MIMO-OFDM channel estimation based on subspace tracking

In this paper, we propose a channel estimation algorithm for multiple-input and multiple-output orthogonal frequency for division multiplexing (MIMO-OFDM) systems, which has considerably less leakage than DFT-based channel estimators. This algorithm uses the optimum low-rank channel approximation obtained by tracking the frequency autocorrelation matrix of the channel response. The coefficients corresponding to dominant eigenfactors of the autocorrelation matrix are estimated every OFDM block while the eigenfactors are only updated using the training block that is transmitted every M blocks due to the slowly-varying feature of the frequency autocorrelation. Simulation results show that the proposed algorithm can effectively reduce channel estimation error and thus improve system performance.