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Cooperative Spectrum Sensing Using Dual Nonparametric Cumulative Sum Algorithm

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Book cover Complex, Intelligent, and Software Intensive Systems (CISIS 2018)

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Abstract

For the problem of reducing the detection delay of spectrum sensing in cognitive radio (CR) networks, an improved CSS approach based on nonparametric cumulative sum is proposed. In local spectrum sensing, the CR users uses the nonparametric CUSUM to process the energy observation data for the 1-bit results which are transmitted to fusion center. In fusion center, the 1-bit results are fused under the disturb of noise. To reduce the detection delay, fusion center uses the nonparametric CUSUM algorithm to accumulate the decision statistics until the primary signal is found. The simulation shows that the proposed approach has less detection delay relative to the conventional nonparametric CSS approaches while maintaining the false alarm probability at 10%.

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References

  1. Kliks, A., Holland, O., Basaure, A., Matinmikko, M.: Spectrum and license flexibility for 5G networks. Int. IEEE Commun. Mag. 53(6), 42–49 (2015)

    Article  Google Scholar 

  2. Xia, J., Yun, R., Yu, K., Yin, F., Wang, H., Bu, Z.: A coordinated mechanism for multimode user equipment accessing wireless sensor network. Int. J. Grid Util. Comput. 5(1), 1–10 (2014)

    Article  Google Scholar 

  3. Bashar, A.: Graphical modelling approach for monitoring and management of telecommunication networks. Int. J. Space Based Situated Comput. 5(2), 65–75 (2015)

    Article  MathSciNet  Google Scholar 

  4. Shen, B., Gao, K., Huang, X., Chen, Q.: User set cardinality estimation based cooperative spectrum sensing in cognitive radio. Int. J. Chongqing Univ. Posts Telecommun. (Natural Science Edition) 28(5), 672–679 (2016)

    Google Scholar 

  5. Zhang, Y., Peng, H., Gong, K.: Multi scale power spectral density subband gradient-based spectrum sensing algorithm and performance analysis. Int. J. Commun. 28(5), 190–198 (2016)

    Google Scholar 

  6. Sun, H., Nallanathan, A., Cui, S., Wang, C.X.: Cooperative wideband spectrum sensing over fading channels. Int. IEEE Trans. Veh. Technol. 65(3), 1382–1394 (2016)

    Article  Google Scholar 

  7. Haykin, S.: Cognitive radio: brain-empowered wireless communications. Int. IEEE J. Select. Areas Commun. 23(2), 201–220 (2005)

    Article  Google Scholar 

  8. Yang, H., Tang, L., Wang, C.: Ability research of cognitive wireless network spectrum sensing that based on distributed non-negative matrix factorization. Int. Sci. Technol. Eng. 16(24), 214–218 (2016)

    Google Scholar 

  9. Bouraoui, R., Besbes, H.: Cooperative spectrum sensing for cognitive radio networks: fusion rules performance analysis. In: 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, pp. 493–498. IEEE (2016)

    Google Scholar 

  10. Page, E.: Continuous inspection schemes. Int. Biometrika 41, 100–115 (1954)

    Article  MathSciNet  Google Scholar 

  11. Li, H., Li, C., Dai, H.: Quickest spectrum sensing in cognitive radio. In: Conference on Information Science and Systems, Princeton, pp. 203–208. IEEE (2008)

    Google Scholar 

  12. Banerjee, T., Kavitha, V., Sharma, V.: Energy efficient change detection over a MAC using physical layer fusion. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, pp. 2501–2504. IEEE (2008)

    Google Scholar 

  13. Urkowitz, H.: Energy detection of unknown deterministic signals. Int. Proc. IEEE 7(11), 523–531 (1967)

    Article  Google Scholar 

  14. Tartakovsky, A.: Asymptotic performance of a multichart CUSUM test under false alarm probability constraint. In: The 44th IEEE Conference on Decision and Control and the European Control Conference, Seville, pp. 320–325. IEEE (2005)

    Google Scholar 

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Correspondence to Jingcheng Miao , Hang Yang , Xiaoou Song or Zili Wang .

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Miao, J., Yang, H., Song, X., Wang, Z. (2019). Cooperative Spectrum Sensing Using Dual Nonparametric Cumulative Sum Algorithm. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_36

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