inis 18(14): e3

Research Article

Eigenvalue-based Detection Techniques Using Finite Dimensional Complex Random Matrix Theory: A Review

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  • @ARTICLE{10.4108/eai.27-6-2018.154834,
        author={Ayse Kortun},
        title={Eigenvalue-based Detection Techniques Using Finite Dimensional Complex Random Matrix Theory: A Review},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={5},
        number={14},
        publisher={EAI},
        journal_a={INIS},
        year={2018},
        month={6},
        keywords={Spectrum sensing, cognitive Radio, eigenvalue-based detection, cooperative spectrum sensing, Wishart matrix.},
        doi={10.4108/eai.27-6-2018.154834}
    }
    
  • Ayse Kortun
    Year: 2018
    Eigenvalue-based Detection Techniques Using Finite Dimensional Complex Random Matrix Theory: A Review
    INIS
    EAI
    DOI: 10.4108/eai.27-6-2018.154834
Ayse Kortun1,*
  • 1: Queen’s University Belfast, Belfast BT7 1NN, UK
*Contact email: akortun@gmail.com

Abstract

Detection of primary users without requiring information of signal is of great importance in spectrum sensing (SS) in Cognitive Radio. Therefore, in recent years, eigenvalue based spectrum sensing algorithms are under the spotlight. Many primary user detection techniques have been proposed for use in Cognitive Radio (CR) and their drawbacks and benefits have been examined. However, among the various methods proposed, only some of them can survive in an antagonistic environment. Therefore, another appealing side of eigenvalue based primary user detection algorithms is the fact that they are totally immune to uncertain noise levels so they are called robust detectors. Random matrix theory (RMT) is a useful tool which is applicable across a large number of fields and in the last decade, a considerable applications in signal detection has emerged. In this paper, the detection performances of the eigenvalue based techniques are analyzed based on the exact threshold formulations using RMT. As opposed to the threshold estimations with large number of samples and antennas presented in the literature, the exact thresholds are used for finite number of samples and antennas. The importance of accurate decision threshold selection in spectrum sensing is emphasized. It is shown that the accurate threshold computations enable the achievement of higher detection performances than asymptotic analyses reported in the literature.