scholarly journals Sparse Bayes Tensor and DOA Tracking Inspired Channel Estimation for V2X Millimeter Wave Massive MIMO System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4021
Author(s):  
Kaihua Luo ◽  
Xiaoping Zhou ◽  
Bin Wang ◽  
Jifeng Huang ◽  
Haichao Liu
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Traffic Safety ◽  
Massive Mimo ◽  
Mimo System ◽  
Wireless Channel ◽  
Low Rank ◽  
Fast Time ◽  
Time Varying ◽  
Redundant Dictionary

Efficient vehicle-to-everything (V2X) communications improve traffic safety, enable autonomous driving, and help to reduce environmental impacts. To achieve these objectives, accurate channel estimation in highly mobile scenarios becomes necessary. However, in the V2X millimeter-wave massive MIMO system, the high mobility of vehicles leads to the rapid time-varying of the wireless channel and results in the existing static channel estimation algorithms no longer applicable. In this paper, we propose a sparse Bayes tensor and DOA tracking inspired channel estimation for V2X millimeter wave massive MIMO system. Specifically, by exploiting the sparse scattering characteristics of the channel, we transform the channel estimation into a sparse recovery problem. In order to reduce the influence of quantization errors, both the receiving and transmitting angle grids should have super-resolution. We obtain the measurement matrix to increase the resolution of the redundant dictionary. Furthermore, we take the low-rank characteristics of the received signals into consideration rather than singly using the traditional sparse prior. Motivated by the sparse Bayes tensor, a direction of arrival (DOA) tracking method is developed to acquire the DOA at the next moment, which equals the sum of the DOA at the previous moment and the offset. The obtained DOA is expected to provide a significant angle information update for tracking fast time-varying vehicular channels. The proposed approach is evaluated over the different speeds of the vehicle scenarios and compared to the other methods. Simulation results validated the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art researches.

scholarly journals Millimeter Wave Time-Varying Channel Estimation via Exploiting Block-Sparse and Low-Rank Structures

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 123355-123366 ◽  
Author(s):  
Long Cheng ◽  
Guangrong Yue ◽  
Daizhong Yu ◽  
Yueyue Liang ◽  
Shaoqian Li
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Low Rank ◽  
Time Varying ◽  
Time Varying Channel

scholarly journals On the Performance of Efficient Channel Estimation Strategies for Hybrid Millimeter Wave MIMO System

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1121
Author(s):  
Prateek Saurabh Srivastav ◽  
Lan Chen ◽  
Arfan Haider Wahla
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Optimization Problem ◽  
Mimo Systems ◽  
Mimo System ◽  
Low Rank ◽  
Potential Candidate ◽  
Alternating Direction ◽  
The Individual ◽  
Joint Channel

Millimeter wave (mmWave) relying upon the multiple output multiple input (MIMO) is a new potential candidate for fulfilling the huge emerging bandwidth requirements. Due to the short wavelength and the complicated hardware architecture of mmWave MIMO systems, the conventional estimation strategies based on the individual exploitation of sparsity or low rank properties are no longer efficient and hence more modern and advance estimation strategies are required to recapture the targeted channel matrix. Therefore, in this paper, we proposed a novel channel estimation strategy based on the symmetrical version of alternating direction methods of multipliers (S-ADMM), which exploits the sparsity and low rank property of channel altogether in a symmetrical manner. In S-ADMM, at each iteration, the Lagrange multipliers are updated twice which results symmetrical handling of all of the available variables in optimization problem. To validate the proposed algorithm, numerous computer simulations have been carried out which straightforwardly depicts that the S-ADMM performed well in terms of convergence as compared to other benchmark algorithms and also able to provide global optimal solutions for the strictly convex mmWave joint channel estimation optimization problem.

scholarly journals Grouping-Based Channel Estimation and Tracking for Millimeter Wave Massive MIMO Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Rui Yin ◽  
Xin Zhou ◽  
Wei Qi ◽  
Celimuge Wu ◽  
Yunlong Cai
Keyword(s):  
Computational Complexity ◽  
Channel Estimation ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Closed Form Solution ◽  
Network Capacity ◽  
Base Station ◽  
Estimation Accuracy

Although the millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) system can potentially boost the network capacity for future communications, the pilot overhead of the system in practice will greatly increase, which causes a significant decrease in system performance. In this paper, we propose a novel grouping-based channel estimation and tracking approach to reduce the pilot overhead and computational complexity while improving the estimation accuracy. Specifically, we design a low-complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS). With the recovered signal eigenvectors, the celebrated multiple-signal classification (MUSIC) algorithm can be employed to estimate the direction of arrival (DoA) angles and the path amplitude for the user terminals (UTs). To improve the estimation accuracy and accelerate the tracking speed, we develop a closed-form solution for updating the step-size in the proposed iterative algorithm. Furthermore, a grouping method is proposed to reduce the number of sharing pilots in the scenario of multiple UTs to shorten the pilot overhead. The computational complexity of the proposed algorithm is analyzed. Simulation results are provided to verify the effectiveness of the proposed schemes in terms of the estimation accuracy, tracking speed, and overhead reduction.

scholarly journals Deep Double-Pilot-Based Hybrid Precoding in UAV-Enabled MmWave Massive MIMO

2020 ◽  
Author(s):  
Ziyao Hong ◽  
Ting Li ◽  
Fei Li
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
Unmanned Aerial Vehicle ◽  
Millimeter Wave ◽  
Communication System ◽  
Massive Mimo ◽  
The Other ◽  
Time Varying ◽  
Hybrid Precoding ◽  
Aerial Vehicle

Abstract Unmanned aerial vehicle (UAV) enabled communication system provides flexibility and reliability compared to conventional ones. Millimeter wave and massive MIMO have widely been researched since recent years, which are promising techniques for the next and even the later generation communication system. Hybrid precoding, as a method to reduce the high cost in hardware and power brought by massive antenna array, develops fiercely and is often combined to deep learning, a kind of popular optimization tool, which brings overwhelming performance. On the other hand, there are not so many attentions about the hybrid precoding in time-varying millimeter wave massive MIMO, which is necessary to be considered in a UAV-enabled communication scenario because the performance will degrade seriously if the channel changed while the transmitter and receiver use the precoding matrix corresponding to the expired channel, yet. In this paper, we propose a double-pilot-based hybrid precoding system, which completes analog precoding and digital precoding separately--predicting the previous one using deep learning structure and updating equivalent channel frequently for the post one by enhancing the frequency of equivalent channel estimation.

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