In high-speed environment

fast fading and non-stationary limits the channel estimation performance

so we proposed a channel estimation method for high-speed mobility in downlink. The time-varying channel was modeled as an autoregressive (AR) process. So that a self-feedback extended Kalman filter (EKF) was set up to track the channel response and AR parameter. In order to eliminate the influence of error propagation in self-feedback EKF

an iterative detector decoder receiver was adopted

by utilizing the redundancy of encoding

to improve the estimation accuracy. The simulation results show that compared with least squares (LS) and linear minimum mean square errors (LMMSE) in high speed environment

the proposed method improves the channel estimation accuracy and performance of whole system. And it could be applied to baseband signal processing of wireless receiver in high-speed train.