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A New Intelligent Approach for Recognition of Digital Satellite Signals

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Abstract

In this paper a novel automatic method to identify digital communication signals is presented for different signal to noise ranges (SNRs). The method is based on the idea of optimization of the adaptive neuro-fuzzy inference system (ANFIS) including three major modules: the feature extraction the classification and optimization module. In the feature extraction module, an effective combination of the higher order moments (up to eighth), higher order cumulants (up to eighth) and spectral characteristics are proposed as the efficient features. For classification of the extracted features, ANFIS is investigated as a powerful classifier. In the training of ANFIS, the vector of radius has very important roles for its recognition accuracy. Therefore, in the optimization module, cuckoo optimization algorithm (COA) is proposed for optimization of the classifier to find the optimum value of radius. Experimental results clearly indicate that the proposed hybrid intelligent method has a high classification accuracy to discriminate different types of digital signals even at very low SNRs.

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References

  1. Sue, W., Jefferson, L. X., & Mengchou, Z. (2008). Real-time modulation classification based on maximum likelihood. IEEE Communications Letters, 12, 781–784.

    Google Scholar 

  2. Yang, Y., Chang, J.-N., Liu, J.-C., & Liu, C.-H. (2004). Maximum loglikelihood function-based QAM signal classification over fading channels. Wireless Personal Communications, 28, 77.

    Article  Google Scholar 

  3. Wei, W., & Mendel, J. M. (2000). Maximum-likelihood classification for digital amplitude-phase modulations. IEEE Transactions on Communications, 48(2), 189–193.

    Article  Google Scholar 

  4. Wu, H. C., Saquib, M., & Yun, Z. (2008). Novel automatic modulation classification using cumulant features for communications via multipath channels. IEEE Transactions on Wireless Communications, 7(8), 3098–3105.

    Article  Google Scholar 

  5. Wong, M. L. D., & Nandi, A. K. (2004). Automatic digital modulation recognition using artificial neural network and genetic algorithm. Signal Processing, 84, 351–365.

    Article  MATH  Google Scholar 

  6. Hassan K., Dayoub I., Hammouda W., Berbineau M. (2009) Automatic modulation recognition using wavelet transform and neural network, in: Proceedings of IEEE Conference on Intelligent Transport Systems Telecommunications (pp. 234–238).

  7. Mobasseri, B. G. (2000). Digital modulation classification using constellation shape. Signal Processing, 80, 251–277.

    Article  MATH  Google Scholar 

  8. Deng, H., Doroslovacki, M., Mustafa, H., Jinghao, X., & Sunggy, K. (2002). Automatic digital modulation classification using instantaneous features. Proceedings ICASSP, 4, IV4168.

    Google Scholar 

  9. Azarbad, M. Hakimi, S. Ebrahimzadeh, A. (2012). Automatic recognition of digital communication signal. International Journal of Energy, Information and Communications, 3(4), 21–34.

    Google Scholar 

  10. Avci, E., Hanbay, D., & Varol, A. (2007). An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition. Expert Systems with Applications, 33, 582–589.

    Article  Google Scholar 

  11. HU, Y., Liu, J., & Tan, X. (2010). Digital modulation recognition based on instantaneous information. The Journal of China Universities of Posts and Telecommunications, 17(3), 52–59.

    Article  Google Scholar 

  12. Swami, A., & Sadler, B. M. (2000). Hierarchical digital modulation classification using cumulants. IEEE Transactions on Communications, 48, 416–429.

    Article  Google Scholar 

  13. Lopatka J., and Macrej P. (2000), Automatic modulation classification using statistical moments and a fuzzy classifier, in Proceedings of the 5th International Conference on Signal Processing (ICSP ’00), vol. 3, pp. 1500–1506, Beijing, China.

  14. Liu, A., & ZHU, Q. (2011). Automatic modulation classification based on the combination of clustering and neural network. The Journal of China Universities of Posts and Telecommunications, 18(4), 13–19.

    Article  Google Scholar 

  15. Zhou, X., Wu, Y., & Wang, D. (2011). Application of kernel methods in signals modulation classification. The Journal of China Universities of Posts and Telecommunications, 18(1), 84–90.

    Article  Google Scholar 

  16. Hagedorn, G., James, B., & Miller, C. (1997). Neural network recognition of signal modulation types, Proc. ANNIEC (pp. 170–175).

    Google Scholar 

  17. Nandi, A. K., & Azzouz, E. E. (1998). Algorithms for automatic modulation recognition of communication signals. IEEE Transactions on Communications, 46(4), 431–436.

    Article  Google Scholar 

  18. Zhao, Y., Ren, G., Wang, X., Wu, Z., & Gu. (2003). Automatic digital modulation recognition using artificial neural networks, Proc. ICNNSP (pp. 257–260).

    Google Scholar 

  19. Ebrahimzadeh, A., Azimi, H., & Mirbozorgi, S. A. (2011). Digital communication signals identification using an efficient recognizer. Measurement, 44, 1475–1481.

    Article  Google Scholar 

  20. Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. NJ: Englewood Cliffs.

    MATH  Google Scholar 

  21. Mobasseri, B. G. (2000). Digital modulation classification using constellation shape. Signal Processing, 80(2), 251–277.

    Article  MATH  Google Scholar 

  22. Azarbad M., Ebrahimzade A., Yousefi M. (2011). “Comparison of clustering algorithms for recognition of radio communication signals based on the HOS”, Proceedings of the IASTED International Conference, Signal Processing, Pattern Recognition, and Applications (SPPRA 2011), 10.2316/P.2011.721-009

  23. Ebrahimzade, S. A. (2012). A novel method for automatic modulation recognition. Applied Soft Computing, 12, 453–461.

    Article  Google Scholar 

  24. Ebrahimzadeh, A., & Ghazalian, R. (2011). Blind digital modulation classification in software radio using the optimized classifier and feature subset selection. Engineering Applications of Artificial Intelligence, 24, 50–59.

    Article  Google Scholar 

  25. Kennedy J., Eberhart R. C., and Shi Y. (2001) Swarm Intelligence (Morgan Kaufmann Publishers).

  26. Jang, J. S.R., Sun, C.T. Mizutani, E. (1997): Neuro-fuzzy and soft Jondral. (Prentice Hall).

  27. Sugeno, M., & Kang, G. T. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28, 15–33.

    Article  MATH  MathSciNet  Google Scholar 

  28. Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems Man and Cybernetics, 15, 116–132.

    Article  MATH  Google Scholar 

  29. Takagi T., Sugeno M. (1985). Derivation of fuzzy control rules from humanoperator’s control actions, in: Proc. IFAC Symp. Fuzzy Inform.,Knowledge Representation and Decision Analysis, 55–60.

  30. Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with afuzzy logic controller. International Journal of Man-Machine Studies, 7, 1–13.

    Article  MATH  Google Scholar 

  31. Jang, J. S. R. (1993). ANFIS: Adaptive-Network-based Fuzzy Inference Systems, IEEE Trans. Systems, Man, and Cybernetics, 23(3), 665–685.

    Article  Google Scholar 

  32. Haykin, S. (2003). Neural networks—a comprehensive foundation (2nd ed.). New Delhi: Prentice-Hall of India Pvt. Ltd.

    Google Scholar 

  33. Zurada J.M. (1992) Introduction to Artificial Neural Systems, PWS Publication Company.

  34. Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996). Neural network design. Boston: PWS Publishing.

    Google Scholar 

  35. Chiu, S. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2(3), 267–278.

    Google Scholar 

  36. Chiu, S. (1996). Selecting input variables for fuzzy models. Journal of Intelligent & Fuzzy Systems, 4(4), 243–256.

    Google Scholar 

  37. Buragohain, M., & Mahanta, C. (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8, 609–625.

    Article  Google Scholar 

  38. Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied Soft Computing, 11, 5508–5518.

    Article  Google Scholar 

  39. Hosoz, M., Ertunc, H. M., & Bulgurcu, H. (2011). An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower. Expert Systems with Applications, 38, 14148–14155.

    Google Scholar 

  40. Keles, A., Keles, A., & Yavuz, U. (2011). Expert system based on neuro-fuzzy rules for diagnosis breast cancer. Expert Systems with Applications, 38, 5719–5726.

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, BABOL University of Technology, Mazandaran, Iran

    M. Azarbad, A. Ebrahimzadeh & J. Addeh

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  1. M. Azarbad
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  2. A. Ebrahimzadeh
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  3. J. Addeh
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Correspondence to M. Azarbad.

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Azarbad, M., Ebrahimzadeh, A. & Addeh, J. A New Intelligent Approach for Recognition of Digital Satellite Signals. J Sign Process Syst 79, 75–88 (2015). https://doi.org/10.1007/s11265-013-0829-0

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  • DOI: https://doi.org/10.1007/s11265-013-0829-0

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