A quadratic complexity eigenspace technique for blind SIMO channel identification

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

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

We present linear (in terms of subchannel impulse responses) noise subspace parameterizations and we prove that using a specic parameterization, which is minimal in terms of the

For the PRP-OFDM, after initial acquisition, the channel es- timate is then refined by a MMSE based semi-blind procedure using an averaging window of 72 and 20 OFDM symbols.. In

Simulations show that the proposed scheme is robust in noisy environments and channel length underestimation, and per- forms better compared to the classic Delay-&-Predict

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-

However, in [5] they show that the practical ML channel estimator preserves the diversity order of MRC (Maximum Ratio Combining), see also [6] for more profound analysis. In this