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

Volume

11

Issue

13

fYear

2014

fDate

Supplement 2014

Firstpage

56

Lastpage

63

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;

fLanguage

English

Journal_Title

Communications, China

Publisher

ieee

ISSN

1673-5447

Type

jour

DOI

10.1109/CC.2014.7022526

Filename

7022526