Training sequence assisted channel estimation for MIMO OFDM

In this paper we present several results of our study on training sequence assisted channel estimation for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. After developing a linear matrix algebraic model for the cyclic prefix based MIMO OFDM systems, we define a generalised preamble structure which is a simple extension of the preambles used in single-input single-output (SISO) OFDM so that its good properties, such as low peak to average power ratio, can be maintained. We then derive the least squares (LS) and linear minimum mean squared error (LMMSE) channel estimation algorithms based on the proposed preamble design. In order to reduce the preamble length, we further propose a switched subcarrier preambles scheme in which the transmit antennas are divided into groups, and preambles are transmitted in alternative subset of subcarriers in each group. A LMMSE filter-based interpolation scheme and a DFT-based LS interpolation scheme are then used to obtain the channel estimates for all the subcarriers of interest. In all the proposed schemes in this paper, the filter parameters can be fixed and robust performance is obtained even when mismatched SNR and channel statistics are used in the filter parameter calculations.