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Intelligent Decision Making System for Digital Modulation Scheme Classification in Software Radio Using Wavelet Transform and Higher Order Statistical Moments

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Abstract

This paper proposes a neural network (NN) based intelligent decision making system for digital modulation classification using wavelet transform, histogram peak and higher order statistical moments. The decision making system is developed to classify the modulation schemes buried in additive white Gaussian noise and channel interference utilizing NN classifier. The performance is verified and validated for M-ary PSK, M-ary FSK, M-ary QAM and GMSK modulation schemes by examining the receiver operating characteristics, confusion matrix and probability of correct identification for various signal-to-noise ratios (SNR) and also for various decision parameters. The performance of the proposed system also has been compared with existing methods and found that this method can be considered as reliable classification method for Digital Modulation Scheme with lower SNR upto  − 5 dB.

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References

  1. Azzouz E.E., Nandi A.K. (1996) Automatic modulation recognition of communication signals. Kluwer Academic Publishers, Norwell

    Google Scholar 

  2. Dobre O.A., Bar-Ness Y., Su W. (2007) A survey of automatic modulation classification techniques: Classical approaches and new trends. IET Communication 1(2): 137–156

    Article  Google Scholar 

  3. Druckmann, I., Plotkim, E. I., & Swamy, M. N. S. (1998). Automatic modulation type classification. In IEEE Canadian Conference on Electrical and Computer Engineering (Vol. 1, pp. 65–68).

  4. Lopatka, J., & Pedzisa, M. (2000). Automatic modulation classification using statistical moments and a fuzzy classifier. In 5th International Conference on Signal Processing Proceedings, WCCC-ICSP 2000 (Vol. 3, pp. 1500–1506).

  5. Callaghan T.G., Pery J.L., Tjho J.K. (1995) Sampling and algorithms aid modulation recognition. Microwave & RF 24(9): 117–179

    Google Scholar 

  6. Shimbo, D., Oka, I., & Ata, S. (2007). Modulation classification using joint moments with linear transform. In IEEE Proceedings, Radio and Wireless Symposium (pp. 567-570).

  7. Jondral, F. (1989). Foundations of automatic modulation classification. ITG—Fachbericht (Vol. 107, pp. 201–206).

  8. Beran R. (1977) Minimum Hellinger distance estimates for parametric models. Annals of Statistics 5: 445–463

    Article  MATH  MathSciNet  Google Scholar 

  9. Shklyaeva A., Kovar P., Kubanek D. (2007) Classification of digital modulations mainly used in mobile radio networks by means of spectrogram analysis. FIP International Federation for Information Processing 245: 341–348

    Google Scholar 

  10. Hero, A. O., III, & Hadinejad-Mahram, H. (1998). Digital modulation classification using power moment matrices. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (Vol. 6, pp. 3285–3288).

  11. Aiello, A., Grimaldi, D., & Rapuno, S. (2001). GMSK neural network based demodulator. In International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (pp. 2–5).

  12. Nolan, K. E., & Doyle, L., Mackenzia, P., & O’Mahory, D. (2002). Modulation scheme recognition for 4G software radio wireless. In Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition, and Application (pp. 25–31).

  13. Park, C.-S., & Kim, D. Y. (2007). Modulation classification of analog and digital signals using neural network and support vector machine. In Lecture Notes in Computer Science (Vol. 4493, pp. 368–373).

  14. Lallo, P. (1999). Signal classification by discrete fourier transform. In Proceedings of the IEEE Conference—MILCOM, (pp. 197–201).

  15. Schreyogg, C., & Reichert, J. (1997). Modulation classification of QAM schemes using the DFT of phase histogram combined with modulus information. In Proceedings of the IEEE Conference—MILCOM (pp. 1372–1376).

  16. Liang Hong, & Ho, K. C. (1999). Identification of digital modulation types using the wavelet transform. In Proceedings of the IEEE confrence—MILCOM (pp. 427–431).

  17. Lin, Y. C., & Jay Kuo, C.-C. (1994). Modulation classification using wavelet transform. Proceedings SPIE, Wavelet Applications in Signal and Image Processing II (Vol. 2303, pp. 260–271).

  18. Pavlik R. (2005) Binary PSK/CPFSK and MSK bandpass modulation identifier based on the complex Shannon Wavelet Transform. Journal of Electrical Engineering 56(3–4): 71–77

    Google Scholar 

  19. Wei X.w., Cao Z.g. (2006) Fast identification of digital amplitude modulation level at low signal- to-noise ratio. Frontiers of Electrical and Electronic Engineering in China 1(4): 421–424

    Article  Google Scholar 

  20. Prakasam, P., & Madheswaran, M. (2007). Automatic modulation identification of QPSK and GMSK using wavelet transform for adaptive demodulator in SDR. In IEEE Conference Proceedings on Signal Processing, Communications and Networking (IEEE-ICSCN 2007) (pp 507–511). February 2007, Chennai.

  21. Haykin S. (2005) Communication Systems. Wiley Eastern Limited, New Delhi

    Google Scholar 

  22. Prakasam, P., & Madheswaran, M. (2008). Digital modulation identification model using wavelet transform and statistical parameters. Journal of Computer Systems, Networks and Communications, 8. doi:10.1155/2008/175236.

  23. Chan Y.T. (1995) Wavelet Basics. Kluwer Academic Publishers, Boston

    Google Scholar 

  24. Le Guen, D., & Mansour, A. (2002). Automatic recognition algorithm for digitally modulated signals. In Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition & Applications (pp. 32–37).

  25. Sapiano, P. C., Martin, J., & Holbeche, R. (1995). Classification of PSK signals using the DFT of phase histogram. In Conference Proceedings—ICASSP (pp. 1868–1871).

  26. Ho, K. C., Prokopiw, W., & Chan, Y. T. (1995). Modulation identification by the wavelet transform. In Proceedings of the IEEE Conference—MILCOM (pp. 886–890).

  27. Martret, C., & Boiteau, D. M. (1997). Modulation classification by means of different order statistical moments. In Proceedings of the IEEE Conference—MILCOM (pp. 1387–1391).

  28. Dobre, O. A., Bar-Ness, Y., & Su, W. (2003). Higher-order cyclic cumulants for high order modulation classification. In Proceedings of the IEEE Conference—MILCOM (pp. 112–117).

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  1. Centre for Advanced Research, Department of Electronics and Communication Engineering, Muthayammal Engineering College, Rasipuram, 637 408, TN, India

    P. Prakasam & M. Madheswaran

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  1. P. Prakasam
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  2. M. Madheswaran
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Correspondence to M. Madheswaran.

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Prakasam, P., Madheswaran, M. Intelligent Decision Making System for Digital Modulation Scheme Classification in Software Radio Using Wavelet Transform and Higher Order Statistical Moments. Wireless Pers Commun 50, 509–528 (2009). https://doi.org/10.1007/s11277-008-9621-z

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  • DOI: https://doi.org/10.1007/s11277-008-9621-z

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