Blind MEM PDF Estimation for Detection in the Presence of Unknown Non-Gaussian Noise

Saberali, S. Mohammad; Amindavar, Hamidreza

doi:10.1109/lsp.2009.2027568

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The theoretic P D 's of the LO detector and the MF are calculated from (19) and (20). Note that I and J in (19) are calculated numerically using (5) and (6). The simulated P D 's of the LO and the MF are obtained from Monte Carlo simulations.…”

Section: For Known F W (X)mentioning

confidence: 99%

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Performance comparison between matched filter and locally optimal detector for composite hypothesis test with inaccurate noise

Tian

Guo

2018

IET signal process.

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This study considers the performance comparison between the matched filter (MF) and the locally optimal (LO) detector when the noise information is not accurate. For a given binary hypothesis testing problem, the authors derive the condition under which the LO detector performs worse than the MF with the assumptions of large number samples and low signal-to-noise ratio. The condition is an inequality, which involves the noise probability density function (pdf) used in the LO detector, the real noise pdf, and the noise variance. Simulations show that the condition is theoretically sound under both known and unknown noise pdf's.

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Section: For Known F W (X)mentioning

confidence: 99%

“…In strategy II, the noise is assumed to be a specific pdf, and the parameters of the pdf are estimated from the given observations [1, pp. 381–385, 5]. The detection problem becomes the composite hypothesis test and the generalised LRT (GLRT) provides the most commonly used solution.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison between matched filter and locally optimal detector for composite hypothesis test with inaccurate noise

Tian

Guo

2018

IET signal process.

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“…In such a situation, the interference which is generated by powerful primary users causes a non-978-1-4799-5359-2/14/$31.00 c 2014 IEEE Gaussian noise for secondary users [7]. Therefore, in order to eliminate the degrading effects of primary users interference and detect the low power signal of the secondary user, a non-linear detector can be designed by the application of an estimation of the mentioned non-Gaussian noise [8]. This estimation can be performed through the MEM.…”

Section: Introductionmentioning

confidence: 99%

Model order selection based on different information criteria for PDF estimation using maximum entropy method and application in cognitive radio systems

Hosseini

Saberali

Sabahi

2014

7'th International Symposium on Telecommunications (IST'2014)

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In this paper, we propose two new information criteria to select the desired model order for probability density function (PDF) estimation using the maximum entropy method (MEM). These two proposed information criteria are based on Akaike information criterion (AIC) and Bayesian information criterion (BIC), respectively. The PDF estimation using MEM can be presented using integer and fractional moments. We use two proposed information criteria by considering trade-off between the goodness of fit of the model and the complexity of the model which result in obtaining the appropriate model order. In underlay cognitive radio (CR) systems, the primary user makes a powerful interference for the secondary user which changes the system noise PDF to a non-Gaussian one. The MEM can estimate this non-Gaussian PDF which in turn can be used in nonlinear detection schemes to suppress the degrading effect of the primary user. The simulation results show the high accuracy of the proposed model order selection criteria.

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Malicious User Detection in Non-Orthogonal Multiple Access Based on Spectrum Analysis

Xia

Tao

et al. 2020

IEEE Signal Process. Lett.

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Blind MEM PDF Estimation for Detection in the Presence of Unknown Non-Gaussian Noise

Cited by 4 publications

References 11 publications

Performance comparison between matched filter and locally optimal detector for composite hypothesis test with inaccurate noise

Performance comparison between matched filter and locally optimal detector for composite hypothesis test with inaccurate noise

Model order selection based on different information criteria for PDF estimation using maximum entropy method and application in cognitive radio systems

Malicious User Detection in Non-Orthogonal Multiple Access Based on Spectrum Analysis

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