Channel Estimation for Fdd Massive Mimo Using Bayesian Estimator

Massive MIMO systems that for a cellular network, the channel from user equipment to a base station is composed of few grouped paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. In the FDD mode, each BS sends a downlink training matrix to its served UEs which estimates the desired channel based on the downlink measurements and feeds back observed CSI through dedicated uplink feedback channels. The Bayesian estimator does not require prior statistical knowledge of the channel responses, which is learned as part of the estimation procedure. The approximate message passing (AMP) algorithm is employed to obtain the Bayesian inference and an expectation–maximization (EM) algorithm to learn the statistical properties. By a proper design on pilot sequences, the proposed estimator leads to a much reduced complexity without the need of second order statistics. The proposed approach achieves much better performance in the presence of pilot contamination when compared to other conventional methods. Keywords— MIMO, FDD, Bayesian Estimator, CSI, Downlink _________________________________________________________________________________________________________________