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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Fast Spectrum Sensing with Coordinate System in Cognitive Radio Networks

  • Lee, Wilaiporn (Electrical Engineering, Department of Electrical and Computer Engineering, the Faculty of Engineering, King Mongkut's University of Technology North Bangkok) ;
  • Srisomboon, Kanabadee (Electrical Engineering, Department of Electrical and Computer Engineering, the Faculty of Engineering, King Mongkut's University of Technology North Bangkok) ;
  • Prayote, Akara (Department of Computer and Information Science, the Faculty of Applied Science, King Mongkut's University of Technology North Bangkok)
  • Received : 2014.06.07
  • Accepted : 2014.12.26
  • Published : 2015.05.01
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Abstract

Spectrum sensing is an elementary function in cognitive radio designed to monitor the existence of a primary user (PU). To achieve a high rate of detection, most techniques rely on knowledge of prior spectrum patterns, with a trade-off between high computational complexity and long sensing time. On the other hand, blind techniques ignore pattern matching processes to reduce processing time, but their accuracy degrades greatly at low signal-to-noise ratios. To achieve both a high rate of detection and short sensing time, we propose fast spectrum sensing with coordinate system (FSC) - a novel technique that decomposes a spectrum with high complexity into a new coordinate system of salient features and that uses these features in its PU detection process. Not only is the space of a buffer that is used to store information about a PU reduced, but also the sensing process is fast. The performance of FSC is evaluated according to its accuracy and sensing time against six other well-known conventional techniques through a wireless microphone signal based on the IEEE 802.22 standard. FSC gives the best performance overall.

Keywords

References

  1. A.M. Wyglinski, M. Nekovee, and Y.T. Hou, "Cognitive Radio Communications and Networks: Principles and Practice," Burlington, MA, USA: Elsevier Science Ltd, 2009.
  2. I.F. Akyildiz et al., "A Survey on Spectrum Management in Cognitive Radio Networks," IEEE Commun. Mag., vol. 46, no. 4, Apr. 2008, pp. 40-48. https://doi.org/10.1109/MCOM.2008.4481339
  3. R. Ranganathan et al., "Cognitive Radio for Smart Grid: Theory, Algorithms, and Security," Int. J. Digital Multimedia Broadcast., vol. 2011, 2011.
  4. I.F. Akyildiz, W.-Y. Lee, and K.R. Chowdhury, "CRAHNs: Cognitive Radio Ad Hoc Networks," Ad Hoc Netw., vol. 7, no. 5, July 2009, pp. 810-836. https://doi.org/10.1016/j.adhoc.2009.01.001
  5. Y. Liang et al., "Cognitive Radio Networking and Communications: An Overview," IEEE Trans. Veh. Technol., vol. 60, no. 7, Sept. 2011, pp. 3386-3407. https://doi.org/10.1109/TVT.2011.2158673
  6. I.F. Akyildiz et al., "Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey," Comput. Netw., vol. 50, no. 13, Sept. 2006, pp. 2127-2159. https://doi.org/10.1016/j.comnet.2006.05.001
  7. B. Wang and K.J.R. Liu, "Advances in Cognitive Radio Networks: A Survey," IEEE J. Sel. Topics Signal Process., vol. 5, no. 1, Feb. 2011, pp. 5-23. https://doi.org/10.1109/JSTSP.2010.2093210
  8. C. Cordeiro et al., "IEEE 802.22: An Introduction to the First Wireless Standard Based on Cognitive Radios," J. Commun., vol. 1, no. 1, 2006, pp. 38-47.
  9. S.J. Shellhammer and G. Chouinard, "Spectrum Sensing Requirements Summary," IEEE 802.22-06/0089rl, June 2006.
  10. T. Yucek and H. Arslan, "A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications," IEEE Commun. Surveys Tutorials, vol. 11, no. 1, 2009, pp. 116-130. https://doi.org/10.1109/SURV.2009.090109
  11. L. Lu et al., "Ten Years of Research in Spectrum Sensing and Sharing in Cognitive Radio," EURASIP J. Wireless Commun. Netw., Jan. 2012, pp. 28-43.
  12. A. Bagwari and B. Singh, "Comparative Performance Evaluation of Spectrum Sensing Techniques for Cognitive Radio Networks," Int. Conf. CICN, Mathura, India, Nov. 3-5, 2012, pp. 98-105.
  13. S.A. Malik et al., "Comparative Analysis of Primary Transmitter Detection Based Spectrum Sensing Techniques in Cognitive Radio Systems," Australian J. Basic Appl. Sci., vol. 4, no. 9, 2010, pp. 4522-4531.
  14. H. Yousry et al., Wireless Microphone Sensing Using Cyclostationary Detector. Accessed June 16, 2012. http://confscoop.org/inct-2012/14_yousry_Hatem.pdf
  15. W. Ejaz et al., "Improved Local Spectrum Sensing for Cognitive Radio Networks," EURASIP J. Adv. Signal Process., vol. 2012, Nov. 2012, pp. 242-251. https://doi.org/10.1186/1687-6180-2012-242
  16. A.K. Dey and A. Banerjee, "On Primary User Detection Using Energy Detection Technique for Cognitive Radio," National Conf. Commun., Guwahati, India, Jan. 16-18, 2009.
  17. Y. Zeng, C. Koh, and Y. Liang, "Maximum Eigenvalue Detection: Theory and Application," IEEE Int. Conf. Commun., Beijing, China, May 19-23, 2008, pp. 4160-4164.
  18. Y. Zeng and Y. Liang, "Spectrum-Sensing Algorithms for Cognitive Radio Based on Statistical Covariances," IEEE Trans. Veh. Technol., vol. 58, no. 4, May 2009, pp. 1804-1815. https://doi.org/10.1109/TVT.2008.2005267
  19. D. Simunic and T.S. Dhope, "Hybrid Detection Method for Spectrum Sensing in Cognitive Radio," Proc. Int. Convention MIPRO, Opatija, Croatia, May 21-25, 2012, pp. 765-770.
  20. A. Mate, K.-H. Lee, and I.-T. Lu, "Spectrum Sensing Based on Time Covariance Matrix Using GNU Radio and USRP for Cognitive Radio," IEEE Long Island Syst. Appl. Technol. Conf., Farmingdale, NY, USA, May 6, 2011, pp. 1-6.
  21. Y. Zeng and Y. Liang, "Maximum-Minimum Eigenvalue Detection for Cognitive Radio," IEEE Int. Symp. PIMRC, Athens, Greece, Sept. 3-7, 2007, pp. 1-5.
  22. Y. Zeng and Y.C. Liang, "Eigenvalue-Based Spectrum Sensing Algorithms for Cognitive Radio," IEEE Trans. Commun., vol. 57, no. 6, June 2009, pp. 1784-1793. https://doi.org/10.1109/TCOMM.2009.06.070402
  23. A.T. Teshome, "FPGA-Based Eigenvalue Detection Algorithm for Cognitive Radio," M.S. thesis, Dept. of Electron., Eng., University of Gavle, Sweden, 2010.
  24. P. Zhang and R.C. Qiu, "GLRT-Based Spectrum Sensing with Blindly Learned Feature under Rank-1 Assumption," IEEE Trans. Commun., vol. 61, no. 1, Jan. 2013, pp. 87-96. https://doi.org/10.1109/TCOMM.2012.100912.120162
  25. C. Clanton, M. Kenkel, and Y. Tang, "Wireless Microphone Signal Simulation Method," IEEE 802.22-07/0124r0, Mar. 2007.
  26. L.I Smith, A Tutorial on Principal Component Analysis., Accessed Feb. 26, 2012. http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
  27. K. Srisomboon et al., "A Performance Comparison of Two PWS Filters in Different Domain for Image Reconstruction Technique under Different Image Types," Int. Conf. ECTI-CON, Hua Hin, Thailand, May 16-18, 2012, pp. 168-171.
  28. Introductory Mathematics of the Analytic Hierarchy Process. Accessed Sept. 25, 2012. http://www.business.pitt.edu/faculty/papers/saaty-into-to-ahp-mathematics.pdf

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