Abstract Automatic classification of modulation type in detected signals is an intermediate step between signal detection and demodulation, and is also an essential task for an intelligent receiver in various civil and military applications. In this paper, a new two-stage partially supervised classification method is proposed for Additive White Gaussian Noise (AWGN) channels with unknown signal to noise ratios, in which a system adaptation to the environment Signal-to-Noise Ratios (SNR) and signals classification are combined. System adaptation to the environment SNR enables us to construct a blind classifier to the SNR. In the classification phase of this algorithm, a passive-aggressive online learning algorithm is applied to identify the modulation type of input signals. Simulation results show that the accuracy of the proposed algorithm approaches to a well- trained system in the target SNR, even in low SNRs.

References

[1] Azarmanesh O. and Bilen S., New Results on Two-Stage Novel Modulation Classification Technique for Cognitive Radio Application, in Proceeding of IEEE MILCOM, Maryland, pp. 266-271, 2011.

[2] Azzouz E. and Nandi A., Automatic Identification of Digital Modulation Types, Signal Processing, vol. 47, no. 1, pp. 55-69, 1995.

[3] Azzouz E. and Nandi A., Algorithms for Automatic Modulation Recognition of Communication Signals, IEEE Transaction Communication, vol. 46, no. 4, pp. 431-436, 1998.

[4] Boyd S. and Vandenberghe L., Convex Optimization, Cambridge University Press, 2004.

[5] Burges C., A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121- 167, 1998.

[6] Chandrashekar G. and Sahin F., A Survey on Feature Selection Methods, Computers and Electrical Engineering, vol. 40, no. 1, pp. 16-28, 2014.

[7] Chergui L., Mammar K., and Salim C., Combining Neural Networks for Arabic Handwriting Recognition, The International Arab Journal of Information Technology, vol. 9, no. 6, pp. 588-595, 2012.

[8] Cortes C. and Vapnik V., Support Vector Networks, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.

[9] Crammer K., Dekel O., Keshet J., Shalev-shwartz S., and Singer Y., Online Passive-Aggressive Algorithms, Journal of Machine Learning Research, vol. 7, pp. 551-585, 2006.

[10] Crammer K. and Singer Y., A New Family of Online Algorithms for Category Ranking, Journal of Machine Learning Research, vol. 3, pp. 1025-1058, 2003.

[11] Dobre O., Abdi A., Bar-Ness Y., and Su W., Survey of Automatic Modulation Classification Techniques: Classical Approaches and New Trends, IET Communications, vol. 1, no. 2, pp. 137-156, 2007.

[12] Dulek B., Ozdemir O., Varshney P., and Su W., A Novel Approach to Dictionary Construction for Automatic Modulation Classification, Journal of the Franklin Institute, vol. 351, no. 5, pp. 299-3012, 2014.

[13] Ebrahimzadeh A., A Novel Method for Automatic Modulation Recognition, Applied Soft Computing, vol. 12, no. 1, pp. 453-461, 2012.

[14] Lopatka J. and Macrej P., Automatic Modulation Classification using Statistical Moments and a Fuzzy Classifier, in Proceeding of International Conference on Signal Processing, Beijing, pp. 1500-1506, 2000.

[15] Mobasseri B., Digital Modulation Classification using Constellation Shape, Signal Processing, vol. 80, no. 2, pp. 251-277, 2000.

[16] Proakis J., Digital Communications, McGraw- Hill, 2001.

[17] Rahman M., Kumar M., and Uddin M., Optimum Threshold Parameter Estimation of Wavelet Coefficients using Fisher Discriminant Analysis for Speckle Noise Reduction, The International Arab Journal of Information Technology, vol. 11, no. 6, pp. 573-581, 2014.

[18] Ramkumar B., Automatic Modulation Classification for Cognitive Radios using Cyclic Feature Detection, IEEE Circuits and Systems, vol. 9, no. 2, pp. 27-45, 2009.

[19] Sengur A., Multiclass Least-Squares Support Vector Machines for Analog Modulation Classification, Expert Systems with Applications, vol. 36, no. 3, pp. 6681-6685, 2009.

[20] Swami A. and Sadler B., Hierarchical Digital Modulation Classification using Cumulants, IEEE Transactions on Communications, vol. 48 no. 3, pp. 416-429, 2000.

[21] Vapnik V., Statistical Learning Theory, John Wiley and Sons, 1998.

[22] Wu H., Saquib M., and Yun Z., Novel Automatic Modulation Classification using Cumulant Features for Communications via Multipath Channels, IEEE Transactions on Wireless Communications, vol. 7 no. 8, pp. 3089- 3105, 2008.

[23] Yaqin Z., Guanghui R., Xuexia W., Zhilu W., and Xuemai Gu., Automatic Digital Modulation An SNR Unaware Large Margin Automatic Modulations Classifier in Variable SNR Environments 781 Recognition using Artificial Neural Networks, in International Conference on Neural Networks and Signal Processing, Cairo, pp. 257-260, 2003. Hamidreza Hosseinzadeh is a Ph.D. candidate in Department of Electrical Engineering, in Islamic Azad University, Science and Research Branch, Tehran, Iran. His research interests are signal processing, and pattern recognition. Farbod Razzazi is an Associate Professorin Department of Electrical Engineering, in Islamic Azad University, Science and Research Branch, Tehran, Iran. His research interests are pattern recognition methods.