A novel modulation classification algorithm based on daubechies5 wavelet and fractional fourier transform in cognitive radio

Modulation classification is one of the most important tasks in signal waveform identification. In this paper, We address the problem of digital modulation classification in Additive White Gaussian Noise and Rayleigh fading channel. In order to classify digital modulation signal reliably, we propose an algorithm for automatic modulation recognition in this paper. We verify this algorithm using higher order statistical moments of wavelet transform and Fractional Fourier Transform as a feature set. Through this algorithm, we can discriminate 2ASK 2FSK BPSK and 16QAM without any prior signal information. In addition pre-processing and features subset selection using principal component analysis will reduce the network complexity and the recognizers performance is no significant decrease. The performance of the identification scheme is investigated through the simulations. Simulation results show that the performance of the proposed modulation classifier is superior to other existing signal classifiers.

[1]  Jean-Louis Lacoume,et al.  Modulation classification based on a maximum-likelihood receiver in the cyclic-HOS domain , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[2]  Iyad Dayoub,et al.  A Blind Modulation Type Detector for DPRS Standard , 2007, Wirel. Pers. Commun..

[3]  V. Namias.  The Fractional Order Fourier Transform and its Application to Quantum Mechanics , 1980 .

[4]  Jian Liu,et al.  Modulation classification based on bispectrum and sparse representation in cognitive radio , 2011, 2011 IEEE 13th International Conference on Communication Technology.

[5]  Dongfeng Yuan,et al.  Optimizing cooperative video streaming in wireless networks , 2011, 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[6]  Muthusamy Madheswaran,et al.  Digital Modulation Identification Model Using Wavelet Transform and Statistical Parameters , 2008, J. Comput. Networks Commun..

[7]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[8]  Nader M. Namazi,et al.  Classification of multiple M-ary frequency-shift keying signals over a Rayleigh fading channel , 2002, IEEE Trans. Commun..

[9]  Achilleas Anastasopoulos,et al.  Likelihood ratio tests for modulation classification , 2000, MILCOM 2000 Proceedings. 21st Century Military Communications. Architectures and Technologies for Information Superiority (Cat. No.00CH37155).

[10]  Reza Ghaderi,et al.  An Intelligent System for Classification of the Communication formats Using PSO , 2008, Informatica.

[11]  Dongfeng Yuan,et al.  On the Effect of Cooperative Relaying on the Performance of Video Streaming Applications in Cognitive Radio Networks , 2011, 2011 IEEE International Conference on Communications (ICC).

[12]  K. Sam Shanmugan,et al.  Simulation of Communication Systems , 1992 .

[13]  Dongfeng Yuan,et al.  Distributed spectrum management and relay selection in interference-limited cooperative wireless networks , 2011, MobiCom.

[14]  Elsayed Elsayed Azzouz,et al.  Algorithms for automatic modulation recognition of communication signals , 1998, IEEE Trans. Commun..

[15]  Geoffrey Ye Li,et al.  A Probability-Based Spectrum Sensing Scheme for Cognitive Radio , 2008, 2008 IEEE International Conference on Communications.

[16]  Bijan G. Mobasseri,et al.  Digital modulation classification using constellation shape , 2000, Signal Process..

[17]  J. Sills.  Maximum-likelihood modulation classification for PSK/QAM , 1999, MILCOM 1999. IEEE Military Communications. Conference Proceedings (Cat. No.99CH36341).

[18]  K. C. Ho,et al.  Identification of digital modulation types using the wavelet transform , 1999, MILCOM 1999. IEEE Military Communications. Conference Proceedings (Cat. No.99CH36341).