiliency to noise. However, extracting cyclic features from wireless signals always requires at least Nyquist rate in traditional. What's more, to better capture cyclostationarity for modulation classification, the sampling rates typically used are higher than Nyquist rate. In this work, a novel AMR method based on compressive sensing principle is introduced, which is able to achieve good modulation recognition performance at sub-Nyquist rates. A new wavelet cyclic feature (WCF) is proposed to reduce the complexity of calculating classical cyclic spectrum. The relationship between nonuniform compressive samples and the WCF is established. A modified compressive sensing reconstruction algorithm is proposed to capture a small subset of magnitude peaks in WCF, which is sufficient for satisfactory modulation recognition. A hierarchical feature reduction method is employed for further reducing data dimension. Four digital modulation types, including BPSK, QPSK, MSK and 2FSK, are investigated and the simulation results show that the AMR with nonuniform compressive samples in sub-Nyquist rate outperforms the one based on classical Nyquist sampling rate.
AB - Cyclic spectrum feature is one of the most popular features used in automatic modulation recognition (AMR) due to its excellent resiliency to noise. However, extracting cyclic features from wireless signals always requires at least Nyquist rate in traditional. What's more, to better capture cyclostationarity for modulation classification, the sampling rates typically used are higher than Nyquist rate. In this work, a novel AMR method based on compressive sensing principle is introduced, which is able to achieve good modulation recognition performance at sub-Nyquist rates. A new wavelet cyclic feature (WCF) is proposed to reduce the complexity of calculating classical cyclic spectrum. The relationship between nonuniform compressive samples and the WCF is established. A modified compressive sensing reconstruction algorithm is proposed to capture a small subset of magnitude peaks in WCF, which is sufficient for satisfactory modulation recognition. A hierarchical feature reduction method is employed for further reducing data dimension. Four digital modulation types, including BPSK, QPSK, MSK and 2FSK, are investigated and the simulation results show that the AMR with nonuniform compressive samples in sub-Nyquist rate outperforms the one based on classical Nyquist sampling rate.
KW - Cognitive radio
KW - Compressive sensing
KW - Cyclostationarity
KW - Modulation classification
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=84893334260&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893334260&partnerID=8YFLogxK
U2 - 10.1109/VTCFall.2013.6692456
DO - 10.1109/VTCFall.2013.6692456
M3 - Conference contribution
AN - SCOPUS:84893334260
SN - 9781467361873
T3 - IEEE Vehicular Technology Conference
BT - 2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013
T2 - 2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013
Y2 - 2 September 2013 through 5 September 2013
ER -