Title :
A mixture Kalman filter approach for blind OFDM channel estimation
Author :
Zhang, Ruifeng ; Chen, Wei
Author_Institution :
Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA
Abstract :
Blind adaptive estimation of time-frequency-selective fading channels in OFDM systems is studied. Based on the Jakes´ model, the channel state is modeled as an autoregressive (AR) process. The post-DFT data gives the observation of the channel state. If training symbols are available, a Kalman filter (KF) can be employed to track the channel. In the absent of training symbols, it is proposed to use mixture Kalman filter (MKF) to blindly estimate the channel and information symbols jointly. In view of the high complexity of the MKF for jointly estimation of all subchannels, it is further proposed to use an MKF for each subchannel respectively. A minimum mean square error (MMSE) combiner is used to further refine the channel estimates from the MKFs. The persubchannel MKF explores the time-domain correlation of the channel, while the MMSE combiner explores the frequency-domain correlation. This two-step solution offers a performance comparable to the optimal solution.
Keywords :
Kalman filters; OFDM modulation; adaptive estimation; autoregressive processes; channel estimation; correlation theory; discrete Fourier transforms; fading channels; least mean squares methods; time-frequency analysis; Jakes model; MKF; MMSE combiner; OFDM system; autoregressive process; blind adaptive estimation; channel state model; frequency-domain correlation; information symbol; minimum mean square error; mixture Kalman filter; post-DFT data; subchannel estimation; time-domain correlation; time-frequency-selective fading channel; training symbol; Adaptive estimation; Blind equalizers; Channel estimation; Fading; Frequency domain analysis; Kalman filters; OFDM modulation; Time domain analysis; Time-varying channels;
Conference_Titel :
Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on
Print_ISBN :
0-7803-8622-1
DOI :
10.1109/ACSSC.2004.1399151