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Reseach Article

Sensing Algorithm for Cognitive Radio Networks based on Random Data Matrix

by Rohitha Ujjinimatad, Siddarama R Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 3
Year of Publication: 2013
Authors: Rohitha Ujjinimatad, Siddarama R Patil
10.5120/10063-4658

Rohitha Ujjinimatad, Siddarama R Patil . Sensing Algorithm for Cognitive Radio Networks based on Random Data Matrix. International Journal of Computer Applications. 62, 3 ( January 2013), 32-37. DOI=10.5120/10063-4658

@article{ 10.5120/10063-4658,
author = { Rohitha Ujjinimatad, Siddarama R Patil },
title = { Sensing Algorithm for Cognitive Radio Networks based on Random Data Matrix },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 3 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 32-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number3/10063-4658/ },
doi = { 10.5120/10063-4658 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:10:43.686343+05:30
%A Rohitha Ujjinimatad
%A Siddarama R Patil
%T Sensing Algorithm for Cognitive Radio Networks based on Random Data Matrix
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 3
%P 32-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Signal detection is a fundamental problem in Cognitive radio. In this paper a new statistical test is proposed based on random data matrix (RDM) for detecting the signals in noise, as opposed to the eigenvalue based tests. Among the many spectrum sensing methods, the RDM method detects the primary users without any prior information. The performance of the test is compared with energy detection (ED), covariance absolute value (CAV) and eigenvalue based algorithms through simulation analysis. This sensing algorithm can be used for very low SNR signal detection without requiring the knowledge of signal, channel and noise. Simulations are based on wireless microphone and identically and independently distributed (iid) signals.

References
  1. J. Mitola and G. Q. Maguire, "Cognitive radios: making software radios more personal," IEEE Personal Communications, vol. 6, no. 4, pp. 13–18, 1999.
  2. S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE Trans. Communications, vol. 23, no. 2, pp. 201–220, 2005.
  3. FCC, "Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies, notice of proposed rule making and order," in FCC 03-322, Dec. 2003.
  4. 802. 22 Working Group, IEEE P802. 22/D0. 1 Draft Standard for Wireless Regional Area Networks. http://grouper. ieee. org/groups/802/22/, May 2006.
  5. Parthapratim De and Ying - Chang Liang, "Blind Spectrum Sensing Algorithms for Cognitive Radio Networks,"IEEE Transactions on Vehicular Technology, vol. 57, no. 5, September 2008
  6. R. Tandra and A. Sahai, " Fundamental limits on detection in low SNR under noise uncertainty," in Proc. 2005 Int. Conf. Wireless Netw. , Commun. , Mobile Comput. , Jun. 13–16, 2005, vol. 1, pp. 464–469.
  7. A. Sonnenschien and P. M. Fishman, "Radiometric detection of spread-spectrum signals in noise of uncertain power," IEEE Trans. Aerosp. Electron. Syst. , vol. 28, no. 3, pp. 654–660, Jul. 1992.
  8. A. Sahai and D. Cabric, "Spectrum sensing: fundamental limits and practical challenges," in Proc. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), (Baltimore, MD), Nov. 2005.
  9. R. Tandra and A. Sahai, "Fundamental limits on detection in low SNR under noise uncertainty," in Wireless Com 2005, (Maui, HI), June 2005.
  10. S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory, vol. 2. Prentice Hall, 1998.
  11. H. Urkowitz, "Energy detection of unkown deterministic signals," Proceedings of the IEEE, vol. 55, no. 4, pp. 523–531, 1967.
  12. D. Cabric, A. Tkachenko, and R. W. Brodersen, "Spectrum sensing measurements of pilot,energy, and collaborative detection," in Military Comm. Conf. (MILCOM), pp. 1–7, Oct. 2006.
  13. H. S. Chen, W. Gao, and D. G. Daut, "Signature based spectrum sensing algorithms for IEEE 802. 22 WRAN," in IEEE Intern. Conf. Comm. (ICC), June 2007.
  14. W. A. Gardner, "Exploitation of spectral redundancy in cyclostationary signals," IEEE Signal Processing Magazine, vol. 8, pp. 14–36, 1991.
  15. W. A. Gardner, W. A. Brown, III, and C. -K. Chen, "Spectral correlation of modulated signals: part ii digital modulation," IEEE Trans. Communications, vol. 35, no. 6, pp. 595–601, 1987.
  16. N. Han, S. H. Shon, J. O. Joo, and J. M. Kim, "Spectral correlation based signal detection method for spectrum sensing in IEEE 802. 22 WRAN systems," in Intern. Conf. Advanced Communication Technology, (Korea), Feb. 2006.
  17. Y. Zeng, Choo Leng Koh, and Y. Liang, "Maximum eigenvalue detection: theory and application," IEEE International Conference on Communications, Beijing, 2008, pp. 4160-4164.
  18. Yonghong Zeng and Ying-Chang Liang, "Spectrum Sensing Algorithms for Cognitive Radio Based on Statistical Covariances," IEEE Transactions on Vehicular Technology, vol. 58, pp. 1804-1815, May 2009.
  19. Y. Zeng and Y. Liang, "Covariance Based Signal Detections for Cognitive Radio," Proc. IEEE Int. Symp. on New Frontiers in Dynamic Spectrum Access Networks, DySPAN, April 2007.
  20. Y. Zeng and Y. -C. Liang, "Eigenvalue-based sectrum sensing algorithms for cognitive radio," IEEE Trans. Commun. , vol. 57, no. 6, pp. 1784– 1793, Jun. 2009.
  21. Yonghong Zeng and YingChang Liang, Sep2007, Maximum-minimum eigenvalue detection for cognitive radio, Singapore: IEEE 18th Int. Symp. Pers. Indoor Mobile Radio Commun.
Index Terms

Computer Science
Information Sciences

Keywords

Cognitive radio Random data matrix Spectrum sensing Sphericity test sensing algorithms