Channel estimation in time-varying cooperative networks using Kalman filter

In this work, we investigate channel estimation in time-varying multi-relay cooperative network. Since channels vary with time, training blocks are inserted periodically to trace channel variation, and we adopt Kalman filter to take advantage of the temporal correlation of channel coefficients. By storing previous channel estimate, Kalman filter simply requires to process the newest observation to update current channel estimate with relatively low complexity. To perform data detection, we need to channel state information over each data block as well. Therefore, estimates over previous training blocks are interpolated to estimate channel over data blocks based on linear mean-square-error (LMMSE) criterion. Since estimates over training blocks are obtained from Kalman filter, it consequently improves estimation quality of the channel over the data blocks.