Journal of Communications and Networks

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Novel Schemes to Optimize Sampling Rate for Compressed Sensing

  • Zhang, Yifan (School of Information and Communication Engineering of Beijing University of Posts and Telecommunications) ;
  • Fu, Xuan (School of Information and Communication Engineering of Beijing University of Posts and Telecommunications) ;
  • Zhang, Qixun (School of Information and Communication Engineering of Beijing University of Posts and Telecommunications) ;
  • Feng, Zhiyong (School of Information and Communication Engineering of Beijing University of Posts and Telecommunications) ;
  • Liu, Xiaomin (School of Information and Communication Engineering of Beijing University of Posts and Telecommunications)
  • Received : 2014.05.20
  • Accepted : 2015.02.13
  • Published : 2015.10.31
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Abstract

The fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge for the radio environment cognition. Considering sparse signal feature, two novel compressed sensing schemes are proposed, which can reduce compressed sampling rate in contrast to the traditional scheme. One algorithm is dynamically adjusting compression ratio based on modulation recognition and identification of symbol rate, which can reduce compression ratio. Furthermore, without priori information of the modulation and symbol rate, another improved algorithm is proposed with the application potential in practice, which does not need to reconstruct the signals. The improved algorithm is divided into two stages, which are the approaching stage and the monitoring stage. The overall sampling rate can be dramatically reduced without the performance deterioration of the spectrum detection compared to the conventional static compressed sampling rate algorithm. Numerous results show that the proposed compressed sensing technique can reduce sampling rate by 35%, with an acceptable detection probability over 0.9.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

References

  1. Federal Communications Commission, "Notice of proposed rule making and order: Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies," ET Docket No. 03-108, Feb. 2005.
  2. Z. Tian and G. B. Giannakis, "Compressed sensing for wideband cognitive radios," in Proc. IEEE ICASSP, Apr. 2007.
  3. R. G. Baraniuk, "Compressive sensing [Lecture notes]," IEEE Signal Processing Magazine, vol. 24, no. 4, pp. 118-121, July 2007. https://doi.org/10.1109/MSP.2007.4286571
  4. J. A. Tropp, et al. "Beyond Nyquist: Efficient sampling of sparse bandlimited signals," IEEE Trans. Inform. Theory, vol. 56, no. 1, 520-544, Jan. 2010. https://doi.org/10.1109/TIT.2009.2034811
  5. V. Havary-Nassab, S. Hassan, and S. Valaee, "Compressive detection for wideband spectrum sensing," in Proc. IEEE ICASSP, Mar. 2010.
  6. G. Leus and Z. Tian, "Recovering second-order statistics from compressive measurements," in Proc. IEEE CAMSAP, Dec. 2011.
  7. Z. Tian, "Cyclic feature based wideband spectrum sensing using compressive sampling," in Proc. IEEE ICC, June 2011.
  8. Z. Tian, Y. Tafesse, and B. M. Sadler, "Cyclic feature detection with sub-Nyquist sampling for wideband spectrum sensing," IEEE J-STSP, vol. 6, no. 1, pp. 58-69, Feb. 2012.
  9. Z. Zhang, et al. "Space-time Bayesian compressed spectrum sensing for wideband cognitive radio networks," in Proc. DySPAN, pp. 1-11, 2010.
  10. W. Yin, et al. "Dynamic compressive spectrum sensing for cognitive radio networks," in Proc. CISS, pp. 1-6, 2011
  11. D. Zhang, et al. "A novel compression ratio allocation algorithm for collaborative wideband spectrum sensing," in Proc. IEEE CCNC, Jan. 2012.
  12. C. C. Huang and L. C. Wang, "Dynamic sampling rate adjustment for compressive spectrum sensing over cognitive radio network," IEEE Wireless Commun. Lett., vol. 1, no. 2, pp. 57-60, Apr. 2012. https://doi.org/10.1109/WCL.2012.010912.110136
  13. J. K. Pant, Lu Wu-Sheng, and A. Antoniou, "Reconstruction of sparse signals by minimizing a re-weighted approximate $l_0$ - norm in the null space of the measurement matrix," IEEE Trans. Inform. Theory, vol. 56, no. 1, pp. 520-544, Jan. 2010. https://doi.org/10.1109/TIT.2009.2034811
  14. S. L. Sabat, et al. "Spectrum sensing based on entropy estimation using cyclostationary features for Cognitive radio," in Proc. COMSNETS, Jan. 2012, pp. 1-6.