Next Article in Journal
Location-Based Resource Allocation in Ultra-Dense Network with Clustering
Previous Article in Journal
Region-Based Static Video Stitching for Reduction of Parallax Distortion
Previous Article in Special Issue
A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations
Article

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

The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Omprakash Kaiwartya
Sensors 2021, 21(12), 4021; https://doi.org/10.3390/s21124021
Received: 2 April 2021 / Revised: 5 June 2021 / Accepted: 6 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communications)
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to the rapid time-varying of the wireless channel and results in the existing static channel estimation algorithms no longer applicable.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. View Full-Text
Keywords: vehicle to everything (V2X); mmWave massive MIMO; channel estimation; direction of arrival (DOA) tracking vehicle to everything (V2X); mmWave massive MIMO; channel estimation; direction of arrival (DOA) tracking
Show Figures

Figure 1

MDPI and ACS Style

Luo, K.; Zhou, X.; Wang, B.; Huang, J.; Liu, H. Sparse Bayes Tensor and DOA Tracking Inspired Channel Estimation for V2X Millimeter Wave Massive MIMO System. Sensors 2021, 21, 4021. https://doi.org/10.3390/s21124021

AMA Style

Luo K, Zhou X, Wang B, Huang J, Liu H. Sparse Bayes Tensor and DOA Tracking Inspired Channel Estimation for V2X Millimeter Wave Massive MIMO System. Sensors. 2021; 21(12):4021. https://doi.org/10.3390/s21124021

Chicago/Turabian Style

Luo, Kaihua; Zhou, Xiaoping; Wang, Bin; Huang, Jifeng; Liu, Haichao. 2021. "Sparse Bayes Tensor and DOA Tracking Inspired Channel Estimation for V2X Millimeter Wave Massive MIMO System" Sensors 21, no. 12: 4021. https://doi.org/10.3390/s21124021

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop