A New Algorithm for MIMO Channel Tracking Based on Kalman Filter

Abstract

The estimation of a time-varying fading channel in multiple-input multiple-output (MIMO) wireless systems is a difficult task for the receiver. Its performance can be improved if an appropriate channel estimation filter is used according to the prior knowledge of the fading channel. The authors propose an algorithm which uses the Kalman filter based on Clarke's model to track the MIMO time-varying fading channel by using superimposed periodic training sequences. To reduce the complexity of the high-dimensional Kalman filter for channel estimation of the paths, the authors use a low-dimensional Kalman filter for the estimation of each path. An analytical formula for the estimation error due to the temporal variation of the channel coefficients is given and verified by link level simulations based on synthetic and measured impulse responses. Simulation results show this algorithm is effective for the estimation of the time-varying fading channel in MIMO system when the performance of the channel estimation is presented in terms of the mean-square error (MSE).