Performance improvements from decision-delay adaption in adaptive MLSE equalizers

This paper analyzes a fully adaptive maximum-likelihood sequence estimator (MLSE) equalizer in fast fading channel conditions. The receiver adaptively estimates the channel state information, and also adapts to learn the channel fading statistics. The equalizer is analyzed using pairwise error probabilities to derive lower bounds on performance. A decision delay adaption rule is proposed, based on the bit-error rate analysis. With this new rule, the fully adaptive MLSE equalizer is shown to outperform existing fixed-delay equalizers when channel statistics are unknown. Simulation studies confirm that the performance is close to the analytic bounds.