Structured spatio-temporal sample covariance matrix enhancement with application to blind channel estimation in cyclic prefix systems

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.

Blind channel identication and equalization based on sec- ond-order statistics by subspace tting and linear prediction have received a lot of attention lately.. On the other hand,

Abstract : Blind channel identication and equalization based on second-order statistics by subspace tting and linear prediction have received a lot of attention lately.. On the

The proposed blind channel estimation method is based on the second order statistics of the received signal, in addition, the algorithm exploits knowledge of the desired spreading

In this paper, we propose an alternative way to characterize the FR estimated channel using a joint ST covariance matrix, combined with a low-complexity semi-parametric spatial

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-

In this paper we assume the channel to be estimated at the Rx using blind and semi-blind deterministic algorithms and we investigate the effect of the resulting channel esti-