Low-Complexity Map Channel Estimation for Mobile MIMO-OFDM Systems

This paper presents a reduced-complexity maximum a posteriori probability (MAP) channel estimator with iterative data detection for orthogonal frequency division multiplexing (OFDM) systems over mobile multiple-input multiple- output channels. The optimal MAP estimator needs to invert an NN
_T
x NN
_T
data-dependent matrix each in OFDM symbol interval, where N is the number of subcarriers and N
_T
is the number of transmit antennas. We derive an expectation maximization (EM) algorithm with low-rank approximation to avoid inverting large-size matrices, and thus drastically reduce the receiver complexity. In the iterative process, channel parameters are initially obtained by a least square (LS) estimator for temporary symbol decisions. Then, inter-carrier interference (ICI) due to fast fading is approximated and canceled. Finally, the temporary symbol decisions and the ICI-canceled received signals are processed by the EM-based MAP estimator to refine the channel state information for improved detection. The proposed scheme achieves about 2 dB gain over the LS scheme in channels with medium to high normalized Doppler shifts.