Integration based frequency offset estimate for the MIMO-OFDM system

In this paper, we focus on the frequency offset estimation for a MIMO system with OFDM transmission technique. In our system, we directly approximate the a posteriori distribution employing the Gauss-Hermite integration in the preamble interval. Using this proposed approach, a better frequency offset estimation can be achieved in relatively large frequency offsets compared to the extended Kalman filter based approach over a quasi-static channel environment.

[1]  Kazufumi Ito,et al.  Gaussian filters for nonlinear filtering problems , 2000, IEEE Trans. Autom. Control..

[2]  Petar M. Djuric,et al.  Gaussian sum particle filtering for dynamic state space models , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[3]  T. Reid,et al.  A sequential Monte-Carlo Kalman filter based delay and channel estimation method in the MIMO-OFDM system , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[4]  J. Naylor,et al.  Applications of a Method for the Efficient Computation of Posterior Distributions , 1982 .

[5]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[6]  Ronald A. Iltis,et al.  A sequential Monte Carlo filter for joint linear/nonlinear state estimation with application to DS-CDMA , 2003, IEEE Trans. Signal Process..

[7]  K. Ito.  Gaussian filter for nonlinear filtering problems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[8]  Jiang Yue,et al.  A QRD-M/Kalman filter-based detection and channel estimation algorithm for MIMO-OFDM systems , 2005, IEEE Transactions on Wireless Communications.