A New Method for Generating Gaussian Random Variates With Clarke's Autocorrelation

We present a new method for efficient and accurate generation of a discrete-time series of N complex Gaussian random variates with Clarke's autocorrelation. When the product f
_D
N is small (f
_D
is the Doppler frequency normalized with respect to the sampling frequency), the proposed method provides variates with accurate autocorrelation over the full lag range at computational complexity equal to that of the well-known inverse discrete Fourier transform method. In addition, the new method is a better practical alternative to the optimum Karhunen-Loève expansion, particularly for large N values.