Channel Prediction in MIMO-OFDM Wireless Systems by Exploiting Spatio- Temporal Correlations

Channel prediction is an appealing technique to mitigate the performance degradation due to the inevitable feedback delay of the channel state information (CSI) in modern wireless systems. In the existing method, the 2-step algorithm fixes the prediction order, which first exploits temporal correlation temporal correlation and then follows spatial correlation. Our proposed method is selected flexibly from both spatial and temporal domains. We first propose a general MIMO-OFDM channel prediction framework, so that both the spatial and temporal correlation among antennas is exploited. Then our proposed predictor, reduced complexity FSS algorithm selects data for auto regressive (AR) predictor according to the proposed framework and chooses the data in heuristic way, which aims to reduce the computational complexity. An application to our algorithm is discussed to improve the precoding performance in multi-user MIMO-OFDM systems. Simulation results show that the proposed reduced complexity fss method can perform better even in the presence of feedback delay.