Full Text:   <3055>

Summary:  <1251>

CLC number: TN928

On-line Access: 2021-07-12

Received: 2020-09-30

Revision Accepted: 2021-01-21

Crosschecked: 2021-05-01

Cited: 0

Clicked: 4621

Citations:  Bibtex RefMan EndNote GB/T7714


Zhao Yi


Weixia Zou


Xuebin Sun


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.6 P.777-789


Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond

Author(s):  Zhao Yi, Weixia Zou, Xuebin Sun

Affiliation(s):  MOE Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China; more

Corresponding email(s):   yz17tx@bupt.edu.cn, zwx0218@bupt.edu.cn

Key Words:  Massive multiple-input multiple-output, Millimeter wave, Channel estimation, Vehicular communication, Time-varying

Zhao Yi, Weixia Zou, Xuebin Sun. Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(6): 777-789.

@article{title="Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond",
author="Zhao Yi, Weixia Zou, Xuebin Sun",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond
%A Zhao Yi
%A Weixia Zou
%A Xuebin Sun
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 6
%P 777-789
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000515

T1 - Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond
A1 - Zhao Yi
A1 - Weixia Zou
A1 - Xuebin Sun
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 6
SP - 777
EP - 789
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000515

millimeter wave (mmWave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5G and beyond. To realize applications in future vehicular communications, it is important to take a robust mmWave vehicular network into consideration. However, one challenge in such a network is that mmWave should provide an ultra-fast and high-rate data exchange among vehicles or vehicle-to-infrastructure (V2I). Moreover, traditional real-time channel estimation strategies are unavailable because vehicle mobility leads to a fast variation mmWave channel. To overcome these issues, a channel estimation approach for mmWave V2I communications is proposed in this paper. Specifically, by considering a fast-moving vehicle secnario, a corresponding mathematical model for a fast time-varying channel is first established. Then, the temporal variation rule between the base station and each mobile user and the determined direction-of-arrival are used to predict the time-varying channel prior information (PI). Finally, by exploiting the PI and the characteristics of the channel, the time-varying channel is estimated. The simulation results show that the scheme in this paper outperforms traditional ones in both normalized mean square error and sum-rate performance in the mmWave time-varying vehicular system.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Awad MM, Seddik KG, Elezabi A, 2015. Channel estimation and tracking algorithms for harsh vehicle to vehicle environments. Proc IEEE 82nd Vehicular Technol Conf, p.1-5.

[2]Bourdoux A, Cappelle H, Dejonghe A, 2011. Channel tracking for fast time-variant channels in IEEE802.11p systems. Proc IEEE Global Telecommunications Conf, p.1-6.

[3]Brady J, Behdad N, Sayeed AM, 2013. Beamspace MIMO for millimeter-wave communications: system architecture, modeling, analysis, and measurements. IEEE Trans Antenn Propag, 61(7):3814-3827.

[4]Brighente A, Cerutti M, Nicoli M, et al., 2020. Estimation of wideband dynamic mmWave and THz channels for 5G systems and beyond. IEEE J Sel Areas Commun, 38(9):2026-2040.

[5]Choi J, Va V, Gonzalez-Prelcic N, et al., 2016. Millimeter-wave vehicular communication to support massive automotive sensing. IEEE Commun Mag, 54(12):160-167.

[6]Fernandez JA, Stancil D, Bai F, 2010. Dynamic channel equalization for IEEE 802.11p waveforms in the vehicle-to-vehicle channel. Proc 48th Annual Allerton Conf on Communication, Control, and Computing, p.542-551.

[7]Gao XY, Dai LL, Zhang Y, et al., 2017a. Fast channel tracking for terahertz beamspace massive MIMO systems. IEEE Trans Veh Technol, 66(7):5689-5696.

[8]Gao XY, Dai LL, Han SF, et al., 2017b. Reliable beamspace channel estimation for millimeter-wave massive MIMO systems with lens antenna array. IEEE Trans Wirel Commun, 16(9):6010-6021.

[9]Garcia N, Wymeersch H, Ström EG, et al., 2016. Location-aided mm-Wave channel estimation for vehicular communication. Proc IEEE 17th Int Workshop on Signal Processing Advances in Wireless Communications, p.1-5.

[10]Gu YJ, Leshem A, 2012. Robust adaptive beamforming based on interference covariance matrix reconstruction and steering vector estimation. IEEE Trans Signal Process, 60(7):3881-3885.

[11]Gu YJ, Goodman NA, Hong SH, et al., 2014. Robust adaptive beamforming based on interference covariance matrix sparse reconstruction. Signal Process, 96:375-381.

[12]Heath RW, Gonz alez-Prelcic N, Rangan S, et al., 2016. An overview of signal processing techniques for millimeter wave MIMO systems. IEEE J Sel Top Signal Process, 10(3):436-453.

[13]Kabaoglu N, 2009. Target tracking using particle filters with support vector regression. IEEE Trans Veh Technol, 58(5):2569-2573.

[14]Kong LH, Khan MK, Wu F, et al., 2017. Millimeter-wave wireless communications for IoT-cloud supported autonomous vehicles: overview, design, and challenges. IEEE Commun Mag, 55(1):62-68.

[15]Ma X, Yang F, Liu SC, et al., 2018. Sparse channel estimation for MIMO-OFDM systems in high-mobility situations. IEEE Trans Veh Technol, 67(7):6113-6124.

[16]Mehrabi M, Mohammadkarimi M, Ardakani M, et al., 2020. A deep learning based channel estimation for high mobility vehicular communications. Proc Int Conf on Computing, Networking and Communications, p.338-342.

[17]Palacios J, De Donno D, Widmer J, 2017. Tracking mm-Wave channel dynamics: fast beam training strategies under mobility. Proc IEEE Conf on Computer Communications, p.1-9.

[18]Rappaport TS, Xing YC, MacCartney GR, et al., 2017. Overview of millimeter wave communications for fifth-generation (5G) wireless networks—with a focus on propagation models. IEEE Trans Antenn Propag, 65(12):6213-6230.

[19]Sayeed A, Brady J, 2013. Beamspace MIMO for high-dimensional multiuser communication at millimeter-wave frequencies. Proc IEEE Global Communications Conf, p.3679-3684.

[20]Shaham S, Ding M, Kokshoorn M, et al., 2018. Fast channel estimation and beam tracking for millimeter wave vehicular communications. https://arxiv.org/abs/1806.00161

[21]Shen WQ, Dai LL, An JP, et al., 2019. Channel estimation for orthogonal time frequency space (OTFS) massive MIMO. IEEE Trans Signal Process, 67(16):4204-4217.

[22]Wu XH, Zhu WP, Yan J, 2019. Channel estimation and tracking with nested sampling for fast-moving users in millimeter-wave communication. Digit Signal Process, 94:29-37.

[23]Zhang C, Guo DN, Fan PY, 2016. Tracking angles of departure and arrival in a mobile millimeter wave channel. Proc IEEE Int Conf on Communications, p.1-6.

[24]Zhou YF, Yip PC, Leung H, 1999. Tracking the direction-of-arrival of multiple moving targets by passive arrays: algorithm. IEEE Trans Signal Process, 47(10):2655-2666.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE