Adaptive frequency-domain channel estimation based on Kalman smoothing for uplink MIMO OFDMA systems

We propose an adaptive frequency-domain channel estimator based on Kalman smoothing for uplink multiple input multiple output (MIMO) orthogonal frequency division multiplexing access (OFDMA) systems. The application of Kalman smoothing in frequency-domain channel estimation results in a much lower computational complexity than time-domain estimation approaches. Channel interpolation is employed to improve the accuracy of the Kalman smoothing based channel estimation by exploiting the correlation between adjacent subcarriers. Simulation results show that the proposed Kalman smoothing based channel estimator outperforms the Kalman filtering based channel estimator. It can also achieve a bit error rate (BER) performance which is close to the case with perfect channel state information (CSI) with a training overhead of only 5%.