On the Generation of Correlated Gaussian Random Variates by Inverse DTF

Author

Tavares, Gonçalo N. ; Petrolino, Antonio

Author_Institution

Dept. of Electr. & Comput. Eng., Inst. Super. Tecnico (IST), Lisbon, Portugal

Volume

59

Issue

1

fYear

2011

fDate

1/1/2011 12:00:00 AM

Firstpage

45

Lastpage

51

Abstract

In this paper the problem of generating a stationary band-limited Gaussian random vector with arbitrary complex autocorrelation by the inverse discrete Fourier transform (IDTF) algorithm is considered. Instead of using the classical frequency mask (FM), determined from samples of the (band-limited) target power spectral density (PSD) of the process, a new FM is obtained by matching the autocorrelation obtained with the IDFT algorithm to a desired arbitrary autocorrelation. Example results presented show that the new FM is able to significantly increase the autocorrelation accuracy of the generated process at no additional online computational cost.

Keywords

Gaussian distribution; correlation methods; discrete Fourier transforms; inverse transforms; random processes; vectors; arbitrary complex autocorrelation; correlated Gaussian random variate; inverse discrete Fourier transform; power spectral density; stationary band limited Gaussian random vector; Accuracy; Computational modeling; Correlation; Discrete Fourier transforms; Fading; Frequency modulation; Scattering; Gaussian random vector; fading channel simulation; inverse discrete Fourier transform;

fLanguage

English

Journal_Title

Communications, IEEE Transactions on

Publisher

ieee

ISSN

0090-6778

Type

jour

DOI

10.1109/TCOMM.2010.101910.090067

Filename

5610970