Research Article
Reducing Computational Complexity of Eigenvalue Based Spectrum Sensing for Cognitive Radio
@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
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.
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