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A Novel Spectrum Sensing Method Based on Tri-Stable Stochastic Resonance and Quantum Particle Swarm Optimization

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Abstract

As a core technology of cognitive radio, the spectrum sensing method requires accurate perform and fast sensing in low signal-to-noise ratio (SNR) environment. In this paper, an energy-based spectrum sensing method based on quantum-behaved particle swarm optimization (QPSO) and tri-stable stochastic resonance (TSR) is proposed. In this method, TSR was adopted to achieve greater SNR output than bi-stable stochastic resonance (BSR). To overcome the shortage that TSR can only set fixed parameters by experience, QPSO was adopted to adjust parameters of TSR adaptively when the noise power was fixed. Simulation results of SNR and spectrum show that the convergence, the output SNR in convergence status of QPSO–TSR performs better than QPSO–BSR under low input SNR, while QPSO–TSR has a slight delay due to its optimization searching process. The simulation result of receiver operating characteristic curves show that the proposed scheme has an excellent convergence performance and can improve the detection probability.

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References

  1. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220. doi:10.1109/Jsac.2004.839380.

    Article  Google Scholar 

  2. Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531.

    Article  Google Scholar 

  3. Sutton, P. D., Nolan, K. E., & Doyle, L. E. (2008). Cyclostationary signatures in practical cognitive radio applications. IEEE Journal on Selected Areas in Communications, 26(1), 13–24. doi:10.1109/Jsac.2008.080103.

    Article  Google Scholar 

  4. Zeng, Y., & Liang, Y. C. (2009). Spectrum-sensing algorithms for cognitive radio based on statistical covariances. IEEE Transactions on Vehicular Technology, 58(4), 1804–1815.

    Article  Google Scholar 

  5. Font-Segura, J., & Wang, X. D. (2010). GLRT-based spectrum sensing for cognitive radio with prior information. IEEE Transactions on Communications, 58(7), 2137–2146. doi:10.1109/Tcomm.2010.07.090556.

    Article  Google Scholar 

  6. Emara, M., Ali, H. S., Khamis, S. E. A., & Abd El-Samie, F. E. (2016). Spectrum sensing optimization and performance enhancement of cognitive radio networks. Wireless Personal Communications, 86(2), 925–941. doi:10.1007/s11277-015-2962-5.

    Article  Google Scholar 

  7. Bera, D., Chakrabarti, I., & Pathak, S. S. (2016). Modelling of cooperative spectrum sensing over rayleigh fading without CSI in cognitive radio networks. Wireless Personal Communications, 86(3), 1281–1297. doi:10.1007/s11277-015-2988-8.

    Article  Google Scholar 

  8. He, D., Lin, Y., He, C., & Jiang, L. (2010). A novel spectrum-sensing technique in cognitive radio based on stochastic resonance. IEEE Transactions on Vehicular Technology, 59(4), 1680–1688. doi:10.1109/tvt.2010.2042311.

    Article  Google Scholar 

  9. Benzi, R., Sutera, A., & Vulpiani, A. (1981). The mechanism of stochastic resonance. Journal of Physics A: Mathematical and General, 14(11), L453–L457.

    Article  MathSciNet  Google Scholar 

  10. Chapeau, F. (1997). Input–output gains for signal in noise in stochastic resonance. Physics Letters A, 232(1–2), 41–48.

    Article  Google Scholar 

  11. He, D. (2013). Chaotic stochastic resonance energy detection fusion used in cooperative spectrum sensing. IEEE Transactions on Vehicular Technology, 62(2), 620–627. doi:10.1109/tvt.2012.2224680.

    Article  Google Scholar 

  12. Li, Q., & Li, Z. (2014). A novel sequential spectrum sensing method in cognitive radio using suprathreshold stochastic resonance. IEEE Transactions on Vehicular Technology, 63(4), 1717–1725. doi:10.1109/tvt.2013.2287616.

    Article  Google Scholar 

  13. Lai, Z. H., & Leng, Y. G. (2015). Dynamic response and stochastic resonance of a tri-stable system. Acta Physica Sinica, 64(20), 77–88. doi:10.7498/aps.64.200503.

    Google Scholar 

  14. Leng, Y. G., & Lai, Z. H. (2014). Generalized parameter-adjusted stochastic resonance of Duffng oscillator based on Kramers rate. Acta Physica Sinica, 63(2), 34–42.

    Google Scholar 

  15. Li, Q. (2007). Research of adaptive stochastic resonance based on approximate entropy. Acta Physica Sinica, 56(12), 6803–6808. doi:10.3321/j.issn:1000-3290.2007.12.005.

    Google Scholar 

  16. Couceiro, M., & Ghamisi, P. (2016). Particle swarm optimization. Springer International Publishing.

  17. Omkar, S. N., Khandelwal, R., Ananth, T. V. S., Naik, G. N., & Gopalakrishnan, S. (2009). Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures. Expert Systems with Applications An International Journal, 36(8), 11312–11322.

    Article  Google Scholar 

  18. Li, Y. B., Zhang, B. L., Liu, Z. X., & Zhang, Z. Y. (2014). Adaptive stochastic resonance method based on quantum particle swarm optimization. Acta Physica Sinica, 63(16), 36–43. doi:10.7498/aps.63.160504.

    Google Scholar 

  19. Mcnamara, B., & Wiesenfeld, K. (1989). Theory of stochastic resonance. Physical Review A, 39(9), 4854–4869.

    Article  Google Scholar 

  20. Kolman, D. (2000). Numerical mathematics. Berlin: Springer.

    Google Scholar 

  21. Wang, J., Ren, X., Zhang, S., Zhang, D., Li, H., & Li, S. (2014). Adaptive bistable stochastic resonance aided spectrum sensing. IEEE Transactions on Wireless Communications, 13(7), 4014–4024. doi:10.1109/twc.2014.2317779.

    Article  Google Scholar 

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Acknowledgements

Our work was funded by the National Natural Science Foundation of China (Grant Nos. 61261002, 61461052, 11564044), the Scientific Research Foundation of the Education Department of Yunnan Province (Grant No. 2015Y020), the Spectrum Sensing and borderlands Security Key Laboratory of Universities in Yunnan (Grant No. C6165903), and the Key Program of Natural Science of Yunnan Province (Grant Nos. 2013FA006, 2015FA015). The authors would like to thank the editor and the anonymous reviewers for their constructive comments and helpful suggestions.

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  1. School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, China

    Jin Lu, Ming Huang & Jing-Jing Yang

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  1. Jin Lu
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  2. Ming Huang
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  3. Jing-Jing Yang
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Correspondence to Jin Lu.

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Lu, J., Huang, M. & Yang, JJ. A Novel Spectrum Sensing Method Based on Tri-Stable Stochastic Resonance and Quantum Particle Swarm Optimization. Wireless Pers Commun 95, 2635–2647 (2017). https://doi.org/10.1007/s11277-017-3945-5

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  • DOI: https://doi.org/10.1007/s11277-017-3945-5

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