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Robust Spectrum Sensing for Cognitive Radio with Impulsive Noise

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Communications and Networking (ChinaCom 2018)

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Abstract

Spectrum sensing plays an important role in cognitive radio. In this paper, a robust spectrum sensing method via empirical characteristic function based on goodness-of-fit testing is proposed, named as ECF detector. The test statistic is derived from the empirical characteristic function of the observed samples, thus the secondary users do not require any prior knowledge of the primary signal and the noise distribution. Extensive simulations are performed and compared with the existing spectrum sensing methods, such as energy detector, eigenvalue-based detector, AD detector and KS detector. The results show that, the proposed ECF detector can offer superior detection performance under both the Gaussian noise and the impulsive noise environments.

This research was supported by the Natural Science Foundation of China under Grant No.61762011, Guangxi Natural Science Foundation under Grant No.2016GXNSFAA380091, Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program.

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References

  1. Urkowitz, H.: Energy detection of unknown deterministic signals. Proc. IEEE 55(4), 523–531 (1967)

    Article  Google Scholar 

  2. Digham, F., Alouini, M.-S., Simon, M.K.: On the energy detectionof unknown signals over fading channels. IEEE Trans. Commun. 55(1), 21–24 (2007)

    Article  Google Scholar 

  3. Zeng, Y., Liang, Y.-C.: Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Trans. Commun. 57(6), 1784–1793 (2009)

    Article  Google Scholar 

  4. Wang, Pu, Fang, Jun, Li, Hongbin: Multiantenna-assisted spectrum sensing for cognitive radio. IEEE Trans. Veh. Technol. 59(4), 1791–1800 (2010)

    Article  Google Scholar 

  5. Abbas, T., Masoumeh, N.-K.: Multiple antenna spectrum sensing in cognitive radios. IEEE Trans. Wirel. Commun. 9(2), 814–823 (2010)

    Article  Google Scholar 

  6. Althaf, C.I.M., Prema, S.: Covariance and eigenvalue based spectrum sensing using USRP in real environment. In: 10th International Conference on Communications Systems and Networks, pp. 414–417. Bangalore, India (2018)

    Google Scholar 

  7. Skomal, E.N.: The range and frequency dependence of VHF-UHF man-made radio noise in and above metropolitan areas. IEEE Trans. Veh. Technol. 19(2), 213–221 (1970)

    Article  Google Scholar 

  8. Blackard, K.L., Rappaport, T.S., Bostian, C.W.: Measurements and models of radio frequency impulsive noise for indoor wireless communication. IEEE J. Sel. Areas Commun. 11(7), 991–1001 (1993)

    Article  Google Scholar 

  9. Kuran, M.S., Tugcu, T.: A survey on emerging broadband wireless access technologies. Comput. Netw. 51(1), 3013–3046 (2007)

    Article  Google Scholar 

  10. Wang, H., Yang, E., Zhao, Z., Zhang, W.: Spectrum sensing in cognitive radio using goodness of fit testing. IEEE Trans. Wirel. Commun. 8(11), 5427–5430 (2009)

    Article  Google Scholar 

  11. Sheers, B., Teguig, D., Le Nir, V.: Modified Anderson-Darling detector for spectrum sensing. Electron. Lett. 15(25), 2156–2158 (2015)

    Article  Google Scholar 

  12. Zhang, G., Wang, X., Liang, Y.-C., Liu, J.: Fast and robust spectrum sensing via Kolmogorov-Smirnov test. IEEE Trans. Commun. 58(12), 3410–3416 (2010)

    Article  Google Scholar 

  13. Arshad, K., Moessner, K.: Robust spectrum sensing based on statistical tests. IET Commun. 7(9), 808–817 (2013)

    Article  Google Scholar 

  14. Lekomtcev, D., Marsalek, R.: Spectrum sensing under transmitter front-end constraints. In: 23rd International Conference on Systems, Signals and Image Processing, pp. 1–4. Bratislava, Slovakia (2016)

    Google Scholar 

  15. Shao, M., Nikias, C.: Signal processing with fractional lower order moments: stable processes and their applications. Proc. IEEE 81(7), 986–1010 (1993)

    Article  Google Scholar 

  16. Nikias, C., Shao, M.: Sibgnal Processing with Alpha-stable Distributions and Applications. Wiley, New York (1995)

    Google Scholar 

  17. Rajan, A., Tepedelenlioglu, C.: Diversity combining over Rayleigh fading channels with symmetric alpha-stable noise. IEEE Trans. Wirel. Commun. 9(9), 2968–2976 (2010)

    Article  Google Scholar 

  18. Brcich, R.F., Iskander, D.R., Zoubir, A.M.: The stability test for symmetric alpha-stable distributions. IEEE Trans. Signal Process. 53(3), 977–986 (2005)

    Article  MathSciNet  Google Scholar 

  19. Andersen, H.H, Hoejbjerre, M., Soerensen, D., et al.: Linear and Graphical Models: For the Multivariate Complex Normal Distribution. Springer, New York (1995)

    Book  Google Scholar 

  20. Fan, Y.: Goodness-of-fit tests for a multivariate distribution by the empirical characteristic function. J. Multivar. Anal. 62, 36–63 (1997)

    Article  MathSciNet  Google Scholar 

  21. Press, S.J.: Estimation in univariate and multivariate stable distributions. J. Am. Stat. Assoc. 67(340), 842–846 (1972)

    Article  MathSciNet  Google Scholar 

  22. Koutrouvelis, I.A.: Regression-type estimation of the parameters of stable laws. J. Am. Stat. Assoc. 75(372), 918–928 (1980)

    Article  MathSciNet  Google Scholar 

  23. Feuerverger, A., Mcdunnough, P.: On the efficiency of empirical characteristic function procedures. J. R. Stat. Soc. 43(1), 20–27 (1981)

    MathSciNet  MATH  Google Scholar 

  24. Tsihrintzis, G., Nikias, C.: Performance of optimum and suboptimum receivers in the presence of impulsive noise modeled as an alpha-stable process. IEEE Trans. Commun. 43(4), 904–914 (1995)

    Article  Google Scholar 

  25. Koutrouvelis, I.A.: A goodness-of-fit test of simple hypotheses based on the empirical characteristic function. Biometrika 67(1), 238–240 (1980)

    Article  MathSciNet  Google Scholar 

  26. Ilow, J., Hatzinakos, D.: Applications of the empirical characteristic function to estimation and detection problems. Elsevier Signal Process. 65(2), 199–219 (1998)

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, China

    Liping Luo

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  1. Liping Luo
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Correspondence to Liping Luo .

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Editors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Xingang Liu

  2. University of Electronic Science and Technology of China, Chengdu, China

    Dai Cheng

  3. University of Electronic Science and Technology of China, Chengdu, China

    Lai Jinfeng

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Luo, L. (2019). Robust Spectrum Sensing for Cognitive Radio with Impulsive Noise. In: Liu, X., Cheng, D., Jinfeng, L. (eds) Communications and Networking. ChinaCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-030-06161-6_42

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  • DOI: https://doi.org/10.1007/978-3-030-06161-6_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-06160-9

  • Online ISBN: 978-3-030-06161-6

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