An investigation of Gaussian tail and Rayleigh tail density functions for importance sampling digital communication system simulation

The gains achieved by the use of importance sampling in communication system simulation are strongly influenced by the choice of the biased input noise distribution. The Rayleigh tail and the Gaussian tail distributions are investigated for use as biased distributions in importance sampling. The robustness of these schemes with respect to the estimate of the unknown error probability is examined. It is shown that the Gaussian tail distribution previously considered to be theoretically optimum is not optimum in this regard. Methods of generating the biased random variates are presented