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.
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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. https://doi.org/10.1109/VTCFall.2015.7390864
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. https://doi.org/10.1109/GLOCOM.2011.6134024
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. https://doi.org/10.1109/TAP.2013.2254442
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. https://doi.org/10.1109/JSAC.2020.3000889
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. https://doi.org/10.1109/MCOM.2016.1600071CM
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. https://doi.org/10.1109/ALLERTON.2010.5706954
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. https://doi.org/10.1109/TVT.2016.2614994
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. https://doi.org/10.1109/TWC.2017.2718502
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. https://doi.org/10.1109/SPAWC.2016.7536855
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. https://doi.org/10.1109/TSP.2012.2194289
Gu YJ, Goodman NA, Hong SH, et al., 2014. Robust adaptive beamforming based on interference covariance matrix sparse reconstruction. Signal Process, 96:375–381. https://doi.org/10.1016/j.sigpro.2013.10.009
Heath RW, Gonzñlez-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. https://doi.org/10.1109/JSTSP.2016.2523924
Kabaoglu N, 2009. Target tracking using particle filters with support vector regression. IEEE Trans Veh Technol, 58(5):2569–2573. https://doi.org/10.1109/TVT.2008.2005723
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. https://doi.org/10.1109/MCOM.2017.1600422CM
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. https://doi.org/10.1109/TVT.2018.2811368
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. https://doi.org/10.1109/ICNC47757.2020.9049735
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. https://doi.org/10.1109/INFOCOM.2017.8056991
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. https://doi.org/10.1109/TAP.2017.2734243
Sayeed A, Brady J, 2013. Beamspace MIMO for high-dimensional multiuser communication at millimeter-wave frequencies. Proc IEEE Global Communications Conf, p.3679–3684. https://doi.org/10.1109/GLOCOM.2013.6831645
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
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. https://doi.org/10.1109/TSP.2019.2919411
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. https://doi.org/10.1016/j.dsp.2019.05.009
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. https://doi.org/10.1109/ICC.2016.7510902
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. https://doi.org/10.1109/78.790648
Author information
Authors and Affiliations
Contributions
Zhao YI designed the research. Zhao YI, Weixia ZOU, and Xuebin SUN processed the data. Zhao YI drafted the manuscript. Weixia ZOU and Xuebin SUN helped organize the manuscript. Zhao YI, Weixia ZOU, and Xuebin SUN revised and finalized the paper.
Corresponding authors
Ethics declarations
Zhao YI, Weixia ZOU, and Xuebin SUN declare that they have no conflict of interest.
Additional information
Project supported by the National Natural Science Foundation of China (No. 61971063)
Rights and permissions
About this article
Cite this article
Yi, Z., Zou, W. & Sun, X. Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond. Front Inform Technol Electron Eng 22, 777–789 (2021). https://doi.org/10.1631/FITEE.2000515
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2000515
Key words
- Massive multiple-input multiple-output
- Millimeter wave
- Channel estimation
- Vehicular communication
- Time-varying