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A subspace method for soft estimation of block fading channels in turbo equalization

The performance of iterative (turbo) equalization strongly relies on the quality of the channel state information (CSI). The use of soft information for channel estimation has been largely investigated in the literature to improve the reliability of the conventional training-based estimate. In this paper we propose a new soft method for the estimation of block-fading channels based on a multi-block (MB) processing. As turbo equalization usually requires a joint processing of several data-blocks, the idea is to take advantage of this latency to improve the performance of channel estimation. The MB estimator exploits the invariance of the subspace spanned by the channel vector over a frame of blocks and it estimates the corresponding basis from different blocks. In this paper the MB method is extended to incorporate the soft information provided by the turbo equalizer. The mean square error for the soft estimate is evaluated analytically and validated by simulations. The comparison with the conventional block-by-block estimate shows the benefits of the proposed approach on the turbo equalization convergence.