In this paper, we derive the Bayes-Optimal estimator based on approximate message passing (AMP) algorithm in massive multiple-input multiple-output (MIMO) systems, which requires statistical channel state information (CSI). According to the analysis of channel model in beam domain, the covariance matrix is derived for CSI acquisition. With the aid of statistical CSI, the convergence of the proposed algorithm has significant improvement in comparison with which use the expectation-maximization (EM) algorithm to fit the statistical CSI. Simulations show great mean squared error (MSE) performance that approximates the Minimum Mean Square Error (MMSE) estimator, and better convergence performance than other AMP algorithm can be achieved. Besides, the results prove that performance of the random pilot in this algorithm is close to that of the orthogonal pilot based on Zadoff-Chu sequences.
Bayesian Channel Estimation for Massive MIMO Communications
2016-05-01
217875 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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