Performance evaluation of eigenvalue-based detection strategies in a sensor network

Ayeh, Eric; Namuduri, Kamesh; Li, Xinrong

doi:10.1109/icc.2014.6884014

Cited by 2 publications

(2 citation statements)

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“…To this end, plenty of spectrum sensing detectors, including an eigenvalue‐based detector, cyclostationary detector, energy detector (ED) and its modified version have been proposed [3–6]. These detectors are mainly derived for additive white Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

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Unilateral left‐tail Anderson Darling test‐based spectrum sensing with Laplacian noise

Jiang

et al. 2019

IET Communications

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This paper focuses on spectrum sensing under Laplacian noise. To remit the negative effects caused by heavytailed behavior of Laplacian noise, the fractional lower order moments (FLOM) technology is employed to pre-process the received samples before spectrum sensing. Via exploiting the asymmetrical difference between the distribution for the FLOM of received samples in the absence and presence of primary users, we formulate the spectrum sensing problem under Laplacian noise as a unilateral goodness-of-fit (GoF) test problem. Based on this test problem, we propose a new GoF-based detector which is called unilateral left-tail Anderson Darling (ULAD) detector. The analytical expressions for the theoretical performance, in terms of false-alarm and detection probabilities, of the ULAD are derived. Moreover, a closed-form expression for the optimal detection threshold is also derived to minimize the total error rate. Simulation results are provided to validate the theoretical analyses and to demonstrate the superior performance of the proposed detector than others.

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Section: Introductionmentioning

confidence: 99%

“…For instance, compared to the Gaussian approximate, the Laplacian approximate is more accurate for the distribution of the multiple access interference in time‐hopping ultrawide bandwidth communications [9]. The detection efficiency in [3–6] may degrade considerably under Laplacian noise.…”

Section: Introductionmentioning

confidence: 99%

Unilateral left‐tail Anderson Darling test‐based spectrum sensing with Laplacian noise

Jiang

et al. 2019

IET Communications

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Eigenvalue-Based Spectrum Sensing with Small Samples Using Circulant Matrix

Chen

et al. 2021

Symmetry

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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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Performance evaluation of eigenvalue-based detection strategies in a sensor network

Cited by 2 publications

References 12 publications

Unilateral left‐tail Anderson Darling test‐based spectrum sensing with Laplacian noise

Unilateral left‐tail Anderson Darling test‐based spectrum sensing with Laplacian noise

Eigenvalue-Based Spectrum Sensing with Small Samples Using Circulant Matrix

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