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Energy-based spectrum sensing with copulas for cognitive radios

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Języki publikacji
EN
Abstrakty
EN
In this study, an energy-based spectrum sensing method combined with copula theory is proposed for cognitive radio systems. In the proposed spectrum sensing model, cognitive radio users first make their own local spectrum decision with energy-based spectrum sensing. Then, they forward their decision to the fusion center. In the fusion center, this decision is compared with the threshold value determined by copula theory and global spectrum decision is made. The test statistic at the fusion center were obtained with the Neyman Pearson approach. Thus, the fusion rule was created for the fusion center and necessary simulation studies were performed. According to the results of the simulation studies, the proposed detection method showed better results than the traditional energy based detection method.
Rocznik
Strony
829--834
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
  • Department of Electrical and Electronics Engineering, Faculty of Engineering, Erzincan Binali YILDIRIM University, Erzincan, Turkey
Bibliografia
  • [1] E. Dahlman, S. Parkvall, and J. Skold, 4G: LTE/LTE-Advanced for Mobile Broadband. 2nd ed., Academic Press, 2013.
  • [2] B.B. Pradhan and L.P. Roy, “Ergodic capacity and symbol error rate of distributed massive MIMO systems over Rayleigh-inverse Gaussian fading channels using ZF detectors”, Phys. Commun. 38, 100906 (2020).
  • [3] D. Cabric, “Addressing feasibility of cognitive radios”, IEEE Signal Process. Mag. 25, 6 (2008).
  • [4] S. Razmi and N. Parhizgar, “OFDM for cognitive radio systems: Novel power allocation and bit loading algorithms”, Int. J. Electron. Telecommun. 65 (1), 139‒145 (2019).
  • [5] Z. Chen and Y. Zhang, “Cooperative energy detection algorithm based on background noise and direction finding error”, AEU-Int. J. Electron. Commun. 95, 326–341 (2018).
  • [6] C. Charan and R. Paney, “Eigenvalue based double threshold spectrum sensing under noise uncertainty for cognitive radio”, Optik 127(15), 5968–5975 (2016).
  • [7] P.S Aparna and M. Jayasheela, “Cyclostationary feature detection in cognitive radio using different modulation schemes”, Int. J. Comput. Appl. 47(21), 12‒16 (2012).
  • [8] N.A. Macmillan and C.D. Creelman, “Detection theory: A user’s guide”, 2nd ed., Psychology Press, 2004.
  • [9] J.A. Bazerque and G.B. Giannakis, “Distributed spectrum sensing for cognitive radio networks by exploiting sparsity”, IEEE Trans. Signal Process. 58(3), 1847–1862 (2010).
  • [10] J. Duan and Y. Li, “An optimal spectrum handoff scheme for cognitive radio mobile Ad hoc networks”, Adv. Electr. Comput. Eng. 11(3), 11–16 (2011).
  • [11] A. Mohammadi, S.H. Javadi, D. Ciuonzo, V. Persico, and A. Pescapé, “Distributed detection with fuzzy censoring sensors in the presence of noise uncertainty”, Neurocomputing 351, 196–204 (2019).
  • [12] M. Reichenbach, M. Kasparek, K. Häublein, J.N. Bauer, M. Alawieh, and D. Fey, “Fast heterogeneous computing architectures for smart antennas”, J. Syst. Architect. 76, 76–88 (2017).
  • [13] C. Çiflikli and F. Y. Ilgin, “Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue”, Teh. Vjesn. 25(6), 100–106 (2018).
  • [14] K. Bień-Barkowska, “A Bivariate Copula-based Model for a Mixed Binary-Continuous Distribution: A Time Series Approach”, Cent. Eur. J. Econ. Model. Econom. 4(2), 117–142 (2012).
  • [15] Y. He, T. Ratnarajah, E.H.G. Yousif, J. Xue, and M. Sellathurai, “Performance analysis of multi-antenna GLRT-based spectrum sensing for cognitive radio”, Signal Process. 120, 580–593 (2016).
  • [16] A. Das, “Copula-based Stochastic Frontier Model with Autocorrelated Inefficiency”, Central European Journal of Economic Modelling and Econometrics 7(2), 111–126 (2015).
  • [17] L.R. Arends et al., “Bivariate random effects meta-analysis of ROC curves”, Med. Decis. Mak. 28(5), 621–638 (2008).
  • [18] K. Lewenstein, M. Jamroży, and T. Leyko, “The use of recurrence plots and beat recordings in chronic heart failure detection”, Bull. Pol. Ac.: Tech. 64(2), 339–345 (2016).
  • [19] J. Hahm and A. Beskok, “Numerical simulation of multiple species detection using hydrodynamic/electrokinetic focusing”, Bull. Pol. Ac.: Tech. 53(4), 325–334 (2005).
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-06913579-6432-4740-980b-0b8e04298166
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