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Volume 30 Issue 2
Jan.  2011
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Hu Luo-quan, Zhu Hong-bo. The Generation of Correlated Non-Gaussian Random Variables with Diffusion Processes[J]. Journal of Electronics & Information Technology, 2008, 30(2): 412-415. doi: 10.3724/SP.J.1146.2006.01083
Citation: Hu Luo-quan, Zhu Hong-bo. The Generation of Correlated Non-Gaussian Random Variables with Diffusion Processes[J]. Journal of Electronics & Information Technology, 2008, 30(2): 412-415. doi: 10.3724/SP.J.1146.2006.01083

The Generation of Correlated Non-Gaussian Random Variables with Diffusion Processes

doi: 10.3724/SP.J.1146.2006.01083
  • Received Date: 2006-07-20
  • Rev Recd Date: 2007-01-22
  • Publish Date: 2008-02-19
  • Random variables of non-Gaussian distribution are produced by diffusion processes. Under the assumption of ergodicity, the stationary distribution of Markov diffusion processes described by a Stochastic Differential Equation (SDE) is obtained, which is determined by drift coefficient and diffusion coefficient. Let the drift coefficient be the first order power of x, and then the diffusion coefficient can be derived as a function of diffusion coefficient and aimed probability density function. As a result, the SDE is determined, and its solution by using Milstein high order method produces the aimed random variables. The correlation of the random samples can be adjusted through changing the constant of diffusion coefficient. Taking the Nakagami distribution and K-distribution as examples, simulation results are similar to the theoretical value, which validates the effectiveness of this method.
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  • Rangaswamy M and Weiner D. Non-Gaussian random vectoridentification using spherically invariant random process.IEEE Trans. on AES , 1993, 29(1): 111-123.[2]Bede L and Munson D C. Generation of a random sequencegiving a jointly specified marginal distribution andautocovariance. IEEE Trans. on ASSP, 1982 , 30(6): 973-983.[3]Sondhi M M. Random processes with specified spectraldensity and first-order probability density. Bell SystemTechnical Journal, 1983, 62(3): 679-700.[4]Spurbeck M S and Scharf L L. Least squares filter design forperiodically correlated times series. IEEE Seventh SPWorkshop on Statistical Signal and Array Processing.Qubec, Canada, 1994: 267-270.[5]Kontorovitch V and Lyandres V. Stochastic differentialequations: An approach to the generation of continuous non-Gaussian processes[J].IEEE Trans, on SP.1995, 43(10):2372-2385[6]Primak S, Lyandres V, Kaufman O, and Kliger M. On thegeneration of correlated time series with a given probabilitydensity function[J].Signal Processing.1999, 72(2):61-68[7]Klebaner F. Introduction to Stochastic Calculus withApplication. London: Imperial College Press, 2001, Chap. 6.[8]张树京, 齐立心. 时间序列分析简明教程. 北京:清华大学出版社,北方交通大学出版社,2003, Chap. 2.Zhang S and Qi L. Course of Time Series[M]. Beijing:Tsinghua Univ Pr, North Jiaotong Univ. Pr., 2003, Chap. 2.[9]Kloeden P E, Platen E, and Schurz H. Numerical Solution ofSDE Through Computer Experiments. NY: Springer- Verlag,1994, Chap. 6.[10]Primak S.[J].Kontorovitch V, and Lyandres V. StochasticMethods and Their Applications to Communications:Stochastic Differential Equations Approach. NY: John Wiley Sons, Inc.2004,:-[11]Gradshteyn I S and Ryzhik I M. Table of Integrals, Series,and Products. 6th ed., Jeffrey A, Ed. NY: Academic,2000,Chap. 8.
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