In a recent contribution, assuming pilot-only based channel estimation, we proposed an improved receiver which reduces the impact of channel uncertainty on the decoder performance. Here, we focus on semi-blind channel estimation based on expectation-maximization (EM) algorithm and propose an improved receiver design by an appropriate use of the channel estimation error statistics. This is done by means of an equivalent formulation of the EM algorithm from which we extract the statistics of the estimation errors. This additional soft information is used at each iteration of the receiver in a Bayesian framework to improve the performance of iterative detection. Our approach is compared to the conventional EM algorithm where the channel estimation error statistics are not available and where the receiver uses the channel estimate as if it was the perfect channel (also known as mismatched detection). Numerical results in the context of orthogonal frequency-division multiplexing show that the proposed approach outperforms the classically-used EM algorithm in terms of bit error rates.
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