Subspace Blind MIMO-OFDM Channel Estimation with Short Averaging Periods: Performance Analysis

Among all blind channel estimation problems, subspace-based algorithms are attractive due to its fast- converging nature. It 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 time samples. To this end, the wireless channel must be time-invariant over a sufficient time interval, which may pose a problem for wideband applications. We proposed a novel subspace-based blind channel estimation algorithm with short time averaging periods, as obtained by exploiting the frequency correlation among adjacent OFDM subcarriers. In this paper, asymptotic performance bounds of the proposed algorithm are investigated by using perturbation analysis. We also present numerical results of the proposed as well as referenced subspace-based methods, including cyclic prefix and virtual carriers approaches. Based on the asymptotic performance bounds, the proposed scheme is justified in obtaining a desired correlation matrix efficiently by reducing the number of the OFDM blocks for time averaging up to 85%.