Channel estimation for MIMO-OFDM systems in non-sample-spaced multipath channels

The decision-directed space-alternating generalized expectation-maximization (SAGE) algorithm is introduced in [1] to estimate the channel and track the channel varying for OFDM systems with transmitter diversity. However, this method is based upon a discrete Fourier transform (DFT), which will cause power leakage and result in an error floor in a multipath channel with non-sample-spaced time delays. In order to overcome this problem, a low rank approximation method is presented by using the signal subspace of the channel frequency autocorrelation matrix. Furthermore, a modified fast subspace tracking algorithm is introduced to adaptive estimate the signal subspace by using a space-time block coded training blocks, sent at regular interval. Simulation results show the advantages of the proposed technique for MIMO-OFDM systems in time-varying non-sample-spaced wireless channels.