Performance improvement via AR modeling based linear prediction for TDD-MIMO systems

The quality of channel state information (CSI) affects the performance of wireless systems. This is especially true for multiple input multiple output (MIMO) systems which use multiple antennas at transmitter and receiver. In time division duplex (TDD) systems, CSI for downlink can be obtained from uplink channel using reciprocity principal. However, the performance of MIMO system can be degraded due to continuously varying channel characteristic when CSI obtained from uplink is used for downlink transmission. In this study, we investigate the performance of autoregressive (AR) modeling based channel prediction under varying channel propagation (mobile speed, multipath number and angle spread) and prediction filter order. Simulation results demonstrate that using predicted CSI for downlink improves the performance of relative channel prediction error significantly.