Skip to main content
Log in

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

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

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.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Poor, H. V., & Hadjiliadis, O. (2009). Quickest detection. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  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.

  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.

  4. Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531.

    Article  Google Scholar 

  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.

  6. Gardner, W. A. (1988). Signal interception: A Uni-fying theoretical framework for feature detection. IEEE Transactions on Communications, 36(8), 897–906.

    Article  Google Scholar 

  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.

  8. Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41, 100–115.

    Article  MATH  MathSciNet  Google Scholar 

  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. Baraniuk, R. (2007). Compressive sensing. IEEE Signal Processing Magazine, 24(4), 118–121.

    Article  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  12. Tropp, J. A., & Gilbert, A. C. (2005). Signal recovery from partial information via orthogonal matching pursuit. Preprint, University of Michigan.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qi Zhao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhao, Q., Li, X. & Wu, Z. Quickest Detection of Multi-channel Based on STFT and Compressed Sensing. Wireless Pers Commun 77, 2183–2193 (2014). https://doi.org/10.1007/s11277-014-1632-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-014-1632-3

Keywords

Navigation