A large deviations theory-based technique for the 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 (1990). For the additive Gaussian channel, the resulting simulation density is equivalent to the conventional 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 (1970) and simulation results are presented for both the recursive and non-recursive equalizers