Subspace-Based Blind Channel Estimation for MIMO-OFDM Systems With Reduced Time Averaging

Among the various approaches recently proposed for blind estimation of wideband multiple-input-multiple-output (MIMO) wireless channels, subspace-based algorithms are particularly attractive due to their good performance and simple structure. These algorithms primarily exploit the orthogonality of the noise and signal subspaces of the correlation matrix of the received signals to estimate the unknown channel coefficients. In practice, the correlation matrix is unknown and must be estimated through time averaging over multiple received samples. To this end, the unknown channel must remain time invariant through the averaging process, which may pose a serious problem in practical applications. In this paper, to relax this requirement, we propose a novel subspace-based blind channel-estimation algorithm with reduced time averaging, as obtained by exploiting the frequency correlation among adjacent subcarriers in MIMO orthogonal frequency-division multiplexing (OFDM) systems. Simulation results show that the proposed approach outperforms other previously proposed methods within a reasonable averaging time over a Third-Generation Partnership Project (3GPP) spatial channel model.