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The optimality of the conventional maximum likelihood sequence estimation(MLSE), also known as the Viterbi Algorithm (VA), relies on the assumption thatthe receiver has perfect knowledge of the channel coefficients or channel stateinformation (CSI). However, in practical situations that fail the assumption,the MLSE method becomes suboptimal and then exhaustive checking is the only wayto obtain the ML sequence. At this background, considering directly the MLcriterion for partial CSI, we propose a two-phase low-complexity MLSEalgorithm, in which the first phase performs the conventional MLSE algorithm inorder to retain necessary information for the backward VA performed in thesecond phase. Simulations show that when the training sequence is moderatelylong in comparison with the entire data block such as 1/3 of the block, theproposed two-phase MLSE can approach the performance of the optimal exhaustivechecking. In a normal case, where the training sequence consumes only 0.14 ofthe bandwidth, our proposed method still outperforms evidently the conventionalMLSE.

A Two-Phase Maximum-Likelihood Sequence Estimation for Receivers with Partial CSI