Cramer-Rao bounds and methods for knowledge based estimation of multiple FIR channels.

We present two approaches to stochastic Maximum Like- lihood identication of multiple FIR channels, where the input symbols are assumed Gaussian and the channel de- terministic1.

training sequence based estimation in which a sequence of symbols known by the receiver is used and blind equal- ization in which the channel and/or the symbols

Training sequence methods base the parameter estimation on the received signal containing known symbols only and all the other observations, contain- ing (some) unknown symbols,

known symbols are grouped in a training sequence: the suboptimal semi{blind cri-.. teria appear as a linear combination of a training sequence criterion and a

Blind identi- fiability in the deterministic model (i.e. identifiability up to a scale factor) requires the channel to be irreducible and the burst length and the number of

Since the prior information is expressed in terms of the channel impulse response, we review a number of blind channel estimation methods that are parameterized directly by the

In the Gaussian symbols case on the other hand, robustness to channel length overdetermination and channel zeros arises, and the channel unidentifiablity gets reduced to a block

Blind channel estimation techniques have also been devel- oped for time-varying channels, by using so-called Basis Expansion Models (BEMs), in which the time-varying chan-