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Automatic Classification of WiMAX Physical Layer OFDM Signals Using Neural Network

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Next-Generation Networks

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 638))

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Abstract

The multicarrier OFDM technology has been chosen by many recent networking standards as preferred modulation scheme at physical layer as it offers high robustness against multipath effects. Automatic modulation recognition of OFDM signal has been thus intensive research area in cognitive radios. Several algorithms have been proposed in past that carry out effective detection, parameter estimations, and automatic recognition of OFDM signals as part of radio sensing techniques. In this paper, we proposed neural network-based classification of WiMAX IEEE 802.19 physical layer OFDM signal which does not require any a priori information or depends on cooperative embedded information from transmitter. The proposed algorithm classifies WiMAX IEEE 802.16d OFDM signal in a heterogeneous network environment having other digital modulation signals. The proposed features are robust to channel noise and multipath fading effects on wireless channel.

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References

  1. Akmouche, W.: Detection of multicarrier modulations using 4th-order cumulants. In: Military Communications Conference Proceedings, MILCOM (1999)

    Google Scholar 

  2. Lenonen, J., Juntti, M.: Modulation classification in adaptive OFDM. In: Vehicular Technology Conference, 2004, vol. 3, pp. 1554–2252 (2004)

    Google Scholar 

  3. Reddy, S.B., Yucek, T., Arslan, H.: An efficient blind modulation detection algorithm for adaptive OFDM systems. In: Proceedings of IEEE Vehicular Technology Conference, Orlando, FL, Oct 2003

    Google Scholar 

  4. Yucek, T., Arslan, H.: A novel sub-optimum maximum-likelihood modulation classification algorithm for adaptive OFDM systems. In: Wireless Communications and Networking Conference, 2004, vol. 2, pp. 739–745, March 2004

    Google Scholar 

  5. Huo, L., Duan, T., Fang, X.: A novel method of modulation classification for digital signals. In: International Joint Conference on Neural Networks, pp. 2435–2438, July 2006

    Google Scholar 

  6. Han, N., Zheng, G., Sohn, S.H., Kim, J.M.: Cyclic autocorrelation based blind OFDM detection and identification for cognitive radio. In: 4th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM’08, pp. 1–5 (2008)

    Google Scholar 

  7. Ulovec, K.: Noncoherent recognition of OFDM modulation. In: 18th International Conference Radioelektronika, pp. 50–55, April 2008

    Google Scholar 

  8. Chen, M., Zhu, Q.: Cooperative automatic modulation recognition in cognitive radio. J. China Univ. Posts Telecomm. 17, 46–52 (2010)

    Article  Google Scholar 

  9. Bouzegzi, A., Ciblat, P., Jallon, P.: New algorithms for blind recognition of OFDM based systems. Signal Process 90, 900–913 (2010)

    Article  MATH  Google Scholar 

  10. Haq, K.N., Mansour, A., Nordholm, S.: Recognition of digital modulated signals based on statistical parameters. In: 4th IEEE International Conference on Digital Ecosystems and Technologies, pp. 565–570 (2010)

    Google Scholar 

  11. Vladimír, S., Roman, M., Zbyněk, F.: OFDM signal detector based on cyclic autocorrelation function and its properties. Radio Eng. 20(4), 927 (2011)

    Google Scholar 

  12. Nouha, A., Abdennaceur, K., Samet, M.: The fourth generation 3GPP LTE identification for cognitive radio. In: International Conference on Microelectronics (ICM), pp. 1–5, Dec 2011

    Google Scholar 

  13. Muhlhaus, .S., aus, Öner, M., Dobre, O.A., Jakel, H.U., Jondral, F.K.: Automatic modulation classification for MIMO systems using fourth-order cumulants. In: IEEE Conference Vehicular Technology, pp. 1–5 (2012)

    Google Scholar 

  14. Al-Makhlasawy, R.M., Elnaby, M.M.A., El-Khobby, H.A., El-Rabaie S., El-samie, F.E.A: Automatic modulation recognition in OFDM systems using cepstral analysis and support vector machines. J. Telecomm. Syst. Manag 1(3), 1–7 (2012)

    Google Scholar 

  15. Al-Makhlasawy, R.M., Elnaby, M.M.A., El-Khobby, H.A., El-Rabaie S., El-samie, F.E.A.: Automatic modulation recognition in OFDM systems using cepstral analysis and a fuzzy logic interface. In: 8th International Conference on Informatics and Systems, pp. 56–61, May 2012

    Google Scholar 

  16. Hassan, K., Dayoub, I., Hamouda, W., Nzéza C.N., Berbineau, M.: Blind digital modulation identification for spatially-correlated MIMO systems. IEEE Trans Wirel. Commun. 11(2), 683–693 (2012)

    Google Scholar 

  17. Al-Habashna, A., Dobre, O.A., Venkatesan, R., Popescu, D.C.: Second-order cyclostationarity of mobile WiMAX and LTE OFDM signals and application to spectrum awareness in cognitive radio systems. IEEE J. Sel. Top. Signal Process. 6(1), 26–42 (2012)

    Article  Google Scholar 

  18. Mühlhaus, M.S., Öner, M., Dobre, O.A., Jondral, F.K.: A low complexity modulation classification algorithm for MIMO systems. IEEE Commun. Lett. 17(10), 1881–1884 (2013)

    Article  Google Scholar 

  19. Mühlhaus, M.S., Öner, M., Dobre, O.A., Jondral, F.K., Jakel, H.U..: A novel algorithm for MIMO signal classification using higher-order cumulants. In: IEEE Radio and Wireless Symposium, pp. 7–9, Jan 2013

    Google Scholar 

  20. Gorcin A., Arslan, H.: Identification of OFDM signals under multipath fading channels. In: Military Communications Conference, pp. 1–7, Nov 2012

    Google Scholar 

  21. Sun, Z., Liu R., Wang, W.: Joint time-frequency domain cyclostationarity-based approach to blind estimation of OFDM transmission parameters. EURASIP J. Wirel. Commun. Netw, 1–8 (2013)

    Google Scholar 

  22. Liu, Y., Haimovich, A.M., Su, W., Dabin, J., Kanterakis, E.: Modulation classification of MIMO-OFDM, signals by independent component analysis and, support vector machines. In: 45th Asilomar Conference on Signals, Systems and Computers, pp. 1903–1907, Nov 2011

    Google Scholar 

  23. Guibene, W., Slock, D.: Signal classification in heterogeneous OFDM-based cognitive radio systems. In: 20th International Conference on Telecommunications, pp. 4284–4299 (2013)

    Google Scholar 

  24. Jantti, J., Chaudhari, S., Koivunen, V.: Cepstrum based detection and classification of OFDM waveforms. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8063–8067 (2014)

    Google Scholar 

  25. Sun, Z., Chen, Y., Liu S., Wang, W.: Cyclostationarity-based joint domain approach to blind recognition of SCLD and OFDM signals. EURASIP J. Adv. Signal Process. pp. 1–6 (2014)

    Google Scholar 

  26. Gorcin, A., Arslan, H.: An OFDM signal identification method for wireless communications systems. IEEE Trans. Veh. Technol. pp. 1–13 (2015)

    Google Scholar 

  27. Hazza, A. Shoaib, M. Alshebeili, S.A.; Fahad, A.: An overview of feature-based methods for digital modulation classification. In: 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), pp. 1–6 (2013)

    Google Scholar 

  28. Nandi, A.K., Azzouz, E.E.: Modulation recognition using neural network. Signal Process. 56(2), 165–175 (1997)

    Article  MATH  Google Scholar 

  29. Nandi, A.K., Azzouz, E.E.: Automatic modulation recognition—II”. J Franklin Inst 334B(2), 241–273 (1997)

    MATH  Google Scholar 

  30. 802.16-2004—Standard for local and metropolitan area networks—part 16: air interface for fixed broadband wireless access systems

    Google Scholar 

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Author information

Authors and Affiliations

  1. Banasthali University, Vanasthali, Rajasthan, India

    Praveen S. Thakur

  2. Lady Shri Ram College for Women, Delhi University, Delhi, India

    Sushila Madan

  3. Vivekananda Institute of Professional Studies, Indraprastha University, Delhi, India

    Mamta Madan

Authors
  1. Praveen S. Thakur
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  2. Sushila Madan
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  3. Mamta Madan
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Corresponding author

Correspondence to Praveen S. Thakur .

Editor information

Editors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, Delhi, India

    Daya K. Lobiyal

  2. Centre for IT, University of Jammu, Jammu, India

    Vibhakar Mansotra

  3. Institute of Technology and Science, Ghaziabad, Uttar Pradesh, India

    Umang Singh

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Thakur, P.S., Madan, S., Madan, M. (2018). Automatic Classification of WiMAX Physical Layer OFDM Signals Using Neural Network. In: Lobiyal, D., Mansotra, V., Singh, U. (eds) Next-Generation Networks. Advances in Intelligent Systems and Computing, vol 638. Springer, Singapore. https://doi.org/10.1007/978-981-10-6005-2_21

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  • DOI: https://doi.org/10.1007/978-981-10-6005-2_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6004-5

  • Online ISBN: 978-981-10-6005-2

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