Wavelet-Based Spectrum Sensing for Cognitive Radios using Hilbert Transform
For cognitive radio networks, there is a major
spectrum sensing problem, i.e. dynamic spectrum management. It is
an important issue to sense and identify the spectrum holes in
cognitive radio networks. The first-order derivative scheme is usually
used to detect the edge of the spectrum. In this paper, a novel
spectrum sensing technique for cognitive radio is presented. The
proposed algorithm offers efficient edge detection. Then, simulation
results show the performance of the first-order derivative scheme and
the proposed scheme and depict that the proposed scheme obtains
better performance than does the first-order derivative scheme.
[1] S. Haykin, "Cognitive radio: Brain-empowered wireless
communications," IEEE J. Sel. Areas Commun., vol. 23, pp. 201-220,
Feb. 2005.
[2] S. Haykin, "Fundamental issues in cognitive radio," in Cognitive
Wireless Communication Networks, E. Hossain and V. K. Bhargava,
Eds. New York: Springer, 2007, pp. 1-43.
[3] S. Haykin, D. J. Thomsom, and J. H. Reed, "Spectrum Sensing for
cognitive radio," Proceedings of the IEEE, vol. 97, no.5, pp. 849-877,
May, 2009.
[4] I. J. Mitola and G. Q. Maguire, "Cognitive radio: making software radios
more personal," IEEE Personal Commun., vol. 6, pp. 13-18, Aug., 1999.
[5] T. Yucek and H. Arslan, "A Survey of Spectrum Sensing Algorithms for
Cognitive Radio Applications," IEEE Commun. Survey & Tutorials, vol.
11, no. 1, pp. 116-130, 2009.
[6] I. Budiarjo, M, K, Lakshmanan and H. Nikookar, "Cognitive Radio
Dynamic Access Techniques," Wireless Pers. Commun., vol. 45, pp.
293-324, Feb., 2008.
[7] Zhi Tian, Georgios B Giannakis, "A Wavelet Approach to Wideband
Spectrum Sensing for Cognitive Radios", IEEE 1st Int. Conf. on
Cognitive Radio Oriented Wireless Networks and Communications
(CROWNCOM), pp. 1-5, 2006.
[8] Y. L. Xu, H. S. Zhang and Z. H. Han, "The Performance Analysis of
Spectrum Sensing Algorithms Based on Wavelet Edge Detection," IEEE
1st Int. Conf. on Wireless Commun., Networking and Mobile Computing
(WiCOM), pp. 1-4, 2009.
[9] S. Mallat, W. Hwang, "Singularity detection and processing with
wavelets," IEEE Trans. Info. Theory, vol.38, no. 2, pp. 617-643, Mar.,
1992.
[1] S. Haykin, "Cognitive radio: Brain-empowered wireless
communications," IEEE J. Sel. Areas Commun., vol. 23, pp. 201-220,
Feb. 2005.
[2] S. Haykin, "Fundamental issues in cognitive radio," in Cognitive
Wireless Communication Networks, E. Hossain and V. K. Bhargava,
Eds. New York: Springer, 2007, pp. 1-43.
[3] S. Haykin, D. J. Thomsom, and J. H. Reed, "Spectrum Sensing for
cognitive radio," Proceedings of the IEEE, vol. 97, no.5, pp. 849-877,
May, 2009.
[4] I. J. Mitola and G. Q. Maguire, "Cognitive radio: making software radios
more personal," IEEE Personal Commun., vol. 6, pp. 13-18, Aug., 1999.
[5] T. Yucek and H. Arslan, "A Survey of Spectrum Sensing Algorithms for
Cognitive Radio Applications," IEEE Commun. Survey & Tutorials, vol.
11, no. 1, pp. 116-130, 2009.
[6] I. Budiarjo, M, K, Lakshmanan and H. Nikookar, "Cognitive Radio
Dynamic Access Techniques," Wireless Pers. Commun., vol. 45, pp.
293-324, Feb., 2008.
[7] Zhi Tian, Georgios B Giannakis, "A Wavelet Approach to Wideband
Spectrum Sensing for Cognitive Radios", IEEE 1st Int. Conf. on
Cognitive Radio Oriented Wireless Networks and Communications
(CROWNCOM), pp. 1-5, 2006.
[8] Y. L. Xu, H. S. Zhang and Z. H. Han, "The Performance Analysis of
Spectrum Sensing Algorithms Based on Wavelet Edge Detection," IEEE
1st Int. Conf. on Wireless Commun., Networking and Mobile Computing
(WiCOM), pp. 1-4, 2009.
[9] S. Mallat, W. Hwang, "Singularity detection and processing with
wavelets," IEEE Trans. Info. Theory, vol.38, no. 2, pp. 617-643, Mar.,
1992.
@article{"International Journal of Electrical, Electronic and Communication Sciences:50404", author = "Shiann-Shiun Jeng and Jia-Ming Chen and Hong-Zong Lin and Chen-Wan Tsung", title = "Wavelet-Based Spectrum Sensing for Cognitive Radios using Hilbert Transform", abstract = "For cognitive radio networks, there is a major
spectrum sensing problem, i.e. dynamic spectrum management. It is
an important issue to sense and identify the spectrum holes in
cognitive radio networks. The first-order derivative scheme is usually
used to detect the edge of the spectrum. In this paper, a novel
spectrum sensing technique for cognitive radio is presented. The
proposed algorithm offers efficient edge detection. Then, simulation
results show the performance of the first-order derivative scheme and
the proposed scheme and depict that the proposed scheme obtains
better performance than does the first-order derivative scheme.", keywords = "cognitive radio, Spectrum Sensing, wavelet, edgedetection", volume = "5", number = "3", pages = "258-5", }