We address the downlink channel estimation problem for massive multiple-input multiple-
output (MIMO) systems in this paper, where the inherit burst-sparsity structure is exploited to …

This paper addresses the problem of massive multiple-input multiple-output (MIMO) channel
estimation in the presence of impulsive noise. In the literature, a sparse Bayesian learning …

This letter provides a novel hierarchical-block channel estimation scheme for massive
multiple-input multiple-output (MIMO) systems based on Bayesian learning frameworks …

How to obtain accurate channel state information at the base station (CSIT) is a key
implementation challenge behind frequency-division duplex massive MIMO systems …

To reduce the pilot overhead of downlink channel estimation in massive multiple-input-
multiple-output (MIMO) systems, a sparse recovery algorithm relying on the vector …

The knowledge of CSI at the BS (CSIT) is required to achieve the high spectrum efficiency
promised by massive MIMO. In Frequency-Division Duplex (FDD) Massive MIMO systems …

To estimate time-varying MIMO channel at base station, traditional downlink (DL) channel
restoration schemes usually require the reconstruction for the covariance of downlink …

This paper addresses wideband channel estimation in an uplink massive multiple-input
multiple-output (MIMO) system, which consists of multiple single-antenna users and a base …

Channel estimation is one of the key issues in practical massive multiple-input multiple-
output (MIMO) systems. Compared with conventional estimation algorithms, deep learning …

To address the challenging problem of downlink channel estimation with low pilot overhead
in massive multiple-input multiple-output (MIMO) systems, an empirical Bayesian block …