Full Text:   <8063>

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CLC number: TN928

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2021-05-01

Cited: 0

Clicked: 5831

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhao Yi

https://orcid.org/0000-0001-7131-4232

Weixia Zou

https://orcid.org/0000-0002-1452-9787

Xuebin Sun

https://orcid.org/0000-0002-7508-4945

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

http://doi.org/10.1631/FITEE.2000515


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.

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pages="777-789",
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Abstract: 
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.

基于先验信息的5G及后5G毫米波大规模多入多出车载通信信道估计

易钊1,邹卫霞1,2,孙学斌1
1北京邮电大学泛网无线通信教育部重点实验室,中国北京市,100876
2东南大学毫米波国家重点实验室,中国南京市,210096
摘要:毫米波(mmWave)被认为是5G及后5G高带宽车载通信的可行解决方案。为实现在未来车辆通信中的应用,鲁棒的毫米波车载网络非常重要。然而,一个挑战是,毫米波应在车辆或车辆到基础设施(V2I)之间提供高速和超高速数据交换。此外,由于车辆的高速移动引起毫米波信道快速变化,传统的实时信道估计方案难以实现。针对这些问题,提出一种毫米波V2I车辆通信信道估计方法。首先考虑快速运动的车辆场景,建立相应的快速时变信道数学模型。然后,利用基站与每个移动用户之间的时间变化规律和确定的到达方向,预测时变信道先验信息(PI)。最后,利用PI和信道特性对时变信道进行估计。仿真结果表明,在毫米波时变车载通信系统中,该方案在归一化均方误差和和率性能上均优于传统方案。

关键词:大规模多入多出;毫米波;信道估计;车辆通信;时变

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

Reference

[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.

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