Iterative soft-kalman filter-based data detection and channel estimation for turbo coded MIMO-OFDM systems

MIMO channels are often assumed to be constant over a block or packet. This assumption of block stationarity is valid for many fixed wireless scenarios. However, for communications in a mobile environment, the stationarity assumption will result in considerable performance degradation. In this paper, we focus on a new channel estimation technique for Turbo coded MIMO systems using OFDM. In the proposed MIMO-OFDM system, pilots are placed on selected subcarriers and used by a pair of Kalman filter (KF) channel estimators at the receiver. The KF channel estimates are then utilized by a MIMO-OFDM soft data detector based on the computationally efficient QRD-M algorithm. The soft detector output is fed back to the Kalman filters to iteratively improve the channel estimates. The extrinsic information generated by the Turbo decoder is also used as a priori information for the soft data detector. The overall receiver thus combines MIMO data detection, KF-based channel estimation, and Turbo decoding in a joint iterative structure yielding computational efficiency and improved bit-error rate (BER) performance. © 2007 Springer Science+Business Media, LLC.