Subspace-based Blind Channel Estimation for MIMO-OFDM Systems: Reducing the Time Averaging Interval of the Correlation Matrix

Subspace-based blind channel estimation primarily exploits the orthogonality structure of the noise and signal subspaces by applying a signal-noise space decomposition to the correlation matrix of the received signal. In practice, the correlation matrix is unknown and must be estimated through time averaging over multiple symbol blocks. To this end, the wireless channel must be time-invariant over a sufficient time interval, which may pose a problem for wideband applications. In this paper, we propose a novel subspace-based blind channel estimation algorithm with a reduced time averaging interval, as obtained by exploiting the frequency correlation among adjacent OFDM subcarriers. We present simulation results of the proposed as well as referenced subspace-based methods, including Cyclic Prefix and Virtual Carriers approaches, and show that the proposed scheme is able to obtain a desired correlation matrix by reducing the number of the OFDM blocks for time averaging up to 85 %.