Stochastic Methods and Their Applications to Communications

Primak, Serguei; Kontorovich, Valeri; Lyandres, V.

doi:10.1002/0470021187

Cited by 198 publications

(170 citation statements)

References 31 publications

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“…, ϑ n ). A different approach is based on the moment and cumulant characteristics [10,12,14,20]. One way to describe statistically dependent random variables involves the use of MD moments and cumulants.…”

Section: Mathematical Models Of Correlated Non-gaussian Processes Usimentioning

confidence: 99%

“…This can be achieved by using 2D cumulant models of the dimension (i, j), where the unknown coefficients k iv and k 0 of the DR are obtained from (20). Then the mean and the variance of the polynomial stochastic DR (20) under our hypotheses and the alternative, are defined as:…”

Section: ξ(T) = S(t) + η(T); η(T)-is a Non-gaussian Stationary Randommentioning

confidence: 99%

“…The optimal coefficients k iv of the polynomial stochastic DR (20) have to minimize the adapted new moment quality criterion Ku1(E, G, ρ) and are defined as…”

Section: ξ(T) = S(t) + η(T); η(T)-is a Non-gaussian Stationary Randommentioning

confidence: 99%

“…This information function is defined as a PDF form of the sample values. It is easy to show that the mean and variance of the polynomial stochastic DR (20) can be represented as Kullback-Leibler information number using a PDF for the hypothesis and the alternative. Then the moment quality criterion Ku1(E, G, ρ) decision making can also be defined by using a PDF.…”

Section: ξ(T) = S(t) + η(T); η(T)-is a Non-gaussian Stationary Randommentioning

confidence: 99%

“…This second approach can be represented in the form of higher-order statistics (HOS), see [12], such as moments and cumulant (semivariants) functions [14,20]. Such functions allow the description of the statistical properties of non-Gaussian processes with reasonable accuracy [10,14].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Signal Detection in Correlated Non-Gaussian Noise Using Higher-Order Statistics

Palahina

Gamcová

Gladisova

et al. 2017

Circuits Syst Signal Process

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The authors of this paper study the synthesis of new models and methods for signal detection in additive correlated non-Gaussian noise. A new moment quality criterion decision making is proposed based on a random process description using moments and a formation of polynomial decision rules. Taking into account parameters of non-Gaussian distribution of random variables (such as the moments of third and higher orders and joint cumulants), it is shown that nonlinear processing of samples can increase the signal processing efficiency. A synthesis of effective methods and algorithms of data processing in non-Gaussian noise is also presented in this work.

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Section: Mathematical Models Of Correlated Non-gaussian Processes Usimentioning

confidence: 99%

Section: ξ(T) = S(t) + η(T); η(T)-is a Non-gaussian Stationary Randommentioning

confidence: 99%

“…The optimal coefficients k iv of the polynomial stochastic DR (20) have to minimize the adapted new moment quality criterion Ku1(E, G, ρ) and are defined as…”

Section: ξ(T) = S(t) + η(T); η(T)-is a Non-gaussian Stationary Randommentioning

confidence: 99%

Section: ξ(T) = S(t) + η(T); η(T)-is a Non-gaussian Stationary Randommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Signal Detection in Correlated Non-Gaussian Noise Using Higher-Order Statistics

Palahina

Gamcová

Gladisova

et al. 2017

Circuits Syst Signal Process

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Advanced time series synthesizer for simulation of joint rain attenuation conditions

2014

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This contribution presents a model to generate time series of rain attenuation by adapting a large set of rain attenuation measurements to different locations. The proposed model allows generating rainy and not rainy conditions for multiple stations in a large geographical area, reproducing the actual spatial and temporal variability of the rain process, with limited computational effort. Single site and joint statistics of rain attenuation obtained by the time series of measurements are discussed. The model is used to show the effectiveness, in terms of link margin, of gateway networks in smart diversity arrangement, for different separation distances among sites.

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