DOI QR코드

DOI QR Code

Sensing Performance of Efficient Cyclostationary Detector with Multiple Antennas in Multipath Fading and Lognormal Shadowing Environments

  • Zhu, Ying (Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications) ;
  • Liu, Jia (Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications) ;
  • Feng, Zhiyong (Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications) ;
  • Zhang, Ping (Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications)
  • Received : 2013.09.15
  • Published : 2014.04.30

Abstract

Spectrum sensing is a key technical challenge for cognitive radio (CR). It is well known that multicycle cyclostationarity (MC) detection is a powerful method for spectrum sensing. However, a conventional MC detector is difficult to implement because of its high computational complexity. This paper considers reducing computational complexity by simplifying the test statistic of a conventional MC detector. On the basis of this simplification process, an improved MC detector is proposed. Compared with the conventional detector, the proposed detector has low-computational complexity and high-accuracy sensing performance. Subsequently, the sensing performance is further investigated for the cases of Rayleigh, Nakagami-m, Rician, and Rayleigh fading and lognormal shadowing channels. Furthermore, square-law combining (SLC) is introduced to improve the detection capability in fading and shadowing environments. The corresponding closed-form expressions of average detection probability are derived for each case by the moment generation function (MGF) and contour integral approaches. Finally, illustrative and analytical results show the efficiency and reliability of the proposed detector and the improvement in sensing performance by SLC in multipath fading and lognormal shadowing environments.

Keywords

References

  1. J. Mitola, "Cognitive radio: An integrated agent architecture for software defined radio," Ph.D. dissertation, KTH Royal Institute of Technology, Sweden, May 2000.
  2. K. Sridhara, A. Chandra, and P. S. M. Tripathi, "Spectrum challenges and solutions by cognitive radio: An overview," Wireless Pers. Commun., vol. 45, no. 3, pp. 281-291, 2008. https://doi.org/10.1007/s11277-008-9465-6
  3. W. El-Hajj, H. Safa, and M. Guizani, "Survey of security issues in cognitive radio networks," J. Internet Technol., vol. 12, no. 2, pp. 181-198, 2011.
  4. Y. Chen, C. Cho, I. You, and H. Chao, "A cross-layer protocol of spectrum mobility and handover in cognitive LTE networks," Simul. Model. Pract. Theory, vol. 19, no. 8, pp. 1723-1744, 2011. https://doi.org/10.1016/j.simpat.2010.09.007
  5. H. Arslan, "Cognitive radio, software defined radio, and adaptive wireless systems," Springer, 2007.
  6. L. Huang, Z. Gao, D. Guo, H. Chao, and J. Park, "A sensing policy based on the statistical property of licensed channel in cognitive network," Int. J. Internet Protocol Technol., vol. 5, no. 4, pp. 219-229, 2010. https://doi.org/10.1504/IJIPT.2010.039233
  7. Z. Gao, L. Huang, Y. Yao, and T.Wu, "Performance analysis of a busycognitive multi-channel MAC protocol," J. Internet Technol., vol. 11, no. 3, pp. 299-306, 2011.
  8. S. Haykin, "Cognitive radio: Brain-empowered wireless communications," IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201-220, Feb. 2005. https://doi.org/10.1109/JSAC.2004.839380
  9. A. Ghasemi and E. S. Sousa, "Spectrum sensing in cognitive radio networks: The cooperation-processing tradeoff," Wireless Commun. Mobile Comput., vol. 7, no. 9, pp. 1049-1060, Nov. 2007. https://doi.org/10.1002/wcm.480
  10. S.M.Mishra, A. Sahai, and R.W. Brodersen, "Cooperative sensing among cognitive radios," Int. Conf. Communications, Istanbul, Turkey, June 2006.
  11. A. V. Dandawate and G. B. Giannakis, "Statistical tests for presence of cyclostationarity," IEEE Trans. Signal Process., vol. 42, no. 9, pp. 2355- 2369, Sept. 1994. https://doi.org/10.1109/78.317857
  12. W. A. Gardner, "Signal interception: A unifying theoretical framework for feature detection," IEEE Trans. Commun., vol. 36, pp. 897-906, 1988. https://doi.org/10.1109/26.3769
  13. J. Wang, T. Chen, and B. Huang, "Cyclo-period estimation for discrete time cyclo-stationary signals," IEEE Trans. Signal Process., vol. 54, no. 1, pp. 83-94, 2006. https://doi.org/10.1109/TSP.2005.859237
  14. R. Tandra and A. Sahai, "Fundamental limits on detection in low SNR under noise uncertainty," in Proc. Wireless Commun. Symp. on Signal Processing, 2005.
  15. M. Derakhshani, M. Nasiri-Kenari, and T. Le-Ngoc, "Cooperative cyclostationary spectrum sensing in cognitive radios at low SNR regimes," IEEE Trans. Wireless Commun., vol. 10, no. 11, pp. 3754-3764, 2012.
  16. A. Pandharipande and J. P. Linnartz, "Performance analysis of primary user detection in multiple antenna cognitive radio," in Proc. of IEEE International Conf. on Commun., IEEE Press, 2007, pp. 6482-6486.
  17. S. Atapattu, C. Tellambura, and H. Jiang, "Energy detection based cooperative spectrum sensing in cognitive radio networks," IEEE Trans. Wireless Commun., vol. 10, no. 4, pp. 1232-1242, 2011. https://doi.org/10.1109/TWC.2011.012411.100611
  18. C. Loo, "Digital Transmission through a land mobile satellite channel," IEEE Trans. Wireless Commun., vol. 38, pp. 693-697, 1990. https://doi.org/10.1109/26.54983
  19. S. P. Herath, N. Rajatheva, and C. Tellambura, "Energy detection of unknown signals in fading and diversity reception," IEEE Trans. Wireless Commun., vol. 59, no. 9, pp. 2443-2453, 2011. https://doi.org/10.1109/TCOMM.2011.071111.090349
  20. Z. Quan, S. Cui, and A. H. Sayed, "Feature detection based on multiple cyclic frequencies in cognitive radios," in Proc. IEEE Microwave Conf., Sept. 2008.
  21. K. W. Choi, W. S. Jeon, and D. G. Jeong, "Sequential detection of cyclostationary signal for cognitive radio systems," IEEE Trans. Wireless Commun., vol. 8, no. 9, pp. 4480-4485, Sept. 2009. https://doi.org/10.1109/TWC.2009.090288
  22. K. L. Du and W. H. Mow, "Affordable cyclostationarity-based spectrum sensing for cognitive radio with smart antennas," IEEE Trans. Veh. Technol., vol. 59, no. 4, pp. 1877-1886, May 2010. https://doi.org/10.1109/TVT.2010.2043860
  23. J. Lunden, V. Koivunen, A. Huttunen, and H. V. Poor, "Collaborative cyclostationary spectrum sensing for cognitive radio systems," IEEE Trans. Signal Process., vol. 57, no. 11, pp. 4182-4195, Nov. 2009. https://doi.org/10.1109/TSP.2009.2025152
  24. H. Sadeghi and P. Azmi, "Cyclostationarity-based cooperative spectrum sensing for cognitive radio networks," in Proc. IEEE IST, Aug. 2008.
  25. M. Derakhshani, M. Nasiri-Kenari, and T. Le-Ngoc, "Cooperative cyclostationary spectrum sensing in cognitive radios at low SNR regimes," IEEE Trans. Wireless Commun., vol. 10, no. 11, pp. 3754-3764, 2012.
  26. C. Tellambura, A. Annamalai, and V. K. Bhargava, "Closed form and infinite series solutions for the MGF of a dual-diversity selection combiner output in bivariate Nakagami-m fading," IEEE Trans. Wireless Commun., vol. 51, no. 4, pp. 539-542, 2003. https://doi.org/10.1109/TCOMM.2003.810870
  27. H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part III., Wiley, 2001.
  28. W. A. Gardner, Cyclostationarity in Communications and Signal Processing, IEEE Press, 1994.
  29. W. A. Gardner and M. S. Spooner, "Signal interception: Performance advantages of cyclic-feature detectors," IEEE Trans. Wireless Commun., vol. 40, no. 1, pp. 149-159, 1992. https://doi.org/10.1109/26.126716
  30. A. Goldsmith, Wireless Communications, Cambridge University Press, 2005.