Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Traffic Analysis of a Cognitive Radio Network Based on the Concept of Medium Access Probability

  • Khan, Risala T. (Institute of Information Technology, Jahangirnagar University) ;
  • Islam, Md. Imdadul (Department of Computer Science and Engineering, Jahangirnagar University) ;
  • Amin, M.R. (Department of Electronics and Communications Engineering, East West University)
  • Received : 2013.10.04
  • Accepted : 2014.03.31
  • Published : 2014.12.31
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Abstract

The performance of a cognitive radio network (CRN) solely depends on how precisely the secondary users can sense the presence or absence of primary users. The incorporation of a spatial false alarm makes deriving the probability of a correct decision a cumbersome task. Previous literature performed this task for the case of a received signal under a Normal probability density function case. In this paper we enhance the previous work, including the impact of carrier frequency, the gain of antennas on both sides, and antenna heights so as to observe the robustness against noise and interference and to make the correct decision of detection. Three small scale fading channels: Rayleigh, Normal, and Weibull were considered to get the real scenario of a CRN in an urban area. The incorporation of a maximal-ratio combining and selection combing with a variation of the number of received antennas have also been studied in order to achieve the correct decision of spectral sensing, so as to serve the cognitive users. Finally, we applied the above concept to a traffic model of the CRN, which we based on a two-dimensional state transition chain.

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References

  1. J. Mitola, "Cognitive radio: an integrated agent architecture for software defined radio," Ph.D. dissertation, Royal Institute of Technology (KTH), Stockholm, Sweden, 2000.
  2. Y. C. Liang, K. C. Chen, G. Li, and P. Mahonen, "Cognitive radio networking and communications: an overview," IEEE Transactions on Vehicular Technology, vol. 60, no. 7, pp. 3386-3407, Sep. 2011. https://doi.org/10.1109/TVT.2011.2158673
  3. K. B. Letaief and W. Zhang, "Cooperative communications for cognitive radio networks," Proceedings of the IEEE, vol. 97, no. 5, pp. 878-893, May 2009. https://doi.org/10.1109/JPROC.2009.2015716
  4. Y. Zeng, Y. C. Liang, A. T. Hoang, and R. Zhang, "A review on spectrum sensing for cognitive radio: challenges and solutions," EURASIP Journal on Advances in Signal Processing, vol. 2010, Jan. 2010.
  5. T. Yucek and H. Arslan, "A survey of spectrum sensing algorithms for cognitive radio applications," IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 116-130, 2009. https://doi.org/10.1109/SURV.2009.090109
  6. J. Ma, G. Y. Li, and B. H. Juang, "Signal processing in cognitive radio," Proceedings of the IEEE, vol. 97, no. 5, pp. 805-823, May 2009. https://doi.org/10.1109/JPROC.2009.2015707
  7. R. Tandra, S. Mishra, and A. Sahai, "What is a spectrum hole and what does it take to recognize one," Proceedings of the IEEE, vol. 97, no. 5, pp. 824-848, May 2009. https://doi.org/10.1109/JPROC.2009.2015710
  8. A. Ghasemi and E. S. Sousa, "Spectrum sensing in cognitive radio networks: the cooperationprocessing tradeoff," Wireless Communications and Mobile Computing, vol. 7, no. 9, pp. 1049-1060, Nov. 2007. https://doi.org/10.1002/wcm.480
  9. A. Ghasemi and E. S. Sousa, "Interference aggregation in spectrum-sensing cognitive wireless networks," IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 41-56, 2008. https://doi.org/10.1109/JSTSP.2007.914897
  10. I. F. Akyildiz, W. M. Lee, M. C. Vuran and S. Mohanty, "Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey," Computer Networks, vol. 50, no. 13, pp. 2127- 2159, 2006. https://doi.org/10.1016/j.comnet.2006.05.001
  11. Z. Li, F. R. Yu, and M. Huang, "A distributed consensus-based cooperative spectrum-sensing scheme in cognitive radios," IEEE Transactions on Vehicular Technology, vol. 59, no. 1, pp. 383-393, Jan. 2010. https://doi.org/10.1109/TVT.2009.2031181
  12. Y. Yang and S. Aissa, "Cross-layer combining of information-guided transmission with network coding relaying for multiuser cognitive radio system," IEEE Wireless Communications Letters, vol. 2, no. 1, pp. 26-29, Feb. 2013. https://doi.org/10.1109/WCL.2012.100912.120629
  13. W. Han, J. Li, Z. Li, J. Si, and Y. Zhang, "Spatial false alarm in cognitive radio network," IEEE Transactions on Signal Processing, vol. 61, no. 6, pp. 1375-1388, Mar. 2013. https://doi.org/10.1109/TSP.2012.2236833
  14. W. Han, J. Li, Q, Liu, and L. Zhao, "Spatial false alarms in cognitive radio," IEEE Communications Letters, vol. 15, no. 5, pp. 518-520, May 2011. https://doi.org/10.1109/LCOMM.2011.031411.102473
  15. M. Rezwan Ahmad, I. Islam, and M. R. Amin, "Determination of medium access probability of cognitive radio under different fading channels," International Journal of Soft Computing and Engineering, vol. 2, no. 3, pp. 459-463, Jul. 2012.
  16. A. Medeisis and A. Kajackas, "On the use of the universal Okumura-Hata propagation prediction model in rural areas," in Proceedings of the IEEE 51st Vehicular Technology Conference, Tokyo, Japan, 2000, pp. 1815-1818.
  17. F. J. Oluwole and O. Y. Olajide, "Radio frequency propagation mechanisms and empirical models for hilly areas," International Journal on Electrical and Electronics Engineering, vol. 3, no. 3, pp. 372- 376, Jun.2013.
  18. N. Shabbir, M. T. Sadiq, H. Kashif, and R. Ullah, "Comparison of radio propagation models for long term evolution (LTE) network," International Journal of Next-Generation Networks, vol. 3, no. 3, pp. 27-41, Sep. 2011. https://doi.org/10.5121/ijngn.2011.3303
  19. W. C. Y. Lee, Mobile Cellular Telecommunications: Analog and Digital Systems, 2nd ed. New York, NY: McGraw-Hill, 1995.
  20. L. Nissirat, M. Ismail, and M. A. Nisirat, "Macro-cell path loss prediction, calibration, and optimization by Lee's model for south of Amman city, Jordan at 900, and 1800 MHz," Journal of Theoretical and Applied Information Technology, vol. 41, no. 2, pp. 253-258, Jul. 2012.
  21. M. Alshami, T. Arslan, J. Thompson, and A. T. Erdogan, "Frequency analysis of path loss models on WIMAX," in Proceedings of the 3rd Computer Science and Electronic Engineering Conference (CEEC2011), Colchester, UK, 2011, pp. 1-6.
  22. P. M. Ghosh, M. A. Hossain, A. Z. Abadin, and K. K. Karmakar, "Comparison among different large scale path loss models for high sites in urban, suburban and rural areas," International Journal of Soft Computing and Engineering, vol. 2, no. 2, pp. 287-290, May 2012.
  23. M. Hamid and I. Kostanic, "Path loss models for LTE and LTE-A relay stations," Universal Journal of Communications and Network, vol. 1, no. 4, pp. 119-126, 2013.
  24. M. K. Steven, Fundamentals of Statistical Signal Processing. Upper Saddle River, NJ: Prentice-Hall, 2011.
  25. B. Jabbari and W. F. Fuhrmann, "Teletraffic modeling and analysis of flexible hierarchical cellular networks with speed-sensitive handoff strategy," IEEE Journal on Selected Areas in Communications, vol. 15, no. 8, pp. 1539-1548, Sep. 2006.
  26. F. N. Pavlidou, "Two-dimensional traffic models for cellular mobile systems," IEEE Transactions on Communications, vol. 42, no. 234, pp. 1505-1511, 1994. https://doi.org/10.1109/TCOMM.1994.582831
  27. P. Fitzpatrick, C. S. Lee, and B. Warfield, "Teletraffic performance of mobile radio networks with hierarchical cells and overflow," IEEE Journal on Selected Areas in Communications, vol. 15, no. 8, pp. 1549-1557, 1997. https://doi.org/10.1109/49.634793
  28. Y. R. Haung, Y. B. Lin, and J. M. Ho, "Performance analysis for voice/data integration on a finitebuffer mobile system," IEEE Transactions on Vehicular Technology, vol. 49, no. 2, pp. 367-378, 2000. https://doi.org/10.1109/25.832967
  29. M. D. Kulavaratharasah and A. H. Aghvami, "Teletraffic performance evaluation of microcellular personal communication networks (PCN's) with prioritized handoff procedures," IEEE Transactions on Vehicular Technology, vol. 48, no. 1, pp. 137-152, 1999. https://doi.org/10.1109/25.740076

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