Semi-Blind Channel Estimation in Massive Mimo Systems with Different Priors on Data Symbols

Abstract

This paper investigates semi-blind channel estimation in massive multiple-input multiple-output (MIMO) systems using different priors on data symbols. We derive two tractable expectation-maximization (EM) based channel estimation algorithms; one based on a Gaussian prior and the other one based on a Gaussian mixture model (GMM) for the unknown data symbols. The numerical results show that the semiblind estimation schemes provide better channel estimates compared with the estimation based on training sequences only. The EM algorithm with a Gaussian prior provides superior channel estimates compared to the EM algorithm with a GMM prior in low signal-to-noise ratio (SNR) regime. However, the latter one outperforms the EM algorithm with Gaussian prior as the SNR or as the number antennas at the base station (BS) increases. Furthermore, the performance of the semi-blind estimators become closer to the genie-aided maximum likelihood estimator based on known data symbols as the number of antennas at the BS increases.