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A family of nonlinear equalizers: Sub-optimal Bayesian classifiers

A family of sub-optimal Bayesian equalizers is proposed in two versions: feed-forward and decision feedback. We show that this family of equalizers provides a range of gradual choices concerning the tradeoff between equalizer complexity and symbol error rate (SER). We also point out the SER equivalence between the simplest proposed structure (the simplest equalizer of the family) and Wiener linear equalizer (or the decision feedback equalizer for the decision feedback version). Some simulations results are also presented.