8th International Conference on Cognitive Radio Oriented Wireless Networks

Research Article

Reducing Computational Complexity of Eigenvalue Based Spectrum Sensing for Cognitive Radio

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  • @INPROCEEDINGS{10.4108/icst.crowncom.2013.252014,
        author={Sener Dikmese and Jiunn Wong and Ahmet Gokceoglu and Elena Guzzon and Mikko Valkama and Markku Renfors},
        title={Reducing Computational Complexity of Eigenvalue  Based Spectrum Sensing for Cognitive Radio},
        proceedings={8th International Conference on Cognitive Radio Oriented Wireless Networks},
        publisher={ICST},
        proceedings_a={CROWNCOM},
        year={2013},
        month={11},
        keywords={energy detector based spectrum sensing eigenvalue  based  spectrum  sensing awgn frequency  selective  channel and noise uncertainty},
        doi={10.4108/icst.crowncom.2013.252014}
    }
    
  • Sener Dikmese
    Jiunn Wong
    Ahmet Gokceoglu
    Elena Guzzon
    Mikko Valkama
    Markku Renfors
    Year: 2013
    Reducing Computational Complexity of Eigenvalue Based Spectrum Sensing for Cognitive Radio
    CROWNCOM
    IEEE
    DOI: 10.4108/icst.crowncom.2013.252014
Sener Dikmese1, Jiunn Wong2, Ahmet Gokceoglu1,*, Elena Guzzon3, Mikko Valkama1, Markku Renfors1
  • 1: Tampere University of Technology
  • 2: Jacobs University
  • 3: University of Roma TRE
*Contact email: ahmet.gokceoglu@tut.fi

Abstract

Spectrum sensing of primary users under very low signal-to-noise ratio (SNR) and noise uncertainty is crucial for cognitive radio (CR) systems. To overcome the drawbacks of weak signal and noise uncertainty, eigenvalue-based spectrum sensing methods have been proposed for advanced CRs. However, one pressing disadvantage of eigenvalue-based spectrum sensing algorithms is their high computational complexity, which is due to the calculation of the covariance matrix and its eigenvalues. In this study, power, inverse power and fast Cholesky methods for eigenvalue computation are investigated as potential methods for reducing the computational complexity.