We consider channel estimation specific to turbo equalization for multiple-input multiple-output (MIMO) wireless communication. We develop soft-decision-driven sequential algorithms geared to a specific pipelined turbo equalizer architecture operating on orthogonal frequency division multiplexing (OFDM) symbols. One interesting feature of the pipelined turbo equalizer is that multiple soft-decisions become available at various processing stages. A tricky issue is the fact that these multiple decisions from different pipeline stages have correlated decision errors as well as varying levels of reliability. This paper establishes an optimization strategy for the channel estimator to track the target channel while dealing with observation sets with different qualities. The resulting algorithm is basically a linear sequential estimation algorithm and, as such, is Kalman-like in nature. The main difference here, however, is that the proposed algorithm must deal with the inherent correlation that exist among the multiple module outputs that cannot easily be removed by the traditional innovation approach. The proposed algorithm continuously monitor the quality of the feedback decisions and incorporate it in the channel estimation process. The proposed channel estimation schemes show certain performance and complexity advantages over existing EM-based algorithms.
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