| Summary: | Abstract One of the main challenges for a massive multi-input multi-output (MIMO) system is to obtain accurate channel state information despite the increasing number of antennas at the base station. The Bayesian learning channel estimation methods have been developed to reconstruct the sparse channel. However, these existing methods depend heavily on the channel distribution. In this paper, based on sparse Bayesian method, an expectation maximization-based parameter iterative approach is proposed to estimate the massive MIMO channel with unknown channel distribution. Using the approximate sparse feature, the massive MIMO channel is modeled as a non-zero Gaussian mixture and the sparse Bayesian channel estimation is introduced. The channel marginal probability density function is expressed by using the general approximate message-passing algorithm. All of the required channel parameters are iteratively estimated by the EM method. Simulation results show that the proposed scheme enables evident performance in channel estimation accuracy with a lower complexity when channel distribution is unknown.
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