scholarly journals Channel Estimation for Massive MIMO Systems

Polireddi Sireesha

Abstract: In MIMO millimeter-wave (mmWave) systems, while the hybrid digital/analog precoding structure provides the ability to increase the reach rate, it also faces the challenge of reducing the channel time limit due to the large number of horns on both sides of the Tx / Rx. . In this paper, channel measurement is done by searching with multiple beams, and a new hierarchical multi-beam search system is proposed, using a pre-designed analog codebook. Performance tests show that, compared to a highperformance system, the proposed system not only achieves a high level of success in getting multiple beams under normal system settings but also significantly reduces channel estimation time Keywords: Massive MIMO, Channel Estimation, precoding

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 218 ◽  
Kifayatullah Bangash ◽  
Imran Khan ◽  
Jaime Lloret ◽  
Antonio Leon

Traditional Minimum Mean Square Error (MMSE) detection is widely used in wireless communications, however, it introduces matrix inversion and has a higher computational complexity. For massive Multiple-input Multiple-output (MIMO) systems, this detection complexity is very high due to its huge channel matrix dimension. Therefore, low-complexity detection technology has become a hot topic in the industry. Aiming at the problem of high computational complexity of the massive MIMO channel estimation, this paper presents a low-complexity algorithm for efficient channel estimation. The proposed algorithm is based on joint Singular Value Decomposition (SVD) and Iterative Least Square with Projection (SVD-ILSP) which overcomes the drawback of finite sample data assumption of the covariance matrix in the existing SVD-based semi-blind channel estimation scheme. Simulation results show that the proposed scheme can effectively reduce the deviation, improve the channel estimation accuracy, mitigate the impact of pilot contamination and obtain accurate CSI with low overhead and computational complexity.

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 713 ◽  
Omar A. Saraereh ◽  
Imran Khan ◽  
Qais Alsafasfeh ◽  
Salem Alemaishat ◽  
Sunghwan Kim

Pilot contamination is the reuse of pilot signals, which is a bottleneck in massive multi-input multi-output (MIMO) systems as it varies directly with the numerous antennas, which are utilized by massive MIMO. This adversely impacts the channel state information (CSI) due to too large pilot overhead outdated feedback CSI. To solve this problem, a compressed sensing scheme is used. The existing algorithms based on compressed sensing require that the channel sparsity should be known, which in the real channel environment is not the case. To deal with the unknown channel sparsity of the massive MIMO channel, this paper proposes a structured sparse adaptive coding sampling matching pursuit (SSA-CoSaMP) algorithm that utilizes the space–time common sparsity specific to massive MIMO channels and improves the CoSaMP algorithm from the perspective of dynamic sparsity adaptive and structural sparsity aspects. It has a unique feature of threshold-based iteration control, which in turn depends on the SNR level. This approach enables us to determine the sparsity in an indirect manner. The proposed algorithm not only optimizes the channel estimation performance but also reduces the pilot overhead, which saves the spectrum and energy resources. Simulation results show that the proposed algorithm has improved channel performance compared with the existing algorithm, in both low SNR and low pilot overhead.

Felipe Augusto Pereira de Figueiredo

In this brief letter we report our initial results on the application of deep-learning to the massive MIMO channel estimation challenge. We show that it is possible to estimate wireless channels and that the possibility of mitigating pilot-contamination with deep-learning is possible given that the leaning model underwent an extensive training-phase and that it has been presented with a large number of different channel conditions.

2020 ◽  
pp. 1-1
Lei Zhou ◽  
Jisheng Dai ◽  
Weichao Xu ◽  
Chunqi Chang

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 49738-49749
Ting Jiang ◽  
Maozhong Song ◽  
Xuejian Zhao ◽  
Xu Liu

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