Abstract
Millimeter wave (mmWave) system with massive multiple input multiple output (mMIMO) meets increasing data traffic requirements. However, fast beam tracking for vehicles with high mobility causes enormous overhead, especially in an ultra-dense network (UDN) with frequently base station (BS) handover. In this paper, we proposed a multi-site coordination beam tracking scheme utilizing the spatial correlation of channel state information (CSI) among different sites to reduce the signaling overhead for beam training and handover. The scenario is a hyper-cellular network (HCN) with one control-BS (CBS) and multiple traffic-BSs (TBSs). The proposed scheme consists of two stages. In the first stage, more accurate position measurement of the moving user equipment (UE) can be achieved by using uniform planar array (UPA), and Extended Kalman Filter (EKF) is exploited in CBS to predict the UE’s location in the next slot. In the second stage, the relationship between multi-site and UE’s location is used by the CBS to remotely infer the candidate beam between each TBS and the UE, and make a TBS handover decision when necessary. Given that it is the CBS in charge of beam tracking between all the TBSs and the UE centrally, the overhead for beam training and handover are both efficiently reduced. Simulation results based on realistic 3D scenario show that the proposed scheme can achieve 99% of the optimal spectral efficiency with fewer overhead for beam sweeping and handover signaling.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Shafi, M., et al.: 5G: a tutorial overview of standards, trials, challenges, deployment, and practice. IEEE J. Sel. Areas Commun. 35(6), 1201–1221 (2017)
Wang, J., et al.: Beam codebook based beamforming protocol for multi-Gbps millimeter-wave WPAN systems. IEEE J. Sel. Areas Commun. 27(8), 1390–1399 (2009)
Va, V., Choi, J., Heath, R.W.: The impact of beamwidth on temporal channel variation in vehicular channels and its implications. IEEE Trans. Veh. Technol. 66(6), 5014–5029 (2017)
Shen, Z., Xu, K., Wang, Y., Xie, W.: Angle-domain channel tracking for high speed railway communications with massive ULA. In: 2018 IEEE 18th International Conference on Communication Technology (ICCT), Chongqing, pp. 159–165 (2018)
Zhang, C., Guo, D., Fan, P.: Tracking angles of departure and arrival in a mobile millimeter wave channel. In: 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, pp. 1–6 (2016)
Shaham, S., Kokshoorn, M., Ding, M., Lin, Z., Shirvanimoghaddam, M.: Extended Kalman filter beam tracking for millimeter wave vehicular communications. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, pp. 1–6 (2020)
Talvitie, J., et al.: Positioning and location-based beamforming for high speed trains in 5G NR networks. In: 2018 IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, United Arab Emirates, pp. 1–7 (2018)
Wang, S., Chen, M., Liu, X., Yin, C., Cui, S., Poor, H.V.: A machine learning approach for task and resource allocation in mobile edge computing based networks. IEEE Internet Things J. (2020). https://doi.org/10.1109/jiot.2020.3011286
Zhou, S., Zhao, T., Niu, Z., Zhou, S.: Software-defined hyper-cellular architecture for green and elastic wireless access. IEEE Commun. Mag. 54(1), 12–19 (2016)
Jiang, Z., Chen, S., Molisch, A.F., Vannithamby, R., Zhou, S., Niu, Z.: Exploiting wireless channel state information structures beyond linear correlations: a deep learning approach. IEEE Commun. Mag. 57(3), 28–34 (2019)
Chen, S., et al.: Remote channel inference for beamforming in ultra-dense hyper-cellular network. In: GLOBECOM 2017 – 2017 IEEE Global Communications Conference, Singapore, pp. 1–6 (2017)
Chen, S., Jiang, Z., Zhou, S., Niu, Z.: Time-sequence channel inference for beam alignment in vehicular networks. In: 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, pp. 1199–1203 (2018)
Goldsmith, A.: Wireless Communications. Cambridge University Press, Cambridge (2005). https://doi.org/10.1017/CBO9780511841224
Talvitie, J., Levanen, T., Koivisto, M., Valkama, M.: Positioning and tracking of high-speed trains with non-linear state model for 5G and beyond systems. In: 2019 16th International Symposium on Wireless Communication Systems (ISWCS), Oulu, Finland, pp. 309–314 (2019)
Bar-Shalom, T.K.Y., Li, X.R.: Estimation with Applications to Tracking and Navigation: Theory. Algorithms and Software. Wiley, Hoboken (2002)
Remcom, Wireless insite. http://www.remcom.com/wireless-insite
Acknowledgement
This work is supported by the National Natural Science Foundation of China under Grant No. 61971069, 61801051, and Key R&D Program Projects in Shanxi Province under Grant No. 2019ZDLGY07-10. This work is also supported by Docomo Beijing Lab.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
He, X., Liu, D., Zhang, Z. (2021). Location-Based Multi-site Coordination Beam Tracking for Vehicle mmWave Communications. In: Gao, H., Fan, P., Wun, J., Xiaoping, X., Yu, J., Wang, Y. (eds) Communications and Networking. ChinaCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 352. Springer, Cham. https://doi.org/10.1007/978-3-030-67720-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-67720-6_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67719-0
Online ISBN: 978-3-030-67720-6
eBook Packages: Computer ScienceComputer Science (R0)