A randomized bias technique for the importance sampling simulation of Bayesian equalizers

An importance sampling (IS) simulation technique is presented for Bayesian equalizers, based on the large deviations theory approach developed by Sadowsky and Bucklew (see ibid., vol.36, no.5, p.579, 1990). The resulting simulation density consists of a sum of exponentially twisted distributions. For the additive Gaussian channel, this simulation density is equivalent to the conventional (mean-shift) noise biasing IS method, but with the bias vector chosen from a fixed set in a random manner. In order to properly select the bias vectors, the asymptotic decision boundary of the Bayesian equalizer is first determined. It is shown that the boundary is formed by multiple hyperplanes, and that the appropriate bias vectors are orthogonal to the hyperplanes. The simulation technique is then extended to the recursive symbol-by-symbol detector of Abend and Fritchman (A-F algorithm) proposed in 1970, and simulation results are presented for both recursive and non-recursive equalizers.<>