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

Millimeter-wave massive MIMO can effectively improve the signal-to-noise ratio, but the high-
dimensional channel matrix significantly increases the complexity of the classic channel …

In this paper, we design a compressed sensing (CS) based channel estimation method for
millimeter wave (mmWave) massive MIMO systems and investigate the impact of dual …

The compressive sensing (CS)-based sparse channel estimator is recognized as the most
effective solution to the excessive pilot overhead in massive MIMO systems. However, due to …

In this letter, we formulate an off-grid channel model to characterize spatial sample
mismatching in the discrete Fourier transform (DFT) based massive multiple-input-multiple …

Compressed sensing (CS) has great potential in channel estimation for millimeter wave
(mmWave) massive multiple-input multiple-output (MIMO) systems. To solve such a CS …

Accurate channel estimation with low pilot overhead is vital to exploit full benefit of Millimeter
wave (mmWave) massive multiple-input multiple-output (MIMO) system. Although …

In this paper, we exploit a novel model-driven deep learning (MDDL)-based scheme for
efficient wideband millimeter-wave (mmWave) massive multiple-input multiple-output …

Channel estimation is essential for millimeter-wave (mmWave) multiple-input multiple-output
(MIMO) systems with hybrid precoding. However, accurate channel estimation is a …

In millimeter-wave massive multiple-input multiple-output systems, to decrease the large
training overhead of traditional channel estimation techniques, compressive sensing (CS) is …