KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Adaptive Algorithms for Bayesian Spectrum Sensing Based on Markov Model

  • Peng, Shengliang (Xiamen Mobile and Multimedia Communications Key Laboratory, Huaqiao University) ;
  • Gao, Renyang (Xiamen Mobile and Multimedia Communications Key Laboratory, Huaqiao University) ;
  • Zheng, Weibin (Xiamen Mobile and Multimedia Communications Key Laboratory, Huaqiao University) ;
  • Lei, Kejun (College of Information Science and Engineering, Jishou University)
  • Received : 2017.08.04
  • Accepted : 2018.03.20
  • Published : 2018.07.31
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Abstract

Spectrum sensing (SS) is one of the fundamental tasks for cognitive radio. In SS, decisions can be made via comparing the test statistics with a threshold. Conventional adaptive algorithms for SS usually adjust their thresholds according to the radio environment. This paper concentrates on the issue of adaptive SS whose threshold is adjusted based on the Markovian behavior of primary user (PU). Moreover, Bayesian cost is adopted as the performance metric to achieve a trade-off between false alarm and missed detection probabilities. Two novel adaptive algorithms, including Markov Bayesian energy detection (MBED) algorithm and IMBED (improved MBED) algorithm, are proposed. Both algorithms model the behavior of PU as a two-state Markov process, with which their thresholds are adaptively adjusted according to the detection results at previous slots. Compared with the existing Bayesian energy detection (BED) algorithm, MBED algorithm can achieve lower Bayesian cost, especially in high signal-to-noise ratio (SNR) regime. Furthermore, it has the advantage of low computational complexity. IMBED algorithm is proposed to alleviate the side effects of detection errors at previous slots. It can reduce Bayesian cost more significantly and in a wider SNR region. Simulation results are provided to illustrate the effectiveness and efficiencies of both algorithms.

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References

  1. S. Filin, T. Baykas, H. Harada, F. Kojima, and H. Yano, "IEEE Standard 802.19.1 for TV white space coexistence," IEEE Communications Magazine, vol. 54, no. 3, pp. 22-26, 2016.
  2. C.-S. Sum et al., "Cognitive communication in TV white spaces: An Overview of Regulations, Standards, and Technology," IEEE Commun. Mag., vol. 51, no. 7, pp. 138-145, 2013. https://doi.org/10.1109/MCOM.2013.6553690
  3. J. Mitola and G. Q. Maguire, "Cognitive radio: making software radios more personal," IEEE personal communications, vol. 6, no. 4, pp. 13-18, 1999. https://doi.org/10.1109/98.788210
  4. S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE journal on selected areas in communications, vol. 23, no. 2, pp. 201-220, 2005. https://doi.org/10.1109/JSAC.2004.839380
  5. S. Haykin, D. J. Thomson, and J. H. Reed, "Spectrum sensing for cognitive radio," Proceedings of the IEEE, vol. 97, no. 5, pp. 849-877, 2009. https://doi.org/10.1109/JPROC.2009.2015711
  6. P. D. Sutton, K. E. Nolan, and L. E. Doyle, "Cyclostationary signatures in practical cognitive radio applications," IEEE Journal on selected areas in Communications, vol. 26, no. 1, pp. 13-24, 2008. https://doi.org/10.1109/JSAC.2008.080103
  7. V. I. Kostylev, "Energy detection of a signal with random amplitude," in Proc. of IEEE International Conference on Communications (ICC 2002), pp. 1606-1610, 2002.
  8. H. Urkowitz, "Energy detection of unknown deterministic signals," Proceedings of the IEEE, vol. 55, no. 4, pp. 523-531, 1967. https://doi.org/10.1109/PROC.1967.5573
  9. R. Tandra and A. Sahai, "SNR Walls for Signal Detection," IEEE J. Sel. Topics Signal Process., vol. 2, no. 1, pp. 4-17, 2008. https://doi.org/10.1109/JSTSP.2007.914879
  10. Y. Zou, Y.-D. Yao, and B. Zheng, "Cooperative relay techniques for cognitive radio systems: Spectrum sensing and secondary user transmissions," IEEE Commun. Mag., vol. 50, no. 4, pp. 98-103, 2012. https://doi.org/10.1109/MCOM.2012.6178840
  11. W. Choi, M.-G. Song, J. Ahn, and G.-H. Im, "Soft Combining for Cooperative Spectrum Sensing over Fast-Fading Channels," IEEE Commun. Lett., vol. 18, no. 2, pp. 193-196, 2014. https://doi.org/10.1109/LCOMM.2013.112713.131767
  12. W. Han, J. Li, Z. Li, J. Si, and Y. Zhang, "Efficient Soft Decision Fusion Rule in Cooperative Spectrum Sensing," IEEE Trans. Signal Process., vol. 61, no. 8, pp. 1931-1943, 2013. https://doi.org/10.1109/TSP.2013.2245659
  13. G. Verma and O. Sahu, "Intelligent selection of threshold in cognitive radio system," Telecommunication Systems, pp. 1-10, 2016.
  14. M. Lopez-Benitez and F. Casadevall, "Improved energy detection spectrum sensing for cognitive radio," IET communications, vol. 6, no. 8, pp. 785-796, 2012. https://doi.org/10.1049/iet-com.2010.0571
  15. M. Kist, L. R. Faganello, L. Bondan, M. A. Marotta, L. Z. Granville, J. Rochol, and C. B. Both, "Adaptive threshold architecture for spectrum sensing in public safety radio channels," in Proc. of 2015 IEEE Wireless Communications and Networking Conference (WCNC 2015), pp. 287-292, 2015.
  16. H.-H. Choi, K. Jang, and Y. Cheong, "Adaptive sensing threshold control based on transmission power in cognitive radio systems," in Proc. of 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008), pp.1-6, 2008.
  17. Z. Bao, B. Wu, P.-H. Ho, and X. Ling, "Adaptive threshold control for energy detection based spectrum sensing in cognitive radio networks," in Proc. of IEEE Global Telecommunications Conference (GLOBECOM 2011), pp. 1-5, 2011.
  18. X. Ling, B. Wu, H. Wen, P.-H. Ho, Z. Bao, and L. Pan, "Adaptive threshold control for energy detection based spectrum sensing in cognitive radios," IEEE Wireless Communications Letters, vol. 1, no. 5, pp. 448-451, 2012. https://doi.org/10.1109/WCL.2012.062512.120299
  19. S. Zhang and Z. Bao, "An adaptive spectrum sensing algorithm under noise uncertainty," in Proc. of 2011 IEEE International Conference on Communications (ICC 2011), pp. 1-5, 2011.
  20. N. Wang, Y. Gao, and X. Zhang, "Adaptive spectrum sensing algorithm under different primary user utilizations," IEEE Communications Letters, vol. 17, no. 9, pp. 1838-1841, 2013. https://doi.org/10.1109/LCOMM.2013.081313.131468
  21. S. Peng, X. Yang, S. Shu, and X. Cao, "Exploitation of temporal persistence for accuracy improvement in primary user detection," IET communications, vol. 4, no. 15, pp. 1855-1864, 2010. https://doi.org/10.1049/iet-com.2009.0787
  22. Q. Zhao, L. Tong, and A. Swami, "Decentralized cognitive mac for dynamic spectrum access," in Proc. of the First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2005), pp. 224-232, 2005.
  23. Q. Zhao, L. Tong, A. Swami, and Y. Chen, "Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework," IEEE Journal on selected areas in communications, vol. 25, no. 3, pp.589-600, 2007. https://doi.org/10.1109/JSAC.2007.070409
  24. S. Geirhofer, L. Tong, and B. M. Sadler, "A measurement-based model for dynamic spectrum access in wlan channels," in Proc. of 2006 IEEE Military Communications conference (MILCOM 2006), pp. 1-7, 2006.
  25. S. Geirhofer, L. Tong, and B. M. Sadler, "Dynamic spectrum access in WLAN channels: Empirical model and its stochastic analysis," in Proc. of the first international workshop on Technology and policy for accessing spectrum, p. 14, 2006.
  26. Q. Zhao and B. Krishnamachari, "Structure and optimality of myopic sensing for opportunistic spectrum access," in Proc. of 2007 IEEE International Conference on Communications (ICC 2007), pp. 6476-6481, 2007.
  27. J. Ma, G. Zhao, and Y. Li, "Soft combination and detection for cooperative spectrum sensing in cognitive radio networks," IEEE Transactions on Wireless Communications, vol. 7, no. 11, pp. 4502-4507, 2008. https://doi.org/10.1109/T-WC.2008.070941
  28. L. Rugini, P. Banelli, and G. Leus, "Small sample size performance of the energy detector," IEEE Communications Letters, vol. 17, no. 9, pp. 1814-1817, 2013. https://doi.org/10.1109/LCOMM.2013.080813.131399
  29. P. R. Nair, A. P. Vinod, and A. K. Krishna, "An adaptive threshold based energy detector for spectrum sensing in cognitive radios at low SNR," in Proc. of IEEE International Conference on Communication Systems, pp. 574-578, 2010.
  30. S.-C. Hong, M. H. Vu, and V. Tarokh, "Cognitive sensing based on side information," in Proc. of 2008 IEEE Sarnoff Symposium, pp. 1-6, 2008.
  31. Q. Zhao and B. M. Sadler, "A survey of dynamic spectrum access," IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 79-89, 2007. https://doi.org/10.1109/MSP.2007.361604