Novel Threshold Formulation for Energy Detection Method to Efficient Spectrum Sensing in Cognitive Radio

Kale, Rohini S.; Wadhai, Vijay M.; Helonde, J. B.

doi:10.1007/978-981-10-6614-6_3

Advances in Intelligent Systems and Computing

2017

DOI: 10.1007/978-981-10-6614-6_3

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Novel Threshold Formulation for Energy Detection Method to Efficient Spectrum Sensing in Cognitive Radio

Rohini S. Kale¹,

Vijay M. Wadhai

J. B. Helonde³

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2021

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Eigenvalue-Based Spectrum Sensing with Small Samples Using Circulant Matrix

Chen

et al. 2021

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In cognitive radio (CR) networks, eigenvalue-based detectors (EBDs) have attracted much attention due to their good performance of detecting secondary users (SUs). In order to further improve the detection performance of EBDs with short samples, we propose two new detectors: average circulant matrix-based Roy’s largest root test (ACM-RLRT) and average circulant matrix-based generalized likelihood ratio test (ACM-GLRT). In the proposed method, the circulant matrix of samples at each time instant from SUs is calculated, and then, the covariance matrix of the circulant matrix is averaged over a short period of time. The eigenvalues of the achieved average circulant matrix (ACM) are used to build our proposed detectors. Using a circulant matrix can improve the dominant eigenvalue of covariance matrix of signals and also the detection performance of EBDs even with short samples. The probability distribution functions of the detectors undernull hypothesis are analyzed, and the asymptotic expressions for the false-alarm and thresholds of two proposed detectors are derived, respectively. The simulation results verify the effectiveness of the proposed detectors.

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Eigenvalue-Based Spectrum Sensing with Small Samples Using Circulant Matrix

Chen

et al. 2021

Symmetry

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Novel Threshold Formulation for Energy Detection Method to Efficient Spectrum Sensing in Cognitive Radio

Cited by 1 publication

References 5 publications

Eigenvalue-Based Spectrum Sensing with Small Samples Using Circulant Matrix

Eigenvalue-Based Spectrum Sensing with Small Samples Using Circulant Matrix

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