Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL

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 the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation is sometimes “unfriendly” to the channel recovery. To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. Simulation results demonstrate that the UTAMP-SBL is able to achieve effective performance improvement than other competitors with low pilot overhead.

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Sparse Channel Estimation for Millimeter Wave Massive MIMO Systems With Lens Antenna Array

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2019.2938541 ◽

2019 ◽

Vol 68 (11) ◽

pp. 11348-11352 ◽

Cited By ~ 6

Author(s):

Xiantao Cheng ◽

Ying Yang ◽

Binyang Xia ◽

Ning Wei ◽

Shaoqian Li

Keyword(s):

Channel Estimation ◽

Antenna Array ◽

Millimeter Wave ◽

Massive Mimo ◽

Mimo Systems ◽

Lens Antenna ◽

Sparse Channel Estimation ◽

Sparse Channel

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WSDSBL Method for Wideband Channel Estimation in Millimeter-Wave MIMO Systems with Lens Antenna Array

Journal of Communications ◽

10.12720/jcm.15.11.826-832 ◽

2020 ◽

pp. 826-832

Author(s):

Jicheng Dong ◽

◽

Wei Zhang ◽

Bowen Yang ◽

Xihong Sang

Keyword(s):

Channel Estimation ◽

Millimeter Wave ◽

Communication Systems ◽

Bayesian Learning ◽

Mimo Systems ◽

Estimation Accuracy ◽

Sparse Bayesian Learning ◽

Low Snr ◽

Estimation Scheme ◽

Wideband Channel

In millimeter wave (mmWave) communication systems, Channel State Information(CSI) is extremely essential for beamforming. The traditional Successive Support Detection (SSD) algorithm can achieve high wideband channel estimation accuracy, but it only used least square (LS) algorithm to recover the detected channel part, the estimation accuracy is low under low SNR regions. To tackle this problem, in this paper, inspired by the classic Support Detection (SD) channel estimation scheme in narrowband, we propose an efficient Wideband Support Detection Sparse Bayesian Learning (WSDSBL) channel estimation scheme. For every subcarrier, we first detect the support of the wideband beamspace channel of the subcarrier, then we use the Sparse Bayesian Learning (SBL) scheme to recover it. Simulation results show that the proposed WSDSBL channel estimation algorithm is better than conventional wideband channel estimation schemes in MSE performance and achievable sum-rate performance, especially in low SNR regions.

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Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1546-6 ◽

2019 ◽

Vol 2019 (1) ◽

Author(s):

Xiao Chen ◽

Zaichen Zhang ◽

Liang Wu ◽

Jian Dang

Keyword(s):

Channel Estimation ◽

Power Allocation ◽

Spectral Efficiency ◽

Communication Systems ◽

Mimo Systems ◽

Beam Steering ◽

Estimation Method ◽

Low Complexity ◽

Estimation Accuracy ◽

Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

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