Journal of Information Processing Systems

  • 제12권2호
  • /
  • Pages.295-309
  • /
  • 2016
  • /
  • 1976-913X(pISSN)
  • /
  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

The Effect of Multiple Energy Detector on Evidence Theory Based Cooperative Spectrum Sensing Scheme for Cognitive Radio Networks

  • 투고 : 2014.12.14
  • 심사 : 2015.02.16
  • 발행 : 2016.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Spectrum sensing is an essential function that enables cognitive radio technology to explore spectral holes and resourcefully access them without any harmful interference to the licenses user. Spectrum sensing done by a single node is highly affected by fading and shadowing. Thus, to overcome this, cooperative spectrum sensing was introduced. Currently, the advancements in multiple antennas have given a new dimension to cognitive radio research. In this paper, we propose a multiple energy detector for cooperative spectrum sensing schemes based on the evidence theory. Also, we propose a reporting mechanism for multiple energy detectors. With our proposed system, we show that a multiple energy detector using a cooperative spectrum sensing scheme based on evidence theory increases the reliability of the system, which ultimately increases the spectrum sensing and reduces the reporting time. Also in simulation results, we show the probability of error for the proposed system. Our simulation results show that our proposed system outperforms the conventional energy detector system.

키워드

참고문헌

  1. S. P Algur and N. P Kumar, "Novel user centric, game theory based bandwidth allocation mechanism in WiMAX," Human-Centric Computing and Information Sciences, vol. 3, no. 20, pp. 1-20, 2013. https://doi.org/10.1186/2192-1962-3-1
  2. G. H. S. Carvalho, I. Woungang, A. Anpalagan, and S. K. Dhurandher, "Energy-efficient radio resource management scheme for heterogeneous wireless networks: a queueing theory perspective," Journal of Convergence, vol. 3, no. 4. pp. 15-22, 2012.
  3. Federal Communications Commission, "Notice of proposed rule making and order," Federal Communications Commission, Washington, DC, FCC 03-322, 2003.
  4. A. Gorcin and B. Thiagarajan, "A signal identification application for cognitive radio," in Proceedings of SDR Forum Technical Conference, Denver, CO, 2007.
  5. U. Habiba, Md. I. Islam, and M. R. Amin, "Performance evaluation of the VoIP services of the cognitive radio system, based on DTMC," Journal of Information Processing Systems, vol. 10, no. 1, pp. 119-131, 2014. https://doi.org/10.3745/JIPS.2014.10.1.119
  6. R. Ujjinimatad and S. R. Patil, "Semi-blind detection method for cognitive radio networks in multiple antenna systems," in Proceedings of 2013 Annual International Conference on Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy (AICERA/ICMiCR), Kanjirapally, India, 2013, pp. 1-6.
  7. A. Bagwari and G. S. Tomar, "Improved spectrum sensing technique using multiple energy detectors for cognitive radio networks," International Journal of Computer Applications, vol. 62, no. 4, pp. 11-21, 2013. https://doi.org/10.5120/10067-4666
  8. H. Elshaafi and D. Botvich, "Trustworthiness inference of multi-tenant component services in service compositions," Journal of Convergence, vol. 4, no. 1, pp. 31-37, 2013. https://doi.org/10.15207/JKCS.2013.4.4.031
  9. A. Sinha and D. K. Lobiyal, "Performance evaluation of data aggregation for cluster-based wireless sensor network," Human-Centric Computing and Information Sciences, vol. 3, no. 1, pp. 1-17, 2013. https://doi.org/10.1186/2192-1962-3-1
  10. M. Yoon, Y. K. Kim, and J. W. Chang, "An energy-efficient routing protocol using message success rate in wireless sensor networks," Journal of Convergence, vol. 4, no. 1, pp. 15-22, 2013.
  11. S. K. Bae, "Power consumption analysis of prominent time synchronization protocols for wireless sensor networks," Journal of Information Processing Systems, vol. 10, no. 2, pp. 300-313, 2014. https://doi.org/10.3745/JIPS.03.0006
  12. S. M. A. Zaidi and B. Song, "Prioritized multipath video forwarding in WSN," Journal of Information Processing Systems, vol. 10, no. 2, pp. 176-192, 2014. https://doi.org/10.3745/JIPS.03.0002
  13. N. Nguyen-Thanh and I. Koo, "An enhanced cooperative spectrum sensing scheme based on evidence theory and reliability source evaluation in cognitive radio context," IEEE Communications Letters, vol. 13, no. 7, pp. 492-494, 2009. https://doi.org/10.1109/LCOMM.2009.090043
  14. Q. Peng, K. Zeng, J. Wang, and S. Li, "A distributed spectrum sensing scheme based on credibility and evidence theory in cognitive radio context," in Proceedings of 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, Helsinki, Finland, 2006, pp. 1-5.
  15. N. Nguyen-Thanh and I. Koo, "An efficient ordered sequential cooperative spectrum sensing scheme based on evidence theory in cognitive radio," IEICE Transactions on Communications, vol. 93B, no. 12, pp. 3248-3257, 2010.
  16. J. H. Chong, C. K. Ng, N. K. Noordin, and B. M. Ali, "A low computational complexity V-BLAST/STBC detection mechanism in MIMO system," Human-centric Computing and Information Sciences, vol. 4, no. 1, pp. 1-28, 2014. https://doi.org/10.1186/2192-1962-4-1
  17. J. H. Chong, C. K. Ng, N. K. Noordin, and B. M. Ali, "Dynamic transmit antenna shuffling scheme for MIMO wireless communication systems," Journal of Convergence, vol. 4, no. 1, pp. 7-14, 2013.
  18. R. F. Ustok, and B. Ozbek, "Spectrum sensing based on energy detection for cognitive radio systems with multiple antennas," in Proceedings of IEEE 17th Signal Processing and Communications Applications Conference (SIU2009), Antalya, Turkey, 2009, pp. 396-399.
  19. A. Salarvan and G. K. Kurt, "Multi-antenna spectrum sensing for cognitive radio under Rayleigh channel," in Proceedings of 2012 IEEE Symposium on Computers and Communications (ISCC), Cappadocia, Turkey, 2012, pp. 780-784.
  20. H. Wang, G. Noh, D. Kim, S. Kim, and D. Hong, "Advanced sensing techniques of energy detection in cognitive radios," Journal of Communications and Networks, vol. 12, no. 1, pp. 19-29, 2010. https://doi.org/10.1109/JCN.2010.6388431
  21. V. R. S. Banjade, N. Rajatheva, and C. Tellambura, "Performance analysis of energy detection with multiple correlated antenna cognitive radio in Nakagami-m fading," IEEE Communications Letters, vol. 16, no. 4, pp. 502-505, 2012. https://doi.org/10.1109/LCOMM.2012.020212.112541
  22. Y. Ou and Y. M. Wang, "Multiple antennas spectrum sensing for cognitive radio networks," Journal of Networks, vol. 8, no. 3, pp. 665-671, 2013.
  23. R. Manna, R. H. Louie, Y. Li, and B. Vucetic, "Cooperative spectrum sharing in cognitive radio networks with multiple antennas," IEEE Transactions on Signal Processing, vol. 59, no. 11, pp. 5509-5522, 2011. https://doi.org/10.1109/TSP.2011.2163068
  24. J. Manco-Vasquez, M. Lazaro-Gredilla, D. Ramirez, J. Via, and I. Santamaria, "A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks," Signal Processing, vol. 96B, pp. 228-240, 2014.
  25. W. Zhao, T. Fang, and Y. Jiang, "Data fusion using improved Dempster-Shafer evidence theory for vehicle detection," in Proceedings of 4th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD2007), Haikou, China, 2007, pp. 487-491.

피인용 문헌

  1. Fair Dynamic Spectrum Allocation Using Modified Game Theory for Resource-Constrained Cognitive Wireless Sensor Networks vol.9, pp.5, 2017, https://doi.org/10.3390/sym9050073