• icon+90(535) 849 84 68
  • iconnwsa.akademi@hotmail.com
  • icon Fırat Akademi Samsun-Türkiye

> Detaylı Arama

Article Details

Article Details

  • Article Code : FIRAT-AKADEMI-8413-3932
  • Article Type : Araştırma Makalesi
  • Publication Number : 1A0477
  • Page Number : 1-8
  • Doi : 10.12739/NWSA.2022.17.1.1A0477
  • Abstract Reading : 1823
  • Download : 425
  • Atıf Sayısı : 0
  • Share :

  • PDF Download

Issue Details

  • Year : 2022
  • Volume : 17
  • Issue : 1
  • Number of Articles Published : 1
  • Published Date : 1.01.2022

Cover Download Context Page Download
Dergi Kapağı
Engineering Sciences

Serial Number : 1A
ISSN No. : 1308-7231
Release Interval (in a Year) : 4 Issues

Fatih Yavuz ILGIN1

Keywords

SPECTRUM SENSING ALGORITHM BASED ON SHAPIRO WILK TEST

Fatih Yavuz ILGIN1

In this study, spectrum algorithm based on Shapiro Wilk test is discussed and a spectrum detection method is proposed for Cognitive Radios with Shapiro Wilk test. In this method, it is tested whether the energy of the received signal has a Gauss distribution. Simulation studies were carried out with different parameters to evaluate the performance of the proposed detection method. It has been compared with the Energy detector in simulation studies. Under some conditions, the proposed method was found to be more successful than ED.

Keywords
Cognitive Radio, Normality Test, Shapiro Wilk Test, Spectrum Sensing, Spectrum Efficiency ,

Details
   

Authors

Fatih Yavuz ILGIN (1) (Corresponding Author)
Erzincan Binali Yildirim University
fyilgin@erzincan.edu.tr | 0000-0002-7449-4811

Supporting Institution

 :

Project Number

 :

Thanks

 :
References
1. Wetzels, R. and Wagenmakers, E.J., (2012). A default Bayesian hypothesis test for correlations and partial correlations. Psychonomic Bulletin & Review, 19(6):1057-1064.

2. Wetzels, R., Grasman, R.P., and Wagenmakers, E.J., (2012). A default Bayesian hypothesis test for ANOVA designs. The American Statistician, 66(2):104-111.

3. Ye, T. and Liu, B., (2021). Uncertain hypothesis test with application to uncertain regression analysis. Fuzzy Optimization and Decision Making, 1-18.

4. Guggenberger, P., (2010). The impact of a Hausman pretest on the size of a hypothesis test: The panel data case. Journal of Econometrics, 156(2):337-343.

5. Kostopoulou, O., Devereaux-Walsh, C., and Delaney, B.C., (2009). Missing celiac disease in family medicine: the importance of hypothesis generation. Medical Decision Making, 29(3):282-290.

6. Goodman, N.A., Venkata, P.R., and Neifeld, M.A., (2007). Adaptive waveform design and sequential hypothesis testing for target recognition with active sensors. IEEE Journal of Selected Topics in Signal Processing, 1(1):105-113.

7. Sheikhi, A., Zamani, A., and Norouzi, Y., (2006, October). Model-based adaptive target detection in clutter using MIMO radar. In 2006 CIE International Conference on Radar (pp:1-4). IEEE.

8. Han, S., Yan, L., Zhang, Y., Addabbo, P., Hao, C., and Orlando, D., (2020). Adaptive radar detection and classification algorithms for multiple coherent signals. IEEE Transactions on Signal Processing, 69:560-572.

9. Eltrass, A. and Khalil, M., (2018). Automotive radar system for multiple-vehicle detection and tracking in urban environments. IET Intelligent Transport Systems, 12(8):783-792.

10. Sohn, K.J., Li, H., and Himed, B., (2007). Parametric Rao test for multichannel adaptive signal detection. IEEE Transactions on Aerospace and Electronic Systems, 43(3):921-933.

11. Bridge, P.D. and Sawilowsky, S.S., (1999). Increasing physicians’ awareness of the impact of statistics on research outcomes: comparative power of the t-test and Wilcoxon rank-sum test in small samples applied research. Journal of Clinical Epidemiology, 52(3):229-235.

12. Christensen, R., (2005). Testing fisher, neyman, pearson, and bayes. The American Statistician, 59(2):121-126.

13. Kaya, V., Tuncer, S., and Baran, A., (2021). Detection and classification of different weapon types using deep learning. Applied Sciences, 11(16):7535.

14. Ilgin, F.Y., (2020). Energy-based spectrum sensing with copulas for cognitive radios. Bulletin of the Polish Academy of Sciences. Technical Sciences, 68(4).

15. Ma, J., Zhao, G., and Li, Y., (2008). Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 7(11):4502-4507.

16. Atapattu, S., Tellambura, C., and Jiang, H., (2011). Energy detection based cooperative spectrum sensing in cognitive radio Networks. IEEE Transactions on Wireless Communications, 10(4):1232-1241.

17. Kumar, B.A. and Rao, P.T., (2017). MDI-SS: matched filter detection with inverse covariance matrix-based spectrum sensing in cognitive radio. International Journal of Internet Technology and Secured Transactions, 7(4):353-363.

18. Shakir, M.Z., Rao, A., and Alouini, M.S., (2013). Generalized mean detector for collaborative spectrum sensing. IEEE Transactions on Communications, 61(4):1242-1253.

19. Zhu, X., Champagne, B., and Zhu, W.P., (2014). Rao test based cooperative spectrum sensing for cognitive radios in non-Gaussian noise. Signal Processing, 97:183-194.

20. Sedighi, S., Taherpour, A., and Monfared, S.S., (2013). Bayesian generalised likelihood ratio test-based multiple antenna spectrum sensing for cognitive radios. IET Communications, 7(18):2151-2165.

21. Hanusz, Z., Tarasinska, J., and Zielinski, W., (2016). Shapiro-Wilk test with known mean. REVSTAT-Statistical Journal, 14(1):89-100.

22. Guner, B., Frankford, M.T., and Johnson, J.T., (2009). A study of the Shapiro–Wilk test for the detection of pulsed sinusoidal radio frequency interference. IEEE Transactions on Geoscience and Remote Sensing, 47(6):1745-1751.

23. https://www.real-statistics.com/statistics-tables/shapiro-wilk-table/ (Date of access: 15 January 2022).

24. https://www.statskingdom.com/shapiro-wilk-test-calculator.html (Date of access: 15 January 2022).