MIMO-OFDM wireless channel prediction by exploiting spatial correlation

Channel prediction is an appealing approach to mitigate channel mismatch in frequency division duplex (FDD) systems. In the presence of correlation among the transmit or receive antennas, it will improve the performance of channel prediction if utilizing the correlation information. In this article, we first propose a MIMO-OFDM channel prediction model on time domain, which considers and exploits the spatial correlation. Then we derive two predictors based on the proposed model, which select data for auto-regressive (AR) modeling in different ways. The first predictor, called the best data predictor, chooses the desired data set via minimizing the mean square error (MSE) of prediction model. Yet the second predictor, called reduced-complexity subset predictor, chooses the data in a heuristic way, which aims to reduce the computational complexity. Both of the two predictors can exploit the temporal and spatial correlation adaptively. Our simulations show that the new algorithms outperform conventional methods which ignore the spatial correlation.