A cost-effective maximum likelihood receiver for multicarrier systems

TLDR: Results indicate that near-optimum performance can be achieved by using the general optimum class of structures derived that includes the decision feedback equalizer and the lesser-known autoregressive moving average filters at a great computational reduction.

Abstract: Equalization structures for maximum likelihood (ML) reception of data transmitted over intersymbol interference channels are studied. The equalizer that is best for the ML receiver is derived from a general theory of decision-aided equalization. The resulting optimum equalizers are linear and do not use previous decisions. If the equalizer complexity is permitted to be infinite, then a general optimum class of structures is derived that includes the decision feedback equalizer and the lesser-known autoregressive moving average filters. When a complexity constraint is also imposed on the equalizer, one of the structures in this class will be best for a given ML receiver. The best structure is found by a simple search procedure, which is given. The results indicate that near-optimum performance can be achieved by using this approach at a great computational reduction. >