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Secondary User Selection Scheme Using Adaptive Genetic Algorithms for Cooperative Spectrum Sensing Under Correlated Shadowing

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Abstract

Cooperative spectrum sensing has been shown to be an effective approach to improve the detection performance by exploiting the spatial diversity among multiple secondary users (or unlicensed users). However, due to correlated shadowing and cooperation overhead in practical cognitive radio networks, it is desired to select an appropriate set of secondary users which have little correlation with each other to participate in cooperation so as to achieve the effective tradeoff between detection performance and cooperation overhead. In this paper, we first study the hypothesis testing model and detection performance of cooperative spectrum sensing under the correlated log-normal shadowing scenario. Afterwards, based on whether the false-alarm and missed-detection probabilities are constrained, three optimization problems are formulated to find the optimal set of secondary users participating in cooperation, which take into account the tradeoff between detection performance and cooperation overhead. Then the solutions using adaptive genetic algorithms are presented for the optimization problems. Finally, simulation experiments demonstrate that our proposed schemes are very effective.

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References

  1. Federal Communications Commission. (2002). Spectrum policy task force report, FCC 02-155. November 2002.

  2. Broderson R. W., Wolisz A., Cabric D., Mishra S. M., Willkomm D. (2004) CORVUS: A cognitive radio approach for usage of virtual unlicensed spectrum. University of California Berkeley Whitepaper, Berkeley, CA

    Google Scholar 

  3. Erpek, T., Lofquist, M., & Patton, K. (2007). Spectrum occupancy measurements: Loring Commerce Centre, Limestone, ME, Sep. 18–20, 2007. Shared Spectrum Co. report. Shared Spectrum Co., Vienna, VA.

  4. Mitola J., Maguire G. Q. (1999) Cognitive radios: Making software radios more personal. IEEE Personal Communications 6(4): 13–18

    Article  Google Scholar 

  5. Haykin S. (2005) Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications 23(2): 201–220

    Article  Google Scholar 

  6. Federal Communications Commission. (2004). Notice of proposed rulemaking, in the matter of unlicensed operation in the TV broadcast bands (ET Docket No. 04-186) and additional spectrum for unlicensed devices below 900 MHz and in the 3 GHz band (ET Docket No. 02-380), FCC 04-113.

  7. IEEE 802.22, Working group on wireless regional area networks (WRAN): Enabling rural broadband and wireless access using cognitive radio technology in TV whitespaces. Retrieved from http://grouper.ieee.org/groups/802/22/.

  8. Akyildiz I. F., Lee W.-Y., Vuran M. C., Mohanty S. (2006) Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks 50(13): 2127–2159

    Article  MATH  Google Scholar 

  9. Visotsky, E., Kuffner, S., & Peterson, R. (2005). On collaborative detection of TV transmissions in support of dynamic spectrum sharing. In Proceedings of 1st IEEE international symposium on new frontiers in dynamic spectrum access networks, (pp. 338–345). Baltimore, MD.

  10. Mishra, S. M., Sahai, A., & Brodersen, R. W. (2006). Cooperative sensing among cognitive radios. In Proceedings of IEEE international conference on communications, (pp. 1658–1663). Istanbul, Turkey.

  11. Ghasemi A., Sousa E. S. (2007) Asymptotic performance of collaborative spectrum sensing under correlated log-normal shadowing. IEEE Communications Letters 11(1): 34–36

    Article  Google Scholar 

  12. Ghasemi A., Sousa E. S. (2007) Opportunistic spectrum access in fading channels through collaborative sensing. Journal of Communications 2(2): 71–82

    Article  Google Scholar 

  13. Rifa-Pous, H., Blasco, M. J., & Garrigues, C. (2011). Review of robust cooperative spectrum sensing techniques for cognitive radio networks. Wireless Personal Communications (Published online: 7 August 2011).

  14. Jiang, Y., Tian, J., Chen, H., & Hu, H. (2011). Cyclostationarity-based decision reporting scheme for cooperative spectrum sensing. Wireless Personal Communications (Published online: 24 December 2011).

  15. Chen Y. (2008) Optimum number of secondary users in collaborative spectrum sensing considering resources usage efficiency. IEEE Communications Letters 12(12): 877–879

    Article  Google Scholar 

  16. Xie S., Liu Y., Zhang Y., Yu R. (2010) A parallel cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Vehicular Technology 59(8): 4079–4092

    Article  Google Scholar 

  17. Matsui, M., Shiba, H., Akabane, K., & Uehara, K. (2007). A novel cooperative sensing technique for cognitive radio. In Proceedings of 18th annual ieee international symposium on personal, indoor and mobile radio communications, (pp. 1–5). Athens.

  18. Pratas, N., Marchetti, N., Prasad, N. R., Rodrigues, A., & Prasad, R., (2012). Adaptive counting rule for cooperative spectrum sensing under correlated environments. Wireless Personal Communications (Published online: 7 February 2012).

  19. Srinivas M., Patnaik L. M. (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on systems, man and cybernetics 24(4): 656–667

    Article  Google Scholar 

  20. Khanbary L. M. O., Vidyarthi D. P. (2009) Reliability-based channel allocation using genetic algorithm in mobile computing. IEEE Transactions on Vehicular Technology 58(8): 4248–4256

    Article  Google Scholar 

  21. Michalewicz Z. (1996) Genetic Algorithm + Data Structures = Evolution Programs. Springer, Berlin, Germany

    Book  Google Scholar 

  22. Lima M. A. C., Araujo A. F. R., Cesar A. C. (2007) Adaptive genetic algorithms for dynamic channel assignment in mobile cellular communication systems. IEEE Transactions on Vehicular Technology 56(5): 2685–2696

    Article  Google Scholar 

  23. Gudmundson M. (1991) Correlation model for shadow fading in mobile radio systems. Electronics Letters 27(23): 2145–2146

    Article  Google Scholar 

  24. Yuen H. P., Kennedy R. S., Lax M. (1975) Optimum testing of multiple hypotheses in quantum detection theory. IEEE Transactions on Information Theory IT-21(2): 125–134

    Article  MathSciNet  Google Scholar 

  25. Benedetto F., Giunta G., Neri A. (2009) A Bayesian business model for video-call billing for end-to-end QoS provision. IEEE Transactions on Vehicular Technology 58(2): 836–842

    Article  Google Scholar 

  26. Patra S. S. M., Roy K., Banerjee S., Vidyarthi D. P. (2006) Improved genetic algorithm for channel allocation with channel borrowing in mobile computing. IEEE Transactions on Mobile Computing 5(7): 884–892

    Article  Google Scholar 

  27. Sharma N., Anupama K. R. (2011) A novel genetic algorithm for adaptive resource allocation in MIMO-OFDM systems with proportional rate constraint. Wireless Personal Communications 61(1): 113–128

    Article  Google Scholar 

  28. Liao S. H., Chiu C. C., Ho M. H., Lin C. H. (2012) Optimal relay antenna location in indoor environment using particle swarm optimizer and genetic algorithm. Wireless Personal Communications 62(3): 599–615

    Article  Google Scholar 

  29. Vidyarthi, D. P., Tripathi, A. K., Sarker, B. K., & Rani, K. (2003). Comparative study of two GA-based task allocation models in distributed computing system. In Proceedings of 4th international conference on parallel and distributed computing, applications and technologies, (pp. 458–462). Chengdu, China.

  30. Cheng W., Shi H., Yin X., Li D. (2011) An elitism strategy based genetic algorithm for streaming pattern discovery in wireless sensor networks. IEEE Communications Letters 15(4): 419–421

    Article  Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Integrated Service Networks, Xidian University, Mailbox 87, No. 2 South Taibai Road, Xi’an, 710071, Shaanxi, China

    Defeng Ren, Jianhua Ge & Jing Li

Authors
  1. Defeng Ren
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  2. Jianhua Ge
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  3. Jing Li
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Corresponding author

Correspondence to Defeng Ren.

Additional information

This work was supported by the National Basic Research Program of China (No. 2012CB316100), the Innovation Fund of Aerospace (No. HTCXJJKT-11), the “111” project (No. B08038), and the National Natural Science Foundation of China (No. 61101144).

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Ren, D., Ge, J. & Li, J. Secondary User Selection Scheme Using Adaptive Genetic Algorithms for Cooperative Spectrum Sensing Under Correlated Shadowing. Wireless Pers Commun 71, 769–788 (2013). https://doi.org/10.1007/s11277-012-0843-8

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  • DOI: https://doi.org/10.1007/s11277-012-0843-8

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