Wireless Personal Communications

, Volume 77, Issue 3, pp 2183–2193 | Cite as

Quickest Detection of Multi-channel Based on STFT and Compressed Sensing

  • Qi ZhaoEmail author
  • Xiaochun Li
  • Zhijie Wu


This paper proposes a multi-channel quickest detection method based on compressed sensing and short-time Fourier transform. Quickest detection performs a statistical test to obtain the minimal detection delay subject to given false alarm constrains. Short-time Fourier transform, which reflects the time–frequency information, implements the multi-channel quickest detection. Compressed sensing reduces the sampling rate at first. Compared with single-channel spectrum sensing, this method substantially improves the spectrum access opportunity in time and frequency domain. The relationship between the detection delay and other parameters, such as the probability of false alarm, SNR, sparsity, and sampling rate, verifies the validity of the method. While simulation results show that this method can perform spectrum sensing in high detection probability and low probability of false alarm.


Spectrum sensing Quickest detection Multi-channel STFT  Compressed sensing 


  1. 1.
    Poor, H. V., & Hadjiliadis, O. (2009). Quickest detection. Cambridge: Cambridge University Press.zbMATHGoogle Scholar
  2. 2.
    Li, H., Li, C., & Dai, H. (2008). Quickest spectrum sensing in cognitive radio. Information sciences and system, 2008. 42nd Annual conference on. IEEE, pp. 203–208.Google Scholar
  3. 3.
    Li, H. (2010). Cyclostationary feature based quickest spectrum sensing in cognitive radio systems. Vehicular technology conference Fall (VTC 2010-Fall), 2010 IEEE, pp. 1–5.Google Scholar
  4. 4.
    Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531.CrossRefGoogle Scholar
  5. 5.
    Cabric, D. (2004). Implementation issues in spectrum sensing for cognitive radios. Signals, systems and computers, 2004. Conference record of the thirty-eighth Asilomar conference on. IEEE, 1, 772–776.Google Scholar
  6. 6.
    Gardner, W. A. (1988). Signal interception: A Uni-fying theoretical framework for feature detection. IEEE Transactions on Communications, 36(8), 897–906.CrossRefGoogle Scholar
  7. 7.
    Lai, L., Fan, Y., & Poor, H. V. (2008). Quickest detection in cognitive radio: A sequential change detection framework. Global telecommunications conference, 2008. IEEE GLOBECOM 2008. IEEE, IEEE, pp. 1–5.Google Scholar
  8. 8.
    Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41, 100–115.CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Candès, E. J. (2008). The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique, 346(9), 589–592.Google Scholar
  10. 10.
    Baraniuk, R. (2007). Compressive sensing. IEEE Signal Processing Magazine, 24(4), 118–121.CrossRefGoogle Scholar
  11. 11.
    Candès, E. J., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: Exact reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489–509.CrossRefzbMATHGoogle Scholar
  12. 12.
    Tropp, J. A., & Gilbert, A. C. (2005). Signal recovery from partial information via orthogonal matching pursuit. Preprint, University of Michigan.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Electronic Information EngineeringBeihang UniversityBeijing China

Personalised recommendations