Wireless Personal Communications

, Volume 97, Issue 3, pp 3901–3920 | Cite as

Joint Rapid Spectrum Scanning and Signal Feature Recognition Scheme Using Compressed Sensing and Cyclostationary Technologies

  • Yifan Zhang
  • Qixun ZhangEmail author
  • Xuan Fu
  • Yucheng Tian
  • Zhiyong Feng


This paper proposes a joint rapid spectrum scanning and signal feature recognition scheme by using both compressed sensing (CS) and cyclostationary technologies to solve the paradox of rapid spectrum scanning and accurate signal feature recognition. First, a new system architecture for signal feature recognition is designed based on rapid spectrum scanning results to decrease the times of proposed signal feature recognition scheme. Moreover, an improved CS technology is brought forward to accelerate the sensing speed without reconstructing received signals for a rapid spectrum scanning. And a tunable compression gain is proposed to reduce both computation complexity and sampling rate based on the difference of modulation mode and symbol rate for various signals. To further reduce the effect of noise on the modulation classification performance, a novel noise reduction scheme is proposed using the cyclostationary technology. Results prove that proposed scheme can achieve both rapid spectrum scanning and accurate signal feature recognition simultaneously. Furthermore, it can reduce the sampling rate for CS over 30% and achieves a signal detection gain of 2–3 dB with signal to noise ratio constraints.


Compressed sensing Cyclostationary Modulation classification 



This work was supported by the National Natural Science Foundation of China (61201152), the Fundamental Research Funds for the Central Universities (2014ZD03-02), the National Natural Science Foundation of China (61227801), the National High-Tech R&D Program (863 Program 2015AA01A705).


  1. 1.
    Federal Communications Commission. (Feb. 2005). Notice of proposed rule making and order: Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies (pp. 03–108). No: ET Docket.Google Scholar
  2. 2.
    Stotas, S., & Nallanathan, A. (2011). Enhancing the capacity of spectrum sharing cognitive radio networks. IEEE Transactions on Vehicular Technology, 60(8), 3768–3779.CrossRefGoogle Scholar
  3. 3.
    McHenry, M. A. (2005, August) NSF Spectrum occupancy measurements project summary. Shared spectrum company report.Google Scholar
  4. 4.
    Chien, Wen-Bin, Yang, Chih-Kai, & Huang, Yuan-Hao. (2010). Energy-saving cooperative spectrum sensing processor for cognitive radio system. IEEE Transactions on Circuits and Systems I: Regular Papers, 58(4), 711–723.MathSciNetCrossRefGoogle Scholar
  5. 5.
    Lin, You-En, Liu, Kun-Hsing, & Hsieh, Hung-Yun. (2012). On using interference-aware spectrum sensing for dynamic spectrum access in cognitive radio networks. IEEE Transactions on Mobile Computing, 12(3), 461–474.CrossRefGoogle Scholar
  6. 6.
    Salt, J. E., & Nguyen, H. H. (2008). Performance prediction for energy detection of unknown signals. IEEE Transactions on Vehicular Technology, 57(6), 3900–3904.CrossRefGoogle Scholar
  7. 7.
    Hauptschein, A., & Knapp, T. (1979). Maximum likelihood energy detection of mary orthogonal signals. IEEE Transactions on Aerospace and Electronic Systems, AES–15(2), 292–299.CrossRefGoogle Scholar
  8. 8.
    Zhao, Y. Q., Li, S. Y., Zhao, N., & Wu, Z. L. (2010). A novel energy detection algorithm for spectrum sensing in cognitive radio. Information Technology Journal, 9(8), 1659–1664.CrossRefGoogle Scholar
  9. 9.
    Eftekhari, A., Romberg, J., & Wakin, M. B. (2013). Matched filtering from limited frequency samples. IEEE Transactions on Information Theory, 59(6), 3475–3496.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Cai, Z. P., Wang, Z. J., & Zheng, K. (2011). A distributed TCAM coprocessor architecture for integrated longest prefix matching, policy filtering, and content filtering. IEEE Transactions on Computers, 62(3), 417–427.MathSciNetCrossRefGoogle Scholar
  11. 11.
    Bhargavi, D., & Murthy, C. R. (2010, June). Performance comparison of energy, matched-filter and cyclostationarity-based spectrum sensing. In 2010 IEEE eleventh international workshop signal processing advances in wireless communications (SPAWC) (Vol. 1(5), pp. 20–23).Google Scholar
  12. 12.
    Riba, J., Font-Segura, J., Villares, J., & Vazquez, G. (2014). Frequency-domain GLR detection of a second-order cyclostationary signal over fading channels. IEEE Transactions on Signal Processing, 62(8), 1899–1912.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Derakhshani, M., Le-ngoc, T., & Nasiri-kenari, M. (2011). Efficient cooperative cyclostationary spectrum sensing in cognitive radios at low SNR regimes. IEEE Transactions on Wireless Communications, 10(11), 3754–3764.CrossRefGoogle Scholar
  14. 14.
    Kyouwoong, K., Akbar, I. A., Bae, K. K., Um, J. S., Spooner, C. M., & Reed, J. H. (2007, April). Cyclostationary approaches to signal detection and classification in cognitive radio. In 2007 IEEE 2nd international symposium new frontiers in dynamic spectrum access networks (DySPAN) (pp. 212–215).Google Scholar
  15. 15.
    Adoum, B. A., & Jeoti, V. (2010, May). Cyclostationary feature based multiresolution spectrum sensing approach for DVB-T and wireless microphone signals. In 2010 international conference computer and communication engineering (ICCCE) (pp. 1–6).Google Scholar
  16. 16.
    Dandawate, A. V., & Giannakis, G. B. (1994). Statistical tests for presence of cyclostationarity. IEEE Transactions Signal Processing, 42(9), 2355–2369.CrossRefGoogle Scholar
  17. 17.
    Ciblat, P., Loubaton, P., Serpedin, E., et al. (2002). Asymptotic analysis of blind cyclic correlation-based symbol-rate estimators. IEEE Transactions Information Theory, 48(7), 1922–1934.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Ciblat, P., Loubaton, P., Serpedin, E., & Giannakis, G. B. (2002). Asymptotic analysis of blind cyclic correlation-based symbol-rate estimators. IEEE Transactions Information Theory, 48(7), 1922–1934.MathSciNetCrossRefGoogle Scholar
  19. 19.
    Hsue, S. Z., & Soliman, S. S. (1990). Automatic modulation classification using zero crossing. IEE Proceedings Radar Signal Processing, 137(6), 459–465.CrossRefGoogle Scholar
  20. 20.
    Schreyogg, C., Kittel, K., Kressel, U., & Reichert, J. (1997, February). Robust classification of modulation types using spectral features applied to HMM. In Proceedings of IEEE MILCOM (pp. 1377–1381), Monterey, CA.Google Scholar
  21. 21.
    Ho, K. C., Prokopiw, W., & Chan, Y. T. (2000). Modulation identification of digital signals by the wavelet transform. Proceedings of Institute of Electrical Engineering, Radar, Sonar and Navigation, 147(4), 169–176.CrossRefGoogle Scholar
  22. 22.
    Kim, K., Akbar, I. A., Bae, K. K., Um, J.-S., Spooner, C. M., & Reed, J. H. (2007, April). Cyclostationary approaches to signal detection and classification in cognitive radio. In Proceedings of IEEE DySpan (pp. 212–215), Dublin, Ireland.Google Scholar
  23. 23.
    Ramkumar, B. (2009). Automatic modulation classification for cognitive radios using cyclic feature detection. IEEE Circuits and Systems Magazine, 9(2), 27–45.CrossRefGoogle Scholar
  24. 24.
    Tian, Z., & Giannakis, G. B. (2007, April). Compressed sensing for wideband cognitive radios. In IEEE acoustics, speech and signal processing (ICASSP) 2007.Google Scholar
  25. 25.
    Leus, G., & Tian, Z. (2011, December). Recovering second-order statistics from compressive measurements. In IEEE computational advances in multi-sensor adaptive processing (CAMSAP) 2011.Google Scholar
  26. 26.
    Golub, G. H., & Loan, C. F. V. (1996). Matrix computations (3rd ed.). Baltimore: The Johns Hopkins University Press.zbMATHGoogle Scholar
  27. 27.
    Tian, Z., Tafesse, Y., & Sadler, B. M. (2012). Cyclic feature detection with sub-Nyquist sampling for wideband spectrum sensing. IEEE Journal of Selected Topics in Signal Processing, 6(1), 58–69.CrossRefGoogle Scholar
  28. 28.
    Sabat, S. L., Srinu, S., Raveendranadh, A., & Udgata, S. K. (2012, January). Spectrum sensing based on entropy estimation using cyclostationary features for Cognitive radio. In Communication systems and networks (COMSNETS) (pp. 1–6).Google Scholar
  29. 29.
    Zhang, Y., Zhang, Q., & Wu, S. (2010). Entropy-based robust spectrum sensing in cognitive radio. IET Communications, 4(4), 428–436.CrossRefGoogle Scholar
  30. 30.
    Tropp, J. A., Laska, J. N., Duarte, M. F., Romberg, J. K., & Baraniuk, R. G. (2010). Beyond Nyquist: Efficient sampling of sparse bandlimited signals. IEEE Transactions Information Theory, 56(1), 520–544.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Yifan Zhang
    • 1
  • Qixun Zhang
    • 1
    Email author
  • Xuan Fu
    • 1
  • Yucheng Tian
    • 1
  • Zhiyong Feng
    • 1
  1. 1.Key Laboratory of Universal Wireless Communications Ministry of EducationInformation and Telecommunication Engineering of Beijing University of Posts and Telecommunications (BUPT)BeijingPeople’s Republic of China

Personalised recommendations