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Original scientific paper
https://doi.org/10.17559/TV-20161217120341

Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue

Cebrail Çiflikli ; Erciyes University, Vocational High School, Electronic and Automation Department, Kayseri, Turkey
Fatih Yavuz Ilgin   ORCID icon orcid.org/0000-0002-7449-4811 ; Erzincan University, Vocational High School, Electronic and Automation Department, Fatih Mahallesi, 726. Sk., 24100 Merkez/Erzincan, Turkey

Fulltext: english, pdf (542 KB) pages 100-106 downloads: 782* cite
APA 6th Edition
Çiflikli, C. & Ilgin, F.Y. (2018). Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue. Tehnički vjesnik, 25 (1), 100-106. https://doi.org/10.17559/TV-20161217120341
MLA 8th Edition
Çiflikli, Cebrail and Fatih Yavuz Ilgin. "Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue." Tehnički vjesnik, vol. 25, no. 1, 2018, pp. 100-106. https://doi.org/10.17559/TV-20161217120341. Accessed 18 Jun. 2021.
Chicago 17th Edition
Çiflikli, Cebrail and Fatih Yavuz Ilgin. "Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue." Tehnički vjesnik 25, no. 1 (2018): 100-106. https://doi.org/10.17559/TV-20161217120341
Harvard
Çiflikli, C., and Ilgin, F.Y. (2018). 'Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue', Tehnički vjesnik, 25(1), pp. 100-106. https://doi.org/10.17559/TV-20161217120341
Vancouver
Çiflikli C, Ilgin FY. Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue. Tehnički vjesnik [Internet]. 2018 [cited 2021 June 18];25(1):100-106. https://doi.org/10.17559/TV-20161217120341
IEEE
C. Çiflikli and F.Y. Ilgin, "Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue", Tehnički vjesnik, vol.25, no. 1, pp. 100-106, 2018. [Online]. https://doi.org/10.17559/TV-20161217120341

Abstracts
The eigenvalue based detection is a low-cost spectrum sensing method that detects the presence of primary user signal at a desired frequency. In this study, the largest eigenvalue distribution used in eigenvalue based detection methods is expressed using a new centering and scaling coefficients adjustment. Thus, the detection probability (Pd) and false detection probability (Pfa) equations for the maximum-minimum eigenvalue (MME), maximum eigenvalue to trace (MET) and maximum eigenvalue-geometric mean (ME-GM) have been obtained again. Weibull fading channels are the best model for wireless communication. For this reason, the studies were simulated in Weibull fading channels and analysed in detail with receiver operating characteristic curves (ROC). The results were compared with traditional methods and found to be more accurate.

Keywords
blind spectrum sensing; eigenvalue based spectrum sensing; Tracy-Widom distribution; Weibull fading

Hrčak ID: 193601

URI
https://hrcak.srce.hr/193601

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