iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s11277-017-4378-x
A Novel Approach for Automatic Modulation Classification via Hidden Markov Models and Gabor Features | Wireless Personal Communications Skip to main content

Advertisement

Springer Nature Link
Log in

A Novel Approach for Automatic Modulation Classification via Hidden Markov Models and Gabor Features

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this paper, a novel approach is proposed for classification of pulse amplitude modulated (PAM) and quadrature amplitude modulated (QAM) signals. The automatic modulation classification is the intermediate step between detection and demodulation of the signal. In this paper, we propose a classifier for digital modulated signals such as PAM and QAM that differs with the existing classifiers. The gabor parameters have been used as input features and the proposed classifier uses hidden markov model in conjunction with genetic algorithm (GA). The fitness function for the genetic algorithm is probability of observation sequence given the model. The objective is to maximize the probability of observation using Baum–Welch algorithm and GA. To improve the classification accuracy, classification process has been divided in two phases. Simulation results shows the significant performance improvement while compare with other existing techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

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

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

    Article  Google Scholar 

  3. Ramkumar, B. (2009). Automatic modulation classification for cognitive radios using cyclic feature detection. IEEE Circuits and Systems Magazine, 9, 27–45.

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Sue, W., Jefferson, L. X., & Mengchou, Z. (2008). Real-time modulation classification based on maximum likelihood. IEEE Communications letters, 12(11), 781–784.

    Google Scholar 

  6. Yang, Y., Chang, J. N., Liu, J. C., & Liu, C. H. (2007). Maximum log-likelihood function based QAM signal classification over fading channel. Wireless Personal Communications, 28, 77–94.

    Article  Google Scholar 

  7. Panagiotou, P., Anastasopoulos, A., & Polydoros, A. (2000). Likelihood ratio tests for modulation classification. In Proceedings of the IEEE MILCOM, vol. 2, (pp. 670–674).

  8. Hameed, F., Dobre, O., & Popescu, D. (2009). On the likelihood based approach to modulation classification. IEEE Transactions on Wireless Communication, 8(12), 5884–5892.

    Article  Google Scholar 

  9. Ramezani Kebrya, A., Kim, I.-M., Kim, D. I., Chan, F., & Inkol, R. (2013). Likelihood based modulation classification for multiple antenna receiver. IEEE Transactions on Communications, 61(9), 3816–3829.

    Article  Google Scholar 

  10. Sergienko, A. B., & Osipov, A. V. (2014). Digital modulation recognition using circular harmonic approximation of likelihood function. In IEEE international conference on acoustic, speech and signal processing (ICASSP), (pp. 3460–3463).

  11. Zhu, Z., & Nandi, A. K. (2014). Blind digital modulation classification using minimum distance centroid estimator and non-parametric likelihood function. IEEE Transactions on Wireless Communication, 13(8), 4483–4494.

    Article  Google Scholar 

  12. Lin, Y. C., & Jay Kuo, C. C. (1997). Classification of quadrature amplitude modulated (QAM) signals via sequential probability ratio test (SPRT). Signal Processing, 60, 263–280.

    Article  MATH  Google Scholar 

  13. Soltan Mohammadi, E., & Naraghi Pour, M. (2013). Blind modulation classification over fading channels using expectation maximization. IEEE Communication Letters, 17(9), 1692–1695.

    Article  Google Scholar 

  14. Wei, Su, Xu, Jefferson L., & Zhou, Mengchu. (2008). Real time modulation classification based on maximum likelihood. IEEE Communication Letters, 12(11), 801–803.

    Article  Google Scholar 

  15. Xu, Jefferson L., Wei, Su, & Zhou, Mengchu. (2010). Software defined radio equipped with rapid modulation recognition. IEEE Transactions on Vehicular Technology, 59(4), 1659–1667.

    Article  Google Scholar 

  16. Ghauri, S. A., Qureshi, I. M., Malik, A. N., & Cheema, T. A. (2014). Automatic digital modulation recognition technique using higher order cummulants on faded channels. Journal of Basic and Applied Scientific Research, 4(3), 1–12.

    Google Scholar 

  17. Ghauri, S. A., Qureshi, I. M., Cheema, T. A., & Malik, A. N. (2014). A Novel Modulation Classification Approach Using Gabor Filter Network. Cairo: The World Scientific Journal, Hindwai Publishers.

    Google Scholar 

  18. Ghauri, S. A., Qureshi, I. M., Malik, A. N., & Cheema, T. A. (2013). Higher order cummulants based digital modulation recognition scheme. Research Journal of Applied Sciences Engineering and Technology (RJASET), 6(20), 3910–3915.

    Google Scholar 

  19. Ahmadi, Negar. (2011). Modulation recognition based on Constellation shape using TTSAS algorithm and template matching. Journal of Pattern Recognition Research, 1, 43–55.

    Article  Google Scholar 

  20. He, Tao, & Zhou, Zheng-Ou. (2008). Classification of modulated signals using multifractal features. Journal of the Chinese Institute of Engineers, 31(2), 335–338.

    Article  Google Scholar 

  21. Aslam, M. W., Zhu, Z., & Nandi, A. K. (2012). Automatic modulation classification using combination of Genetic programming and KNN. IEEE Transaction on Wireless Communication, 11(8), 2742–2750.

    Google Scholar 

  22. Puengnim, A., Thomas, N., Tourneret, J. Y., & Vidal, J. (2010). Classification of linear and nonlinear modulation using the Baum–Welch algorithms and MCMC methods. Signal Processing, 90(12), 3342–3355.

    Article  MATH  Google Scholar 

  23. Dulek, B., Ozdemir, O., Varshney, P. K., & Su, W. (2014). A novel approach to dictionary construction for Automatic modulation classification. Journal of the Franklin Institute, 351, 2991–3012.

    Article  MathSciNet  Google Scholar 

  24. Dobre, O. A., Abdi, A., Bar Ness, Y., & Su, W. (2010). Cyclostatoinarity based modulation classification of linear digital modulations in flat fading channels. Wireless Personal Communication, 54, 699–717.

    Article  Google Scholar 

  25. Swami, A., & Sadler, B. (2000). Hierarchical digital modulation classification using cummulants. IEEE Transactions on Communication, 48(3), 416–429.

    Article  Google Scholar 

  26. Liu, P., & Shui, P. L. (2014). Digital modulation classifier with rejection ability via greedy convex hull learning and alternative convex hull shrinkage in feature space. IEEE Transaction on Wireless Communication, 13(5), 2683–2695.

    Article  Google Scholar 

  27. Su, W. (2013). Feature space analysis of modulation classification using very higher order statistics. IEEE Communication Letters, 17(9), 1688–1691.

    Article  Google Scholar 

  28. Kharbech, S., Dayoub, I., Zwingelstein Colin, M., Simon, E. P., & Hassan, K. (2014). Blind digital modulation identification for time selective MIMO channels. IEEE Wireless Communication Letters, 3(4), 373–376.

    Article  Google Scholar 

  29. Marey, M., & Dobre, O. A. (2014). Blind modulation classification algorithm for single and multiple antenna systems over frequency selective channels. IEEE Signal Processing Letters, 21(9), 1098–1102.

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Shi, Q., Gong, Y., & Guan, Y. L. (2011). Modulation classification for asynchronous high-order QAM signals. Wireless Communications and Mobile Computing, 11, 1415–1422.

    Article  Google Scholar 

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

  33. Xi, S., & Wu, H. C. (2006). Robust automatic modulation classification using cummulants features in the presence of fading channels. IEEE Wireless Communication and Networking Conference, 4, 2094–2099.

    Google Scholar 

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

    Article  Google Scholar 

  35. Chaithanya, V., & Reddy, V. U., (2010). Blind modulation classification in the presence of carrier frequency offset. In Proceedings of the 2010 international conference on signal processing and communications, (pp. 1–5).

  36. Puengnim, A., Thomas, N., Tourneret J. Y., & Vidal, J. (2007). Hidden markov models for digital modulation classification in unknown ISI channels. In 15th European signal processing conference, (pp. 1882–1886).

  37. Mirrarab, M. R., & Sobhani, M. A., Robust modulation classification for PSK/QAM/ASK using higher order cummulants. In International conference on information, communications and signals, (pp.1–4).

Download references

Author information

Authors and Affiliations

  1. International Islamic University, Islamabad, Pakistan

    Sajjad Ahmed Ghauri & Aqdas Naveed Malik

  2. AIR University, Islamabad, Pakistan

    Ijaz Mansoor Qureshi

Authors
  1. Sajjad Ahmed Ghauri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ijaz Mansoor Qureshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aqdas Naveed Malik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sajjad Ahmed Ghauri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghauri, S.A., Qureshi, I.M. & Malik, A.N. A Novel Approach for Automatic Modulation Classification via Hidden Markov Models and Gabor Features. Wireless Pers Commun 96, 4199–4216 (2017). https://doi.org/10.1007/s11277-017-4378-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4378-x

Keywords

Search

Navigation