The optimality of the conventional maximum likelihood sequence estimation
(MLSE), also known as the Viterbi Algorithm (VA), relies on the assumption that
the receiver has perfect knowledge of the channel coefficients or channel state
information (CSI). However, in practical situations that fail the assumption,
the MLSE method becomes suboptimal and then exhaustive checking is the only way
to obtain the ML sequence. At this background, considering directly the ML
criterion for partial CSI, we propose a two-phase low-complexity MLSE
algorithm, in which the first phase performs the conventional MLSE algorithm in
order to retain necessary information for the backward VA performed in the
second phase. Simulations show that when the training sequence is moderately
long in comparison with the entire data block such as 1/3 of the block, the
proposed two-phase MLSE can approach the performance of the optimal exhaustive
checking. In a normal case, where the training sequence consumes only 0.14 of
the bandwidth, our proposed method still outperforms evidently the conventional
MLSE.Comment: 5 pages and 4 figure