The inability of reconfigurable intelligent surfaces (RISs) to signal processing has become a
critical bottleneck in terms of the precise capture of cascaded channels. There exists a pair …

In this paper, we investigate the massive multiple-input multiple-output orthogonal frequency
division multiplexing channel estimation for low-earth-orbit satellite communication systems …

In this article, we present two clustered sparse Bayesian learning (Cluster-SBL) channel
estimation algorithms for millimeter-wave (mmWave) massive multiple-input-multiple-output …

Large antenna arrays for mmWaves use hybrid analog/digital processing to reduce the
number of RF-chains. For such systems, this paper proposes a new channel estimation …

Bayesian modelling of the channel distribution is a crucial step before channel recovery
specially in the underdetermined scenario in multiple input multiple output (MIMO) antenna …

We propose a Bayesian deep learning framework for model driven online sparse channel
estimation task in Multi-user MIMO systems. Tools from Bayesian neural network and …

In massive multiple-input multiple-output (MIMO) systems, the large size of channel state
information (CSI) matrix significantly increases the computational complexity of uplink …

In this paper, we study blind channel-and-signal estimation by exploiting the burst-sparse
structure of angular-domain propagation channels in massive MIMO systems. The state-of …

For many practical applications in wireless communications, we need to recover a structured
sparse signal from a linear observation model with dynamic grid parameters in the sensing …

In this paper, we study joint antenna activity detection, channel estimation, and multiuser
detection for massive multiple-input multiple-output (MIMO) systems with general spatial …