MIMO-OFDM Downlink Channel Prediction for IEEE802.16e Systems Using Kalman Filter

Channel state information (CSI) at the transmitter in multiple-input multiple-output (MIMO) downlink systems is indispensable to beamforming or precoding to take advantage of MIMO capacity. In time-division duplex (TDD) systems, CSI for downlink can be obtained from uplink channel using reciprocity. However, the CSI from uplink is not accurate enough to keep track of the continuously varying channel characteristic in downlink period. In this paper, a MIMO-OFDM downlink channel prediction technique based on Kalman filter is proposed for IEEE802.16e systems. The proposed method consists of three procedures: MMSE channel estimation, Kalman filtering and prediction, and linear interpolation. Kalman filter is employed to filter the estimated channel and to predict the next channel sample to determine the precoding weights. Simulation results demonstrate that the proposed method improves the bit error rate (BER) performance significantly.