An Image Processing Approach to Wideband Spectrum Sensing of Heterogeneous Signals

  • Ha Q. Nguyen
  • Ha P. K. Nguyen
  • Binh T. Nguyen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 261)


We introduce a simple yet efficient framework for the localization and tracking of fixed-frequency and frequency-hopping (FH) wireless signals that coexist in a wide radio-frequency band. In this spectrum sensing scheme, an energy detector is applied to each Short-time Fourier Transform of the wideband signal to produce a binary spectrogram. Bounding boxes for narrowband signals are then identified by using image processing techniques on a block of the spectrogram at a time. These boxes are also tracked along the time axis and fused with the newly detected boxes to provide an on-line system for spectrum sensing. Fast and highly accurate detection is achieved in simulations for various FF signals and FH signals with different hopping patterns and speeds. In particular, for the SNR of 4 dB over a bandwidth of 50 MHz, 97.98% of narrowband signals were detected with average deviations of about \(0.02\,\mathrm{ms}\) in time and \(2.15\,\mathrm{KHz}\) in frequency.


Wideband spectrum sensing Wireless signal detection Frequency hopping Time-frequency analysis Spectrogram Waterfall image Image morphology Blob extraction 


  1. 1.
    Ariananda, D.D., Leus, G.: Cooperative compressive wideband power spectrum sensing. In: Proceedings of IEEE ASILOMAR, pp. 303–307. Pacific Groove, CA, July 2012Google Scholar
  2. 2.
    Cohen, D., Eldar, Y.C.: Sub-Nyquist sampling for power spectrum sensing in cognitive radios: a unified approach. IEEE Trans. Signal Process. 62(15), 3897–3910 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Farhang-Boroujeny, B.: Filter bank spectrum sensing for cognitive radios. IEEE Trans. Signal Process. 56(5), 1801–1811 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Upper Saddle River (2006)Google Scholar
  5. 5.
    Lampert, T.A., O’keefe, S.E.M.: A survey of spectrogram track detection algorithms. Appl. Acoustics 71(2), 87–100 (2010)CrossRefGoogle Scholar
  6. 6.
    Liang, Y.C., Chen, K.C., Li, G.Y., Mahonen, P.: Cognitive radio networking and communications: an overview. IEEE Trans. Veh. Technol. 60(7), 3386–3407 (2011)CrossRefGoogle Scholar
  7. 7.
    Mishali, M., Eldar, Y.C.: Blind multiband signal reconstruction: compressive sensing for analog signals. IEEE Trans. Signal Process. 57(3), 993–1009 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Mitola, J., Maguire, G.Q.: Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6(4), 13–18 (1999)CrossRefGoogle Scholar
  9. 9.
    Ready, M.J., Downey, M.L., Corbalis, L.J.: Automatic noise floor spectrum estimation in the presence of signals. In: Proceedings of IEEE ASILOMAR, pp. 877–881 (1997)Google Scholar
  10. 10.
    Sun, H., Nallanathan, A., Wang, C.X.: Wideband spectrum sensing for cognitive radio networks: a survey. IEEE Wirel. Commun. 20(2), 74–81 (2013)CrossRefGoogle Scholar
  11. 11.
    Tian, Z., Giannakis, G.B.: A wavelet approach to wideband spectrum sensing for cognitive radios. In: Proceeding of IEEE CROWNCOM, pp. 1–5, Mykonos Island, Greece, 08–10 July 2006Google Scholar
  12. 12.
    Tian, Z., Tafesse, Y., Sadler, B.M.: Cyclic feature detection with sub-Nyquist sampling for wideband spectrum sensing. IEEE J. Sel. Top. Signal Process. 6(1), 58–69 (2012)CrossRefGoogle Scholar
  13. 13.
    Watson, C.M.: Signal detection and digital modulation classification-based spectrum sensing for cognitive radio. Ph.D. thesis, Northeastern University, Boston, MA, USA (2013)Google Scholar
  14. 14.
    Yucek, T., Arslan, H.: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutor. 11(1), 116–130 (2009)CrossRefGoogle Scholar
  15. 15.
    Zeng, F., Li, C., Tian, Z.: Distributed compressive spectrum sensing in cooperative multihop cognitive networks. IEEE J. Sel. Top. Signal Process. 5(1), 37–48 (2011)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Ha Q. Nguyen
    • 1
  • Ha P. K. Nguyen
    • 1
  • Binh T. Nguyen
    • 1
  1. 1.Viettel Research and Development Institute, Hoa Lac High-tech ParkHanoiVietnam

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