What is the difference between Maximum Likelihood Sequence Estimation and Maximum Likelihood Estimation? Which one is a better choice in case of channel non-linearities? And why and how oversampling helps in this?
MLSE is mathematical algorithm meant to extract meaningful data from a noisy scenario. MLE on the other hand is a criterion for parameter estimation such that likelihood of the sample is maximized. In case of nonlinearities I guess the former is a better choice because for MLE to be applied it must be in a modelling situation.
MLSE is an optimum equalizer that make the decision on the symbol based on observation of a sequence of received signals over successive signal intervals. It searches the minimum Euclidean distance path through the trellis that characterizes the memory in transmitted signal.
The Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a specific model. It selects the set of values of the model parameters that maximizes the likelihood function. Intuitively, this maximizes the "agreement" of the selected model with the observed data.
Thank you very much @Leila and @Ette,
Actually the thing I'm more wondered about is the "sequence" in MLSE. Does that mean it's the same symbol, detected multiple times to improve link reliability. Particularly, if we have a multipolarized scenario, can I detect the symbol transmitted at multiple polarizations using MLSE?
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