A HOS-Based Blind Spectrum Sensing in Noise Uncertainty
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2015.9.1.2Abstract
Spectrum sensing for cognitive radio is a challenging task since it has to be able to detect the primary signal at a low signal to noise ratio (SNR). At a low SNR, the variance of noise fluctuates due to noise uncertainty. Detection of the primary signal will be difficult especially for blind spectrum sensing methods that rely on the variance of noise for their threshold setting, such as energy detection. Instead of using the energy difference, we propose a spectrum sensing method based on the distribution difference. When the channel is occupied, the distribution of the received signal, which propagates under a wireless fading channel, will have a non-Gaussian distribution. This will be different from the Gaussian noise when the channel is vacant. Kurtosis, a higher order statistic (HOS) of the 4th order, is used as normality test for the test statistic. We measured the detection rate of the proposed method by performing a simulation of the detection process. Our proposed method's performance proved superior in detecting a real digital TV signal in noise uncertainty.
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References
Mitola, J. & Maguire, G., Cognitive Radio: Making Software Radios More Personal, IEEE Personal Communications, 6(3), pp. 13-18, 1999.
Haykin, S., Cognitive Radio: Brain-Empowered Wireless Communications, IEEE Journal on Selected Areas in Communications, 23(2), pp.201-220, 2005.
Haykin, S., Cognitive Radar: a Way of the Future, IEEE Signal Processing Magazine, 23(1), pp. 30-40, 2006.
Inggs, M., Passive Coherent Location as Cognitive Radar, IEEE Aerospace and Electronic Systems Magazine, 25(5), pp. 12-17, May 2010.
Tarchi, D., Guidotti, A., Icolari, V., Vanelli-Coralli, A., Sharma, S.,Chatzinotas, S., Malekil, S., Evans, B., Thompson, P., Tang, W. & Grotz, J., Technical Challenges for Cognitive Radio Application In Satellite Communications, 9th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), Oulu-Finland, European Alliance for Innovation (EAI), pp. 136-142.June 2014.
Harada, H., White Space Communication Systems: an Overview of Regulation, Standardization and Trial, IEICE Transactions on Communications, E97-B(2), pp. 261-274, 2014.
Chang, K.H., IEEE 802 Standards for Tv White Space, IEEE Wireless Communications, 21(2), pp. 4-5, April 2014.
Yucek, T. & Arslan, H., A Survey of Spectrum Sensing Algorithm for Cognitive Radio Applications, IEEE Communications Surveys and Tutorials, 11(1), pp. 116-130, 2009.
Urkowitz, H., Energy Detection of Unknown Deterministic Signals, Proc. IEEE, 55(4), pp. 523-531, 1967.
Danev, D., Axell, E. & Larsson, E.,Spectrum Sensing Methods for Detection of Dvb-T Signals in Awgn and Fading Channels, in Proc. 2010 IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Istanbul-Turkey, IEEE Comm. Society, 2010.
Song, C., Matsumura, T. & Harada, H., A Prototype of TV White Space Spectrum Sensing and Power Measurements, IEICE Transactions on Communications, E97-B(2), pp. 314, 2014.
Tandra, R. & Sahai, A., SNR Walls for Signal Detection, IEEE Journal of Selected Topics in Signal Processing, 2(1), pp. 4-17, 2008.
Chen, Y., Improved Energy Detector for Random Signals in Gaussian Noise, IEEE Transactions on Wireless Communications, 9(2), pp. 558-563, 2010.
Singh, A., Bhatnagar, M. & Mallik, R., Cooperative Spectrum Sensing in Multiple Antenna Based Cognitive Radio Network Using an Improved Energy Detector, IEEE Communication Letters, 16(1), January 2012.
Nallagonda, S., Chandra, A., Roy, S. & Kundu, S., Performance of Improved Energy Detector Based Cooperative Spectrum Sensing over Hoyt and Rician Faded Channels, IEICE Communications Express, 2(7), pp. 319-324, July 2013.
Lu, L., Wu, H.-C. & Iyengar, S.S., A Novel Robust Detection Algorithm for Spectrum Sensing, IEEE Journal on Selected Areas in Communications, 29(2), pp. 305-315, February 2011.
Shen, L., Wang, H., Zhang, W. & Zhao, Z., Blind Spectrum Sensing for Cognitive Radio Channels with Noise Uncertainty, IEEE Transactions on Wireless Communications, 10(6), pp. 1721-1724, 2011.
Kay, S.M., Fundamentals of Statistical Signal Processing: Detection Theory, Prentice-Hall, New Jersey, 1998.
Chen, K. & Prasad, R., Cognitive Radio Networks, John Wiley, West Sussex, 2009.
Shellhammer, S. & Tandra, R., Performance of the Power Detector with Noise Uncertainty, IEEE 802.22-06/0134r0, Tech. Rep., 2006.
Taub, H. & Schilling, D.L., Principles of Communication Systems, McGraw-Hill, New York, 1989.
Hyvarinen, A., Independent Component Analysis, Wiley-Interscience, New York, 2001.
Garrison, I.M, Martin, R.K., Sethares, W.A., Hart, B., Chung, W., Balakrishnan, J., Casas, R.A., Endres, T.J., Larimore, M., Schniter, P. & Johnson, C.R., DTV Channel Characterization, Conference on Information Sciences and Systems (CISS), Maryland, The John Hopkins University, 2001.
