Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques — Oak Academic Publishing
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Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques
Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt
,
Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt
1 Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt
2 Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt
Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios.
KeywordsArtificial Intelligence TechniquesClassificationDiscrete Wavelet TransformHigher Order StatisticsMulti-User Chirp Modulation Signals
C. R. C. M. da Silva, W. C. Headley, J. D. Reed and Y. Zhao, “The Applications of Distributed Spectrum Sensing and Available Resources Maps to Cognitive Radio Systems,” Proceedings of Information Theory and Application Workshop, San Diegoq, 27 January-1 February 2008, pp. 53-57.
H. Guldemir and A. Sengur, “Comparison of Clustering Algorithms for Analog Modulation Classification,” Expert Systems with Applications, An International Journal, Vol. 30, No. 4, 2006, pp. 642-649.
A. Ebrahimzadeh and M. Ebrahimzadeh, “An Expert System for Digital Signal Type Classification,” Journal of Electrical Engineering, Vol. 58, No. 6, 2007, pp. 334-341.
X. Zhou, Y. Wu and B. Yang, “Signal Classification Method Based on Support Vector Machine and High-Order Cumulants,” Wireless Sensor Network, Vol. 2, No. 1, 2010, pp. 48-52. doi:10.4236/wsn.2010.21007
A. Ebrhimzadeh, H. Azimi and H. M. Naeemi, “Classification of Communication Signals Using an Optimized Classifier and Efficient Features,” The Arabian Journal for Science and Engineering, Vol. 35, No. 1B, 2010, pp. 225-235.
S. M. Zhou, J. Q. Gan and F. Sepulveda, “Classifying Mental Tasks Based on Features of Higher-Order Statistics from EEG Signals in Brain-Computer Interface,” Information Sciences, Vol. 178, No. 6, 2008, pp. 1629-1640. doi:10.1016/j.ins.2007.11.012
C. K. Chua, V. Chandran, R. U. Acharya and L. C. Min, “Cardiac Health Diagnosis Using Higher Order Spectra and Support Vector Machine,” The Open Medical Informatics Journal, Vol. 3, 2009, pp. 1-8. doi:10.2174/1874431100903010001
M. Kaur and A. S. Arora, “Unsupervised Analysis of Arrhythmias Using K-Means Clustering,” International Journal of Computer Science and Information Technologies, Vol. 1, No. 5, 2010, pp. 417-419.
M. Murugappan, M. Rizon, R. Nagarajan, S. Yaacob, I. Zunaidi and D. Hazry, “EEG Feature Extraction for Classifying Emotions Using FCM and FKM,” International Journal of Computers and Communications, Vol. 1, 2007, pp. 21-25.
N. Ahmadi and R. Berangi, “Symbol Based Modulation Classification using Combination of Fuzzy Clustering and Hierarchical Clustering,” Signal Processing: An International Journal, Vol. 4, No. 2, 2010, pp. 123-137.
M. Sayadi, L. Tlig and F. Fnaiech, “A New Texture Segmentation Method Based on the Fuzzy C-Mean Algorithm and Statistical Features,” Applied Mathematical Sciences, Vol. 1, No. 60, 2007, pp. 2999-3007.
P. Prakasam and M. Madheswaran, “Mary Shift Keying Modulation Scheme Identification Algorithm Using Wavelet Transform and Higher Order Statistical Moment,” Journal of Applied Science, Vol. 8, No. 1, 2008, pp. 112-119. doi:10.3923/jas.2008.112.119
R. M. Al-Makhlasawy, W. Hamouda, M. M. Abd Elnaby, H. A. El-Khobby and F. E. Abd El-Samie, “Automatic Modulation Recognition Using Cepestral Analysis and Neural Networks in Wireless Systems,” 29th National Radio Science Conference, Cairo, 10-12 April 2012.
S. E. El-Khamy H. A. Elsayed and M. M. Rizk, “Classification of Multi-User Chirp Modulation Signals Using Higher Order Cumulant Features and Four Types of Classifiers,” 28th National Radio Science Conference, Cairo, Egypt, 26-28 April 2011.
H. A. Elsayed, S. E. El-Khamy and M. R. Rizk, “Higher Order Statistics Classification of Multi-User Chirp Modulation Signals using Clustering Techniques,” URSI GASS 2011, Istanbul, 13-20 August 2011.
S. E. El-Khamy, H. A. Elsayed and M. R. Rizk, “Bispectrum Classification of Multi-User Chirp Modulation Signals Using Artificial Intelligent Techniques,” WorldComp 2011, Las Vegas, 18-21 July 2011.
S. E. El-Khamy and M. B. Al-Ghoniemy, “The Wavelet Transform: A Review and Application to Enhanced Data Storage Reduction in Mismatched Filter Receivers,” Proceedings of the Thirteenth National Radio Science Conference, Cairo, 19-21 March 1996.
M. P. Fargues and G. Hatzichristos, “A Hierarchical Approach to the Classification of Digital Modulation Types In Multipath Environments,” Conference Record of the Thirty-Fifth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, 4-7 November 2001.
C. L. Nikias and J. M. Mendel, “Signal Processing with Higher Order Spectra,” IEEE Signal Processing Magazine, Vol. 10, No. 3, 1993, pp. 10-37. doi:10.1109/79.221324
S. E. El-Khamy, “Matched Swept-Frequency Digital Modulation for Binary Signaling in Inhomogeneous Dispersive Media,” IEEE Transactions on Antennas and Propagation, Vol. 28, No. 1, 1980, pp. 29-35. doi:10.1109/TAP.1980.1142283
S. E. El-Khamy and S. E. Shaaban, “Matched Chirp Modulation: Detection and Performance in Dispersive Communication Channels,” IEEE Transactions on Communications, Vol. 36, No. 4, 1988, pp. 506-509. doi:10.1109/26.2777
S. E. El-Khamy, S. E. Shaaban and E. A. Thabet, “Multi-User Chirp Modulation Signals (M-CM) for Efficient Multiple Access Communication Systems,” Proceedings of the Thirteenth National Radio Science Conference, Cairo, 19-21 March 1996.
S. E. El-Khamy, S. E. Shaaban and E. A. Thabet, “Efficient Multiple-Access Communications Using Multi-User Chirp Modulation Signals,” IEEE 4th International Symposium on Spread Spectrum Techniques and Applications Proceedings, Mainz, 22-25 September 1996, pp. 1209-1213.
S. E. El-Khamy, S. E. Shaaban and E. A. Thabet, “Frequency Hopped-Chirp Modulation (FH-CM) for Multi-User Signaling in Multipath Dispersive Media,” IEEE Antennas and Propagation Society International Symposium, Orlando, 11-16 July 1999, pp. 396-399.
C. L. Nikias and A. P. Petropulu, “Higher-Order Spectra Analysis: A Nonlinear Signal Processing Framework,” PTR Prentice-Hall, Englewood Cliffs, 1993.
High Order Spectral Analysis Matlab Toolbox (9 February 2011). http://www.mathworks.com/matlabcentral/fileexchange/3013
A. V. Rosti, “Statistical Methods in Modulation Classification,” Master’s Thesis, Tampere University of Technology, Tampere, 1999.
H. Yoshioka, “A Fast Modulation Recognition Technique Using Nearest Neighbor Rules with Optimized Threshold for Modulation Classification in Rayleigh Fading Channels,” Proceedings of WPMC, Honolulu, 27-30 October 2002, pp. 1049-1052.
C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998, pp. 121-167. doi:10.1023/A:1009715923555
A. Swami and B. M. Sadler, “Hierarchical Digital Modulation Classification Using Cumulants,” IEEE Transactions on Communications, Vol. 48, No. 3, 2000, pp. 416-429. doi:10.1109/26.837045