The Performance Improvement of Automatic Modulation Recognition Using Simple Feature Manipulation, Analysis of the HOS, and Voted Decision
The use of High Order Statistics (HOS) analysis is
expected to provide so many candidates of features that can be selected for pattern recognition. More candidates of the feature can
be extracted using simple manipulation through a specific mathematical function prior to the HOS analysis. Feature extraction
method using HOS analysis combined with Difference to the Nth-Power manipulation has been examined in application for Automatic
Modulation Recognition (AMR) to perform scheme recognition of three digital modulation signal, i.e. QPSK-16QAM-64QAM in the
AWGN transmission channel. The simulation results is reported
when the analysis of HOS up to order-12 and the manipulation of Difference to the Nth-Power up to N = 4. The obtained accuracy rate
of AMR using the method of Simple Decision obtained 90% in SNR > 10 dB in its classifier, while using the method of Voted Decision is
96% in SNR > 2 dB.
[1] E. E. Azzouz, A. K. Nandi, Automatic Modulation Recognition of
Communication Signals (Boston- Dordrecht-London, Kluwer Academic
Publisher, 2003).
[2] A. Ebrahimzadeh, M. Ardebilipour, & A. Movahedian, Automatic
Digital Signal Types Recognition Using SI-NN and HOS, Advanced
Topics in Signal Processing, Proc. IEEE Conf. on ICC, 2007.
[3] A. Ebrahimzadeh, S. A. Seyedin, & M. Dehghan, Digital-Signal-Type
Identification Using an Efficient Identifier, EURASIP Journal on Advances in Signal Processing, Vol. 2007, Article ID 37690.
[4] Jie Li, Jun Wang, Xiaoyan Fan, & Yi Zhang, Automatic Digital Modulation Recognition Using Feature Subset Selection, Proc.
Progress in Electromagnetics Research Symp., Hangzhou, China,
March 24-28, 2008
[5] O. A. Dobre, Y. Bar-Ness, & Wei Su, Higher-Order Cyclic Cumulants
for High Order Modulation Classification, Proc. IEEE Conf. on MILCOM, 2003.
[6] O. A. Dobre, Y. Bar-Ness, & Wei Su, Robust QAM Modulation Classification Algorithm Using Cyclic Cumulants, Proc. IEEE Conf. on WCNC, 2004.
[1] E. E. Azzouz, A. K. Nandi, Automatic Modulation Recognition of
Communication Signals (Boston- Dordrecht-London, Kluwer Academic
Publisher, 2003).
[2] A. Ebrahimzadeh, M. Ardebilipour, & A. Movahedian, Automatic
Digital Signal Types Recognition Using SI-NN and HOS, Advanced
Topics in Signal Processing, Proc. IEEE Conf. on ICC, 2007.
[3] A. Ebrahimzadeh, S. A. Seyedin, & M. Dehghan, Digital-Signal-Type
Identification Using an Efficient Identifier, EURASIP Journal on Advances in Signal Processing, Vol. 2007, Article ID 37690.
[4] Jie Li, Jun Wang, Xiaoyan Fan, & Yi Zhang, Automatic Digital Modulation Recognition Using Feature Subset Selection, Proc.
Progress in Electromagnetics Research Symp., Hangzhou, China,
March 24-28, 2008
[5] O. A. Dobre, Y. Bar-Ness, & Wei Su, Higher-Order Cyclic Cumulants
for High Order Modulation Classification, Proc. IEEE Conf. on MILCOM, 2003.
[6] O. A. Dobre, Y. Bar-Ness, & Wei Su, Robust QAM Modulation Classification Algorithm Using Cyclic Cumulants, Proc. IEEE Conf. on WCNC, 2004.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51974", author = "Heroe Wijanto and Sugihartono and Suhartono Tjondronegoro and Kuspriyanto", title = "The Performance Improvement of Automatic Modulation Recognition Using Simple Feature Manipulation, Analysis of the HOS, and Voted Decision", abstract = "The use of High Order Statistics (HOS) analysis is
expected to provide so many candidates of features that can be selected for pattern recognition. More candidates of the feature can
be extracted using simple manipulation through a specific mathematical function prior to the HOS analysis. Feature extraction
method using HOS analysis combined with Difference to the Nth-Power manipulation has been examined in application for Automatic
Modulation Recognition (AMR) to perform scheme recognition of three digital modulation signal, i.e. QPSK-16QAM-64QAM in the
AWGN transmission channel. The simulation results is reported
when the analysis of HOS up to order-12 and the manipulation of Difference to the Nth-Power up to N = 4. The obtained accuracy rate
of AMR using the method of Simple Decision obtained 90% in SNR > 10 dB in its classifier, while using the method of Voted Decision is
96% in SNR > 2 dB.", keywords = "modulation, automatic modulation recognition, feature analysis, feature manipulation.", volume = "3", number = "10", pages = "1800-5", }