"Blind Identification and Equalization of MIMO FIR Channels Based on Subspace Decomposition and Independent Component Analysis"

The rst one (MFB1) uses the channel model to perform the spatio-temporal matched ltering that yields data reduction from multichannel to single-channel form.. The rest of the

As a result, a ZF equalizer can be determined from the received signal second- order statistics by linear prediction in the noise- free case, and by using a Pisarenko-style modi-

Abstract : Blind channel identification and equalization based on second-order statistics by subspace fitting and linear prediction have received a lot of attention lately1. On

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,

Semi-Blind Channel Identification – Blind+TS method In this section, we suppose that the channel is blindly identified up to a unitary mixing matrix by a blind method, and that

It is observed that especially for the sparse scenario, an estimate of the desired user channel is obtained in a small number of data samples, despite the fact that the processing

In this section, we will investigate the time-domain counterpart of this approach; namely, we will study the possibility of blind SIMO equalization and/or channel identification

Since the blind receiver relies on the inversion of the signal covariance matrix R Y Y , a consistent estimate of which requires a large number of data points if a large number of