Performance Analysis of Volume-Based Spectrum Sensing for Cognitive Radio

Qian, Cheng; Xiao, Yu-Hang; Zhang, Q. T.

doi:10.1109/twc.2014.2345660

Cited by 45 publications

(22 citation statements)

References 38 publications

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“…We now conduct experiments to compare the performance of the proposed NCC method with the following seven methods: CAV [5], LMPIT [6], HDM [7], VD [8], SFET [9], NC-HDM [12], and NC-LAV [13]. At each run, the elements in H are drawn independent and identically distributed from Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

Covariance-Based Spectrum Sensing for Noncircular Signal in Cognitive Radio Networks With Uncalibrated Multiple Antennas

Chen

Shi

2020

IEEE Wireless Commun. Lett.

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In this letter, the problem of spectrum sensing is addressed for noncircular (NC) signal in cognitive radio networks with uncalibrated multiple antennas. Specifically, by taking both the standard covariance and complementary covariance information of the NC signal into account, a new robust spectrum sensing method called NC covariance (NCC) is proposed, which can fully reap the statistical property of the NC signals. Meanwhile, we derive the asymptotic distribution of the NCC statistic under the signal-absence hypothesis and obtain the theoretical decision threshold of the NCC method. Simulation results demonstrate that the proposed method is capable of outperforming state-ofthe-art methods.

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Section: Simulation Resultsmentioning

confidence: 99%

Covariance-Based Spectrum Sensing for Noncircular Signal in Cognitive Radio Networks With Uncalibrated Multiple Antennas

Chen

Shi

2020

IEEE Wireless Commun. Lett.

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“…O método dos momentosé também aplicado em [13] para caracterizar com a distribuição Beta a estatística denominada de teste de esfericidade. Em [14] demonstra-se que a distribuição Gamma, devidamente parametrizada, pode ser utilizada para avaliar o desempenho da técnica baseada em volume. Há outras técnicas de sensoriamento cujo desenvolvimento analítico em busca das distribuições da estatística de teste se torna tão complexo, ou até mesmo intratável matematicamente, que lança-se mão de técnicas empíricas.…”

Section: B Trabalhos Relacionados E Estrutura Do Artigounclassified

Estimação das Distribuições da Estatística de Teste GRCR sob as Hipóteses H0 e H1

Lemes¹,

Guimarães²

2018

Anais De XXXVI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-O detector GRCR (Gerschgorin radii and centers ratio) foi recentemente proposto para o sensoriamento espectral em sistemas de rádio cognitivo. Seus principais atributos são a baixa complexidade e a robustez frente a potências de sinal e de ruído térmico desiguais e variantes no tempo. Neste artigo apresentam-se os procedimentos e os resultados da análise de aderência da estatística de teste do GRCR a diversas distribuições de probabilidade. Demonstra-se que a distribuição Beta caracteriza adequadamente tal estatística, sob as hipóteses de ausência e presença do sinal sensoriado, permitindo que cálculos teóricos do desempenho e do limiar de decisão do sensoriamento espectral sejam realizados. Palavras-Chave-Rádio cognitivo, GRCR, teste de aderência, sensoriamento espectral. Abstract-The GRCR (Gerschgorin radii and centers ratio) detector was recently proposed to be applied in spectrum sensing for cognitive radio systems. Its main attributes are the low complexity and the robustness against nonuniform and dynamical thermal noise and received signal power. In this paper are reported the procedures and results concerning the goodnessof-fit of the GRCR test statistic to several probability density functions. It is demonstrated that the Beta distribution adequately characterizes such a statistic, under the hypotheses of absence and presence of the sensed signal, allowing theoretical calculations of the performance and the decision threshold of the spectrum sensing.

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“…When multiple antennas are available at the SU, the detectors can overcome the aforementioned limitations, such as the maximum-minimum eigenvalue (MME) [21], covariance absolute value (CAV) [22], covariance Frobenius norm (CFN) [22], arithmetic-to-geometric mean (AGM) [23], scaled largest eigenvalue (SLE) [24], locally most powerful invariant test (LMPIT) [25], Hadamard ratio test [26], volume-based detection (VD) [27], [28] and eigenvalue moment ratio (EMR) [29] methods have been proposed without any prior knowledge and can deliver desirable performance. Whereas, these detection methods focused mainly on time-invariant channels (i.e., channel state remains unchanged during a sensing period), and may not perform well in time-varying channels.…”

Section: Introductionmentioning

confidence: 99%

Generalized Real-Valued Weighted Covariance-Based Detection Methods for Cognitive Radio Networks With Correlated Multiple Antennas

2019

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This paper is concerned with the spectrum sensing problem for cognitive radio networks with correlated receiving multiple antennas in the time-varying fading channel. We first consider the scenario that all the antennas have the same noise variance and present a generalized real-valued weighted-covariancebased detection (GRWCD) method. In particular, we derive the distribution of the GRWCD statistic under the null hypothesis, which allows us to develop the theoretical decision threshold for a given false alarm probability. Besides, we derive the distribution of the GRWCD statistic under the alternative hypothesis, which enables us to provide a mathematical expression for the detection probability as well as the theoretical receiver operating characteristic. Meanwhile, we consider a more general scenario of unequal per-antenna noise variances and present a modified GRWCD method as well as the theoretical expressions of the decision threshold. The simulation results are provided to verify the accuracy of the derived results and show that the proposed two methods are capable of providing performance improvement over several advanced methods in the literature. INDEX TERMS Spectrum sensing, correlated receiving multiple antennas, time-varying fading channel, cognitive radio network.

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Performance Analysis of Volume-Based Spectrum Sensing for Cognitive Radio

Cited by 45 publications

References 38 publications

Covariance-Based Spectrum Sensing for Noncircular Signal in Cognitive Radio Networks With Uncalibrated Multiple Antennas

Covariance-Based Spectrum Sensing for Noncircular Signal in Cognitive Radio Networks With Uncalibrated Multiple Antennas

Estimação das Distribuições da Estatística de Teste GRCR sob as Hipóteses H0 e H1

Generalized Real-Valued Weighted Covariance-Based Detection Methods for Cognitive Radio Networks With Correlated Multiple Antennas

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