he semi-blind estimator. To begin with, the whitening matrix Wm can be assumed to be known at the receiver. This assumption, as shown in [52], holds well with the transmission of a few hundred OQAM data symbols, because the accuracy for estimating the matrix Wm is sufficiently high. It follows from [52] that CRLB for the MSE of the semi-blind estimator for (1, k)th element P. Singh and E. Sharma Hm(k,l) of the CSI matrix Hm is expressed as (3.52) whereQm(1,j) and Sm (k, i), respectively, represent the (1, j)t th element of Qm the (k, i)th element of Sm. It is important to note that the weighting factor milm mij gives the net reduction in the CSI estimation error in comparison to that of the pilot-aided LS CSI estimator. Furthermore, it follows from [50] that the minimum error for estimating Hm by using the semi-blind technique is bounded as (3.53) where No = N2 gives the number of real parameters required to parameterize Qm [54]. It also implies that when the estimation of the whitening matrix Wm is performed accurately by using the blind information of data symbols, the CSI matrix Hm = WmQH also requires estimation of only N2 parameters. These parameters can be estimated with high degree of accuracy by using a limited pilot overhead. The constraint CRLB for the estimation of the CSI matrix in (3.48) is given as The CRLB for CSI estimation per parameter is which reduces with increasing the number of receive antennas Nr. Upon using (3.53) and (3.50), the MSE gain of the semi-blind technique over the pilot-based LS technique is (3.54) Since Nr > Nt, it is immediately clear that 2Nr K > 2. It follows from (3.54) that for square MIMO-FBMC system that has Nr = N the semi-blind technique outperforms the LS technique up to 3 dB. Furthermore, the MSE gain G can be increased by increasing the number of receive antennas Nr. Figure 3.4a shows numerical results to verify MSE gain of the semi-blind (SB) technique over the conventional LS scheme quantified in (3.54). For this study, an N X Nr MIMO-FBMC system with the number o