Block decision feedback equalization

A natural generalization of the conventional decision feedback equalizer (DFE) based on block processing and maximum a posteriori decisions is presented. This block DFE is indexed by two parameters depending on the block length p and the number of decisions q⩽p made at each iteration. The block DFE emulates: the conventional DFE when p=q=1; the maximum likelihood sequence estimator when p=q→∞; and the maximum symbol-by-symbol a posteriori optimal detector when q=1, p→∞. For more general (p,q) parameter settings, one achieves combinations and simplifications of these classical nonlinear detectors. The authors also recover more recently developed nonlinear equalizer structures based on forward tree search algorithms in the high SNR limit (for q =1). They simulate the equalizer using a standard literature example. The theoretical framework presented accommodates autoregressive moving average (ARMA) channels models. Investigations into the error performance of the block DFE are briefly discussed