Title :
E2KF based joint multiple CFOs and channel estimate for MIMO-OFDM systems over high mobility scenarios
Author :
Qiao Jing ; Chen Qingchun ; Shen Feifei
Author_Institution :
Key Lab. of Inf. Coding & Transm., Southwest Jiaotong Univ., Chengdu, China
Abstract :
An enhanced extended Kalman filtering (E2KF) algorithm is proposed in this paper to cope with the joint multiple carrier frequency offsets (CFOs) and time-variant channel estimate for MIMO-OFDM systems over high mobility scenarios. It is unveiled that, the auto-regressive (AR) model not only provides an effective method to capture the dynamics of the channel parameters, which enables the prediction capability in the EKF algorithm, but also suggests an method to incorporate multiple successive pilot symbols for the improved measurement update.
Keywords :
Kalman filters; MIMO communication; OFDM modulation; autoregressive processes; channel estimation; nonlinear filters; time-varying channels; CFO; E2KF algorithm; MIMO-OFDM systems; autoregressive model; extended Kalman filtering; joint multiple carrier frequency offsets; multiple successive pilot symbols; time-variant channel estimation; Channel estimation; Covariance matrices; Joints; Kalman filters; Mathematical model; OFDM; Vectors; CFO; MIMO-OFDM; channel estimate;
Journal_Title :
Communications, China
DOI :
10.1109/CC.2014.7022526
