Skip to main content
SpringerLink
Log in

Absolute Value Cumulating Based Spectrum Sensing with Laplacian Noise in Cognitive Radio Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Spectrum sensing in the presence of non-Gaussian noise is a challenging problem for cognitive radio networks. However, there are few detectors that can work well in this case. Motivated by these, we propose a spectrum sensing algorithm via absolute value cumulating (AVC) with Laplacian noise. The AVC makes full use of the stochastic properties of Laplacian noise and the central limit theorem. Then the statistic of the proposed detector is derived. A performance analysis about the influence of noise uncertainty in the low signal-to-noise ratio regime is also given, which shows that the SNR Wall of the AVC is half of that of the energy detection. The algorithm are further introduced into existing cooperative spectrum sensing scheme. Simulation results validate the algorithm, and show that the proposed algorithm can improve the performance of existing algorithm at least 3 dB with Laplacian noise.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127–2159.

    Article  Google Scholar 

  2. Zheng, S., Kam, P., Liang, Y., et al. (2013). Spectrum sensing for digital primary signals in cognitive radio: A Bayesian approach for maximizing spectrum utilization. IEEE Transaction on Wireless Communications, 12(4), 1774–1782.

    Article  Google Scholar 

  3. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communication, 6(4), 13–18.

    Article  Google Scholar 

  4. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal of Selected Areas Communication, 23, 201–220.

    Article  Google Scholar 

  5. Gavrilovska, L., & Atanasovski, V. (2011). Spectrum sensing framework for cognitive radio networks. Wireless Personal Communications, 59, 447–469.

    Article  Google Scholar 

  6. Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communication Surveys and Tutorials, 11(1), 116–130. First Quarter.

    Article  Google Scholar 

  7. Rif-Pous, H., Blasco, M. J., & Garrigues, C. (2012). Review of robust cooperative spectrum sensing techniques for cognitive radio networks. Wireless Personal Communications, 67, 175–198.

    Article  Google Scholar 

  8. Gong, Shimin, Wang, Ping, & Huang, Jianwei. (2013). Robust performance of spectrum sensing in cognitive radio networks. IEEE Transaction on Wireless Communications, 12(5), 2217–2227.

    Article  Google Scholar 

  9. Sedighi, Saeid, Taherpour, Abbas, & Sala, Josep. (2013). Spectrum sensing using correlated receiving multiple antennas in cognitive radios. IEEE Transaction on Wireless Communications, 12(11), 5754–5766.

    Article  Google Scholar 

  10. Chung, Wei-Ho. (2013). Sequential likelihood ratio test under incomplete signal model for spectrum sensing. IEEE Transaction on Wireless Communications, 12(2), 494–503.

    Article  Google Scholar 

  11. Axell, E., Leus, G., Larsson, E. G., & Poor, H. V. (2012). Spectrum sensing for cognitive radio: State-of-the-art and recent advances. IEEE Signal Processing Magazine, 29(3), 101–116.

    Article  Google Scholar 

  12. Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4(1), 40–62.

    Article  Google Scholar 

  13. Bansal, G., Hossain, M., Kaligineedi, P., et al. (2007). Some research issues in cognitive radio networks. In AFRICON, IEEE. Windhoek, pp. 1–7.

  14. Middleton, D. (1974, 1976). Statistical physical models of man-made radio noise parts I and II. In US department of commerce office telecommunication, April 1974, 1976.

  15. Taher, T., Misurac, M., LoCicero, J., et al. (2008). Microwave oven signal interference mitigation for Wi-Fi communication systems. In Proceedings of IEEE consumer communications networking conference, Jan. 2008.

  16. Zhu, X., Champagne, B., & Zhu, W. P. (2014). Rao test based cooperative spectrum sensing for cognitive radios in non-Gaussian noise. Signal Processing, 97, 183–194.

    Article  Google Scholar 

  17. Tan, F., Song, X., Leung, C., et al. (2012). Collaborative spectrum sensing in a cognitive radio system with laplacian noise. IEEE Communications Letters, 16(10), 1691–1694.

    Article  Google Scholar 

  18. Li, Q., & Li, Z. (2014). A novel sequential spectrum sensing method in cognitive radio using suprathreshold stochastic resonance. IEEE Transaction on Vehicular Technology, 63(4), 1717–1725.

  19. Li, Q., Li, Z., Shen, J., et al. (2012). A novel spectrum sensing method in cognitive radio based on suprathreshold stochastic resonance. In IEEE international conference on communications, ICC, pp. 4426–4430.

  20. Zahabi, S. J., & Tadaion, A. A. (2010). Local spectrum sensing in non-Gaussian noise. In IEEE 17th international conference on telecommunications, ICT.

  21. Unnikrishnan, J., & Veeravalli, V. V. (2008). Cooperative sensing for primary detection in cognitive radio. IEEE Journal Selected Topics Signal Process, 2(1), 18–27.

    Article  Google Scholar 

  22. Tandra, R., & Sahai, A. (2008). SNR walls for signal detection. IEEE Journal Selected Topics Signal Process., 2(1), 4–17.

    Article  Google Scholar 

  23. Cabric, D., Mishra, S. M., & Brodersen, R. W. (2004). Implementation issues in spectrum sensing for cognitive radios. In Proceedings of asilomar conference on signals, systems and computers, vol. 1, Pacific Grove, California, pp. 772–776.

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant (61301179 and 61401323), National 863 Project of China (2014AA8098080E), and the China Scholarship Council.

Author information

Authors and Affiliations

  1. State Key Laboratory of Integrated Service Networks, Xidian University, Mailbox 87 , No. 2 South Taibai Road, Xi’an, 710071, Shaanxi, China

    Rui Gao & Zan Li

  2. Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, 37996, USA

    Husheng Li

  3. State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong Unviersity, Beijng, 100044, China

    Bo Ai

Authors
  1. Rui Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Husheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bo Ai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rui Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, R., Li, Z., Li, H. et al. Absolute Value Cumulating Based Spectrum Sensing with Laplacian Noise in Cognitive Radio Networks. Wireless Pers Commun 83, 1387–1404 (2015). https://doi.org/10.1007/s11277-015-2457-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2457-4

Keywords

Search

Navigation