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http://dx.doi.org/10.3837/tiis.2013.05.011

Robust Spectrum Sensing for Blind Multiband Detection in Cognitive Radio Systems: A Gerschgorin Likelihood Approach  

Qing, Haobo (School of Electronic Engineering, Beijing University of Posts and Telecommunications)
Liu, Yuanan (School of Electronic Engineering, Beijing University of Posts and Telecommunications)
Xie, Gang (School of Information and Communication Engineering, Beijing University of Posts and Telecommunications)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.7, no.5, 2013 , pp. 1131-1145 More about this Journal
Abstract
Energy detection is a widely used method for spectrum sensing in cognitive radios due to its simplicity and accuracy. However, it is severely affected by the noise uncertainty. To solve this problem, a blind multiband spectrum sensing scheme which is robust to noise uncertainty is proposed in this paper. The proposed scheme performs spectrum sensing over the total frequency channels simultaneously rather than a single channel each time. To improve the detection performance, the proposal jointly utilizes the likelihood function combined with Gerschgorin radii of unitary transformed covariance matrix. Unlike the conventional sensing methods, our scheme does not need any prior knowledge of noise power or PU signals, and thus is suitable for blind spectrum sensing. In addition, no subjective decision threshold setting is required in our scheme, making it robust to noise uncertainty. Finally, numerical results based on the probability of detection and false alarm versus SNR or the number of samples are presented to validate the performance of the proposed scheme.
Keywords
Cognitive radio; spectrum sensing; energy detection; multiband detection; likelihood function;
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1 J. Mitola and G.Q. Maguire, "Cognitive radio: making software radios more personal," IEEE Pers. Commun., vol. 6, no. 4, pp. 13-18, 1999.   DOI   ScienceOn
2 S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201-220, 2005.   DOI   ScienceOn
3 B. Wang and K.J.R. Liu, "Advances in cognitive radio networks: a survey," IEEE J. Sel. Areas Signal Process., vol. 5, no. 1, pp. 5-23, 2011.   DOI   ScienceOn
4 D. Cabric, S.M. Mishra and R.W. Brodersen, "Implementation issues in spectrum sensing for cognitive radios," in Proc. of Asilomar Conf. Signals, Systems and Computers, vol. 1, pp. 772-776, Nov. 2004.
5 T. Yucek and H. Arslan, "A survey of spectrum sensing algorithms for cognitive radio applications," IEEE Commun. Surveys & Tutorials, vol. 11, no. 1, pp. 116-130, 2009.   DOI   ScienceOn
6 G. Ganesan and Y.(G.) Li, "Cooperative spectrum sensing in cognitive radio, part I and part II," IEEE Trans. Wireless Commun., vol. 6, no. 6, pp. 2204-2213 and vol. 6, no. 6, pp. 2214-2222, 2007.   DOI   ScienceOn
7 A.A. EI-Saleh, M. Ismail, M.A.M. Ali and I.H. Arka, "Hybrid SDF-HDF cluster-based fusion scheme for cooperative spectrum sensing in cognitive radio networks," KSII Trans. Internet and Information Systems, vol. 4, no. 6, pp. 1023-1041, 2010.
8 H. Urkowitz, "Energy detection of unknown deterministic signals," in Proc. of IEEE, vol. 55, no. 4, pp. 523-531, 1967.   DOI   ScienceOn
9 R. Tandra and A. Sahai, "SNR walls for signal detection," IEEE J. Sel. Topics Signal Process., vol. 2, no. 1, pp. 4-17, 2008.   DOI   ScienceOn
10 R. Tandra and A. Sahai, "Fundamental limits on detection in low SNR under noise uncertainty," in Proc. of IEEE Int. Conf. Wireless Networks, Commun. and Mobile Computing, pp. 464-469, Jun. 2005.
11 IEEE 802.22 Working Group, "Spectrum sensing requirements summary," Doc Num.22-06-0089-04-0000, http://grouper.ieee.org/groups/802/22/Meeting documents/2006 July/22-06-0089-04-0000.
12 A. Sahai and D. Cabric, "A tutorial on spectrum sensing: fundamental limits and practical challenges," in Proc. of IEEE Int. Symp. Dynamic Spectrum Access Networks, Nov. 2005.
13 A. Taherpour, S. Gazor and M. Nasiri-Kenari, "Invariant wideband spectrum sensing under unknown variances," IEEE Trans. Wireless Commun., vol. 8, no. 5, pp. 2182-2186, 2009.   DOI   ScienceOn
14 Z. Tian and G.B. Giannakis, "A wavelet approach to wideband spectrum sensing for cognitive radios," in Proc. of Int. Conf. Cognitive Radio Oriented Wireless Networks and Commun., Jun. 2006.
15 Y. Hur, J. Park, W. Woo, K. Lim, C.H. Lee, H.S. Kim and J. Laskar, "A wideband analog multi-resolution spectrum sensing technique for cognitive radio systems," in Proc. of IEEE Int. Symp. Circuits and Systems, pp. 4090-4093, May 2006.
16 Z. Quan, S. Cui, A.H. Sayed and H.V. Poor, "Optimal multiband joint detection for spectrum sensing in cognitive radio networks," IEEE Trans. Signal Process., vol. 57, no. 3, pp. 1128-1140, 2009.   DOI   ScienceOn
17 M. Wax and T. Kailath, "Detection of signals by information theoretic criteria," IEEE Trans. Acoust., Speech, Signal Process., vol. 33, no. 2, pp. 387-392, 1985.   DOI
18 H.T. Wu, J.F. Yang and F.K. Chen, "Source number estimation using transformed Gerschgorin radii," IEEE Trans. Signal Process., vol. 43,no. 6, pp. 1325-1333, 1995.   DOI   ScienceOn
19 E. Fisher and H.V. Poor, "Estimation of the number of sources in unbalanced arrays via information theoretic criteria," IEEE Trans. Signal Process., vol. 53, no. 9, pp. 3543-3553,2005.   DOI   ScienceOn
20 FCC Spectrum Policy Task Force, "Report of the spectrum efficiency working group," http://www.fcc.gov/sptf/files/SEWGFinalReport_1.pdf, 2002.
21 J.H. Wilkinso, "The algebraic eigenvalue problem," Oxford, 1965.
22 R.O.Schmidt, "A signal subspace approach to multiple emitter location and spectral estimation," Ph. D. dissertation, Stanford Univ., Stanford, CA, 1981.
23 M. Wax, "Detection and localization of multiple sources via the stochastic signals model," IEEE Trans. Signal Processing, vol. 39, pp. 2450-2456, Nov. 1991.   DOI