Browse > Article
http://dx.doi.org/10.1109/JCN.2014.000027

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)
Publication Information
Journal of Communications and Networks / v.16, no.2, 2014 , pp. 162-171 More about this Journal
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
Improved MC detector; MGF; Nakagami-m fading; Rayleigh fading; Rayleigh fading and lognormal shadowing; Rician fading; SLC; spectrum sensing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.   DOI   ScienceOn
2 A. Goldsmith, Wireless Communications, Cambridge University Press, 2005.
3 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.
4 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.   DOI   ScienceOn
5 C. Loo, "Digital Transmission through a land mobile satellite channel," IEEE Trans. Wireless Commun., vol. 38, pp. 693-697, 1990.   DOI   ScienceOn
6 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.   DOI   ScienceOn
7 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.
8 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.   DOI   ScienceOn
9 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.   DOI   ScienceOn
10 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.   DOI   ScienceOn
11 H. Sadeghi and P. Azmi, "Cyclostationarity-based cooperative spectrum sensing for cognitive radio networks," in Proc. IEEE IST, Aug. 2008.
12 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.
13 H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part III., Wiley, 2001.
14 W. A. Gardner, Cyclostationarity in Communications and Signal Processing, IEEE Press, 1994.
15 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.
16 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.
17 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.   DOI   ScienceOn
18 H. Arslan, "Cognitive radio, software defined radio, and adaptive wireless systems," Springer, 2007.
19 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.   DOI
20 S. Haykin, "Cognitive radio: Brain-empowered wireless communications," IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201-220, Feb. 2005.   DOI   ScienceOn
21 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.   DOI   ScienceOn
22 S.M.Mishra, A. Sahai, and R.W. Brodersen, "Cooperative sensing among cognitive radios," Int. Conf. Communications, Istanbul, Turkey, June 2006.
23 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.   DOI   ScienceOn
24 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.   DOI   ScienceOn
25 R. Tandra and A. Sahai, "Fundamental limits on detection in low SNR under noise uncertainty," in Proc. Wireless Commun. Symp. on Signal Processing, 2005.
26 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.
27 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.   DOI   ScienceOn
28 J. Mitola, "Cognitive radio: An integrated agent architecture for software defined radio," Ph.D. dissertation, KTH Royal Institute of Technology, Sweden, May 2000.
29 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.   DOI
30 W. A. Gardner, "Signal interception: A unifying theoretical framework for feature detection," IEEE Trans. Commun., vol. 36, pp. 897-906, 1988.   DOI   ScienceOn