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Open Access

Space-air-ground integrated vehicular network for connected and automated vehicles: Challenges and solutions

Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada
School of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Department of Information and Communication Engineering, Xiamen University, Xiamen 361005, China
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Abstract

Unlimited and seamless coverage as well as ultra-reliable and low-latency communications are vital for connected vehicles, in particular for new use cases like autonomous driving and vehicle platooning. In this paper, we propose a novel Space-Air-Ground integrated vehicular network (SAGiven) architecture to gracefully integrate the multi-dimensional and multi-scale context-information and network resources from satellites, High-Altitude Platform stations (HAPs), low-altitude Unmanned Aerial Vehicles (UAVs), and terrestrial cellular communication systems. One of the key features of the SAGiven is the reconfigurability of heterogeneous network functions as well as network resources. We first give a comprehensive review of the key challenges of this new architecture and then provide some up-to-date solutions on those challenges. Specifically, the solutions will cover the following topics: (1) space-air-ground integrated network reconfiguration under dynamic space resources constraints; (2) multi-dimensional sensing and efficient integration of multi-dimensional context information; (3) real-time, reliable, and secure communications among vehicles and between vehicles and the SAGiven platform; and (4) a holistic integration and demonstration of the SAGiven. Finally, it is concluded that the SAGiven can play a key role in future autonomous driving and Internet-of-Vehicles applications.

References

[1]
M. Sheng, Y. Wang, J. D. Li, R. Z. Liu, D. Zhou, and L. J. He, Toward a flexible and reconfigurable broadband satellite network: Resource management architecture and strategies, IEEE Wirel. Commun., vol. 24, no. 4, pp. 127-133, 2017.
[2]
Z. H. Tan, H. L. Qin, L. Cong, and C. Zhao, New method for positioning using iridium satellite signals of opportunity, IEEE Access, vol. 7, pp. 83 412-83 423, 2019.
[4]
C. Niephaus, M. Kretschmer, and G. Ghinea, QoS provisioning in converged satellite and terrestrial networks: A survey of the state-of-the-art, IEEE Commun. Surv. Tutor., vol. 18, no. 4, pp. 2415-2441, 2016.
[5]
Y. H. Ruan, Y. Z. Li, C. X. Wang, R. Zhang, and H. L. Zhang, Power allocation in cognitive satellite-vehicular networks from energy-spectral efficiency tradeoff perspective, IEEE Trans. Cognit. Commun. Netw., vol. 5, no. 2, pp. 318-329, 2019.
[6]
Z. W. Hou, X. Q. Yi, Y. H. Zhang, Y. H. Y. Kuang, and Y. Zhao, Satellite-ground link planning for LEO satellite navigation augmentation networks, IEEE Access, vol. 7, pp. 98 715-98 724, 2019.
[7]
N. Alam, T. Balaei, and A. G. Dempster, Relative positioning enhancement in VANETs: A tight integration approach, IEEE Trans. Intell. Transp. Syst., vol. 14, no. 1, pp. 47-55, 2013.
[8]
X. B. Cao, P. Yang, M. Alzenad, X. Xi, D. P. Wu, and H. Yanikomeroglu, Airborne communication networks: A survey, IEEE J. Sel. Areas Commun., vol. 36, no. 9, pp. 1907-1926, 2018.
[9]
S. Chandrasekharan, K. Gomez, A. Al-Hourani, S. Kandeepan, T. Rasheed, L. Goratti, L. Reynaud, D. Grace, I. Bucaille, T. Wirth, et al., Designing and implementing future aerial communication networks, IEEE Commun. Mag., vol. 54, no. 5, pp. 26-34, 2016.
[10]
S. Karapantazis and F. Pavlidou, Broadband communications via high-altitude platforms: A survey, IEEE Commun. Surv. Tutor., vol. 7, no. 1, pp. 2-31, 2005.
[11]
G. Araniti, A. Iera, and A. Molinaro, The role of HAPs in supporting multimedia broadcast and multicast services in terrestrial-satellite integrated systems, Wirel. Pers. Commun., vol. 32, nos. 3&4, pp. 195-213, 2005.
[12]
A. Mohammed, A. Mehmood, F. N. Pavlidou, and M. Mohorcic, The role of high-altitude platforms (HAPs) in the global wireless connectivity, Proc. IEEE, vol. 99, no. 11, pp. 1939-1953, 2011.
[13]
E. Mack, Meet Google’s ‘project loon’: Balloon-powered net access, https://www.cnet.com/news/meet-googles-project-loon-balloon-powered-net-access/, 2013.
[14]
A. K. Widiawan and R. Tafazolli, High altitude platform station (HAPS): A review of new infrastructure development for future wireless communications, Wirel. Pers. Commun., vol. 42, no. 3, pp. 387-404, 2007.
[15]
G. Avdikos, G. Papadakis, and N. Dimitriou, Overview of the application of High Altitude Platform (HAP) systems in future telecommunication networks, in Proc. 2008 10thInt. Workshop on Signal Processing for Space Communications, Rhodes Island, Greece, 2008.
[16]
Y. G. Lin, L. Wang, and L. F. Shen, Satellite and high altitude platform-based inter-vehicle communications in vast and desolate areas, J. Southeast Univ. Eng. Ed., vol. 28, no. 2, pp. 135-139, 2012.
[17]
L. Gupta, R. Jain, and G. Vaszkun, Survey of important issues in UAV communication networks, IEEE Commun. Surv. Tutor., vol. 18, no. 2, pp. 1123-1152, 2016.
[18]
Y. Zeng, R. Zhang, and T. J. Lim, Wireless communications with unmanned aerial vehicles: Opportunities and challenges, IEEE Commun. Mag., vol. 54, no. 5, pp. 36-42, 2016.
[19]
Y. Zhou, N. Cheng, N. Lu, and X. S. Shen, Multi-UAV-aided networks: Aerial-ground cooperative vehicular networking architecture, IEEE Veh. Technol. Mag., vol. 10, no. 4, pp. 36-44, 2015.
[20]
M. Khabbaz, J. Antoun, and C. Assi, Modeling and performance analysis of UAV-assisted vehicular networks, IEEE Trans. Veh. Technol., vol. 68, no. 9, pp. 8384-8396, 2019.
[21]
L. J. Deng, G. Wu, J. W. Fu, Y. Z. Zhang, and Y. F. Yang, Joint resource allocation and trajectory control for UAV-enabled vehicular communications, IEEE Access, vol. 7, pp. 132 806-132 815, 2019.
[22]
M. Garzón, J. Valente, D. Zapata, and A. Barrientos, An aerial-ground robotic system for navigation and obstacle mapping in large outdoor areas, Sensors, vol. 13, no. 1, pp. 1247-1267, 2013.
[23]
N. Goddemeier, K. Daniel, and C. Wietfeld, Role-based connectivity management with realistic air-to-ground channels for cooperative UAVs, IEEE J. Sel. Areas Commun., vol. 30, no. 5, pp. 951-963, 2012.
[24]
N. Lu, N. Cheng, N. Zhang, X. M. Shen, and J. W. Mark, Connected vehicles: Solutions and challenges, IEEE Internet Things J., vol. 1, no. 4, pp. 289-299, 2014.
[25]
K. Zheng, Q. Zheng, P. Chatzimisios, W. Xiang, and Y. Q. Zhou, Heterogeneous vehicular networking: A survey on architecture, challenges, and solutions, IEEE Commun. Surv. Tutor., vol. 17, no. 4, pp. 2377-2396, 2015.
[26]
X. Z. Wu, S. Subramanian, R. Guha, R. G. White, J. Y. Li, K. W. Lu, A. Bucceri, and T. Zhang, Vehicular communications using DSRC: Challenges, enhancements, and evolution, IEEE J. Sel. Areas Commun., vol. 31, no. 9, pp. 399-408, 2013.
[27]
J. Gozalvez, M. Sepulcre, and R. Bauza, IEEE 802.11p vehicle to infrastructure communications in urban environments, IEEE Commun. Mag., vol. 50, no. 5, pp. 176-183, 2012.
[28]
S. H. Sun, J. L. Hu, Y. Peng, X. M. Pan, L. Zhao, and J. Y. Fang, Support for vehicle-to-everything services based on LTE, IEEE Wirel. Commun., vol. 23, no. 3, pp. 4-8, 2016.
[29]
Release 14 Description; Summary of Rel-14 Work Items (Release 14), 3GPP, 2018.
[30]
Release 15 Description; Summary of Rel-15 Work Items (Release 15), 3GPP, 2019.
[31]
S. Z. Chen, J. L. Hu, Y. Shi, and L. Zhao, LTE-V: A TD-LTE-based V2X solution for future vehicular network, IEEE Internet Things J., vol. 3, no. 6, pp. 997-1005, 2016.
[32]
Release 16 Description; Summary of Rel-16 Work Items (Release 16), 3GPP, 2020.
[33]
M. Wang, Q. H. Shen, R. Zhang, H. Liang, and X. M. Shen, Vehicle-density-based adaptive MAC for high throughput in drive-thru networks, IEEE Internet Things J., vol. 1, no. 6, pp. 533-543, 2014.
[34]
H. B. Zhou, B. Liu, F. Hou, T. H. Luan, N. Zhang, L. Gui, Q. Yu, and X. S. Shen, Spatial coordinated medium sharing: Optimal access control management in drive-thru internet, IEEE Trans. Intell. Transp. Syst., vol. 16, no. 5, pp. 2673-2686, 2015.
[35]
H. B. Zhou, N. Cheng, N. Lu, L. Gui, D. Y. Zhang, Q. Yu, F. Bai, and X. S. Shen, WhiteFi infostation: Engineering vehicular media streaming with geolocation database, IEEE J. Sel. Areas Commun., vol. 34, no. 8, pp. 2260-2274, 2016.
[36]
F. X. Tang, Y. Kawamoto, N. Kato, and J. J. Liu, Future intelligent and secure vehicular network toward 6G: Machine-learning approaches, Proc. IEEE, vol. 108, no. 2, pp. 292-307, 2020.
[37]
F. A. Silva, A. Boukerche, T. R. M. B. Silva, E. Cerqueira, L. B. Ruiz, and A. A. F. Loureiro, Information-driven software-defined vehicular networks: Adapting flexible architecture to various scenarios, IEEE Veh. Technol. Mag., vol. 14, no. 1, pp. 98-107, 2019.
[38]
M. A. Salahuddin, A. Al-Fuqaha, and M. Guizani, Software-defined networking for RSU clouds in support of the internet of vehicles, IEEE Internet Things J., vol. 2, no. 2, pp. 133-144, 2015.
[39]
Network Functions Virtualisation (NFV); Architectural Framework, ETSI GS NFV 002, V1.2.1, 2014.
[40]
R. Mijumbi, J. Serrat, J. L. Gorricho, N. Bouten, F. De Turck, and R. Boutaba, Network function virtualization: State-of-the-art and research challenges, IEEE Commun. Surv. Tutor., vol. 18, no. 1, pp. 236-262, 2016.
[41]
C. Qiu, H. P. Yao, F. R. Yu, F. M. Xu, and C. L. Zhao, Deep q-learning aided networking, caching, and computing resources allocation in software-defined satellite-terrestrial networks, IEEE Trans. Veh. Technol., vol. 68, no. 6, pp. 5871-5883, 2019.
[42]
J. Du, C. X. Jiang, H. J. Zhang, Y. Ren, and M. Guizani, Auction design and analysis for SDN-based traffic offloading in hybrid satellite-terrestrial networks, IEEE J. Sel. Areas Commun., vol. 36, no. 10, pp. 2202-2217, 2018.
[43]
J. F. Qiu, D. Grace, G. R. Ding, M. D. Zakaria, and Q. H. Wu, Air-ground heterogeneous networks for 5G and beyond via integrating high and low altitude platforms, IEEE Wirel. Commun., vol. 26, no. 6, pp. 140-148, 2019.
[44]
N. Zhang, S. Zhang, P. Yang, O. Alhussein, W. H. Zhuang, and X. S. Shen, Software defined space-air-ground integrated vehicular networks: Challenges and solutions, IEEE Commun. Mag., vol. 55, no. 7, pp. 101-109, 2017.
[45]
G. C. Wang, S. Zhou, S. Zhang, Z. S. Niu, and X. M. Shen, SFC-based service provisioning for reconfigurable space-air-ground integrated networks, IEEE J. Sel. Areas Commun., vol. 38, no. 7, pp. 1478-1489, 2020.
[46]
G. Mirjalily and Z. Q. Luo, Optimal network function virtualization and service function chaining: A survey, Chin. J. Electron., vol. 27, no. 4, pp. 704-717, 2018.
[47]
J. G. Herrera and J. F. Botero, Resource allocation in NFV: A comprehensive survey, IEEE Trans. Netw. Serv. Manag., vol. 13, no. 3, pp. 518-532, 2016.
[48]
F. Bari, S. R. Chowdhury, R. Ahmed, R. Boutaba, and O. C. M. B. Duarte, Orchestrating virtualized network functions, IEEE Trans. Netw. Serv. Manag., vol. 13, no. 4, pp. 725-739, 2016.
[49]
L. H. Wang, Z. M. Lu, X. M. Wen, R. Knopp, and R. Gupta, Joint optimization of service function chaining and resource allocation in network function virtualization, IEEE Access, vol. 4, pp. 8084-8094, 2016.
[50]
M. T. Beck and J. F. Botero, Coordinated allocation of service function chains, in Proc. 2015 IEEE Global Communications Conf., San Diego, CA, USA, 2015.
[51]
L. Qu, C. Assi, K. Shaban, and M. J. Khabbaz, A reliability-aware network service chain provisioning with delay guarantees in NFV-enabled enterprise datacenter networks, IEEE Trans. Netw. Serv. Manag., vol. 14, no. 3, pp. 554-568, 2017.
[52]
D. F. Li, P. L. Hong, K. P. Xue, and J. N. Pei, Virtual network function placement considering resource optimization and SFC requests in cloud datacenter, IEEE Trans. Parallel Distrib. Syst., vol. 29, no. 7, pp. 1664-1677, 2018.
[53]
Service Function Chaining (SFC) Architecture, IETF RFC 7665, 2015.
[54]
G. C. Wang, S. Zhou, Z. S. Niu, S. Zhang, and X. M. Shen, Service function chain planning with resource balancing in space-air-ground integrated networks, in Proc. 2019 IEEE Global Communications Conf., Waikoloa, HI, USA, 2019.
[55]
S. Zhou, G. C. Wang, S. Zhang, Z. S. Niu, and X. S. Shen, Bidirectional mission offloading for agile space-air-ground integrated networks, IEEE Wirel. Commun., vol. 26, no. 2, pp. 38-45, 2019.
[56]
G. C. Wang, S. Zhou, and Z. S. Niu, Radio resource allocation for bidirectional offloading in space-air-ground integrated vehicular network, J. Commun. Inf. Netw., vol. 4, no. 4, pp. 24-31, 2019.
[57]
W. Li, Formation-preserving properties of cooperative kinematic agents with or without external influence of target attraction, IEEE Trans. Autom. Control, vol. 63, no. 6, pp. 1737-1744, 2018.
[58]
Y. P. Liu and Y. Shen, UAV-aided high-accuracy relative localization of ground vehicles, in Proc. 2018 IEEE Int. Conf. Communications, Kansas City, MO, USA, 2018.
[59]
J. N. Ash and R. L. Moses, On the relative and absolute positioning errors in self-localization systems, IEEE Trans. Signal Process, vol. 56, no. 11, pp. 5668-5679, 2008.
[60]
X. Zheng, S. Zhou, and Z. S. Niu, Context-aware information lapse for timely status updates in remote control systems, in Proc. 2019 IEEE Global Communications Conf., Waikoloa, HI, USA, 2019.
[61]
X. Zheng, S. Zhou, and Z. S. Niu, Beyond age: Urgency of information for timeliness guarantee in status update systems, in Proc. 2020 2nd 6G Wireless Summit, Levi, Finland, 2020.
[62]
S. Kaul, M. Gruteser, V. Rai, and J. Kenney, Minimizing age of information in vehicular networks, in Proc. 2011 8th Ann. IEEE Communications Society Conf. on Sensor, Mesh and Ad Hoc Communications and Networks, Salt Lake City, UT, USA, 2011.
[63]
X. Yu, H. Y. Xiao, S. Y. Wang, and Y. J. Li, An adaptive back-off scheme based on improved markov model for vehicular ad hoc networks, IEEE Access, vol. 6, pp. 67 373-67 384, 2018.
[64]
Z. P. Lin and Y. L. Tang, Distributed multi-channel MAC Protocol for VANET: An adaptive frame structure scheme, IEEE Access, vol. 7, pp. 12 868-12 878, 2019.
[65]
Y. Kim, M. Lee, and T. J. Lee, Coordinated multichannel MAC protocol for vehicular ad hoc networks, IEEE Trans. Veh. Technol., vol. 65, no. 8, pp. 6508-6517, 2016.
[66]
K. A. Hafeez, L. Zhao, J. W. Mark, X. M. Shen, and Z. S. Niu, Distributed multichannel and mobility-aware cluster-based MAC protocol for vehicular ad hoc networks, IEEE Trans. Veh. Technol., vol. 62, no. 8, pp. 3886-3902, 2013.
[67]
G. Y. Luo, J. L. Li, L. Zhang, Q. Yuan, Z. H. Liu, and F. C. Yang, sdnMAC: A software-defined network inspired MAC protocol for cooperative safety in VANETs, IEEE Trans. Intell. Transp. Syst., vol. 19, no. 6, pp. 2011-2024, 2018.
[68]
R. Molina-Masegosa and J. Gozalvez, LTE-V for sidelink 5G V2X vehicular communications: A new 5G technology for short-range vehicle-to-everything communications, IEEE Veh. Technol. Mag., vol. 12, no. 4, pp. 30-39, 2017.
[69]
B. Karp and H. T. Kung, GPSR: Greedy perimeter stateless routing for wireless networks, in Proc. 6th Ann. Int. Conf. on Mobile Computing and Networking, Boston, MA, USA, 2000.
[70]
C. E. Perkins and P. Bhagwat, Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers, ACM SIGCOMM Comput. Commun. Rev., vol. 24, no. 4, pp. 234-244, 1994
[71]
J. Bernsen and D. Manivannan, Greedy routing protocols for vehicular ad hoc networks, in Proc. 2008 Int. Wireless Communications and Mobile Computing Conf., Crete Island, Greece, 2008.
[72]
W. S. Shi, H. B. Zhou, J. L. Li, W. C. Xu, N. Zhang, and X. M. Shen, Drone assisted vehicular networks: Architecture, challenges and opportunities, IEEE Netw., vol. 32, no. 3, pp. 130-137, 2018.
[73]
Y. L. Sun, L. Xu, and Y. L. Tang, Cooperative downloading in vehicular networks: A graph-based approach, in Proc. 2018 IEEE 87th Vehicular Technology Conf., Porto, Portugal, 2018.
[74]
Y. F. Chen, W. Feng, and G. Zheng, Optimum placement of UAV as relays, IEEE Commun. Lett., vol. 22, no, 2, pp. 248-251, 2018.
[75]
Y. F. Chen, N. Zhao, Z. G. Ding, and M. S. Alouini, Multiple UAVs as relays: Multi-hop single link versus multiple dual-hop links, IEEE Trans. Wirel. Commun., vol. 17, no. 9, pp. 6348-6359, 2018.
[76]
L. Xiao, X. Z. Lu, D. J. Xu, Y. L. Tang, L. Wang, and W. H. Zhuang, UAV relay in VANETs against smart jamming with reinforcement learning, IEEE Trans. Veh. Technol., vol. 67, no. 5, pp. 4087-4097, 2018.
[77]
F. Z. Qu, Z. H. Wu, F. Y. Wang, and W. Cho, A security and privacy review of VANETs, IEEE Trans. Intell. Transp. Syst., vol. 16, no. 6, pp. 2985-2996, 2015.
[78]
J. Li, H. Lu, and M. Guizani, ACPN: A novel authentication framework with conditional privacy-preservation and non-repudiation for VANETs, IEEE Trans. Parallel Distrib. Syst., vol. 26, no. 4, pp. 938-948, 2015.
[79]
H. Han, F. Y. Xu, C. C. Tan, Y. F. Zhang, and Q. Li, VR-defender: Self-defense against vehicular rogue APs for drive-thru internet, IEEE Trans. Veh. Technol., vol. 63, no. 8, pp. 3927-3934, 2014.
[80]
Z. Y. Shi, M. M. Huang, C. D. Zhao, L. F. Huang, X. J. Du, and Y. F. Zhao, Detection of LSSUAV using hash fingerprint based SVDD, in Proc. 2017 IEEE Int. Conf. on Communications, Paris, France, 2017.
[81]
C. D. Zhao, M. M. Huang, L. F. Huang, X. J. Du, and M. Guizani, A robust authentication scheme based on physical-layer phase noise fingerprint for emerging wireless networks, Comput. Netw., vol. 128, pp. 164-171, 2017.
[82]
X. Z. Lu, L. Xiao, T. W. Xu, Y. F. Zhao, Y. L. Tang, and W. H. Zhuang, Reinforcement learning based PHY authentication for VANETs, IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 3068-3079, 2020
[83]
C. D. Zhao, M. X. Shi, M. M. Huang, and X. J. Du, Authentication scheme based on hashchain for space-air-ground integrated network, in Proc. 2019 IEEE Int. Conf. on Communication, Shanghai, China, 2019.
[84]
L. Wang, H. Q. Wu, Y. N. Ding, W. Chen, and H. V. Poor, Hypergraph-based wireless distributed storage optimization for cellular D2D underlays, IEEE J. Sel. Areas Commun., vol. 34, no. 10, pp. 2650-2666, 2016.
[85]
C. Celes, F. A. Silva, A. Boukerche, R. M. de Castro Andrade, and A. A. F. Loureiro, Improving VANET simulation with calibrated vehicular mobility traces, IEEE Trans. Mobile Comput., vol. 16, no. 12, pp. 3376-3389, 2017.
[86]
Y. M. Miao, W. Li, D. X. Tian, M. S. Hossain, and M. F. Alhamid, Narrowband internet of things: simulation and modeling, IEEE Internet Things J., vol. 5, no. 4, pp. 2304-2314, 2018.
[87]
A. Ranjan, B. Panigrahi, H. K. Rath, P. Misra, and A. Simha, LTE-CAS: LTE-based criticality aware scheduling for UAV assisted emergency response, in Proc. IEEE Conf. on Computer Communications Workshops, Honolulu, HI, USA, 2018, pp. 894-899.
[88]
Y. Kawamoto, H. Nishiyama, N. Kato, and N. Kadowaki, A traffic distribution technique to minimize packet delivery delay in multilayered satellite networks, IEEE Trans. Veh. Technol., vol. 62, no. 7, pp. 3315-3324, 2013.
[89]
X. H. Jia, T. Lv, F. He, and H. J. Huang, Collaborative data downloading by using inter-satellite links in LEO satellite networks, IEEE Trans. Wirel. Commun., vol. 16, no. 3, pp. 1523-1532, 2017.
[90]
N. Zangar and S. Hendaoui, Leveraging multiuser diversity for adaptive hybrid satellite-LTE downlink scheduler (H-MUDoS) in emerging 5G-satellite network, Int. J. Satell. Commun. Netw., vol. 35, no. 1, pp. 67-88, 2017.
[91]
N. Cheng, W. Quan, W. S. Shi, H. Q. Wu, Q. Ye, H. B. Zhou, W. H. Zhuang, X. M. Shen, and B. Bai, A comprehensive simulation platform for space-air-ground integrated network, IEEE Wirel. Commun., vol. 27, no. 1, pp. 178-185, 2020.
[92]
N. Cheng, F. Lyu, W. Quan, C. H. Zhou, H. L. He, W. Shi, and X. M. Shen, Space/aerial-assisted computing offloading for iot applications: A learning-based approach, IEEE J. Sel. Areas Commun., vol. 37, no. 5, pp. 1117-1129, 2019.
[93]
S. Zhang, W. Quan, J. L. Li, W. Shi, P. Yang, and X. M. Shen, Air-ground integrated vehicular network slicing with content pushing and caching, IEEE J. Sel. Areas Commun., vol. 36, no. 9, pp. 2114-2127, 2018.
[94]
W. Shi, J. L. Li, N. Cheng, F. Lyu, S. Zhang, H. B. Zhou, and X. M. Shen, Multi-drone 3-d trajectory planning and scheduling in drone-assisted radio access networks, IEEE Trans. Veh. Technol., vol. 68, no. 8, pp. 8145-8158, 2019.
Intelligent and Converged Networks
Pages 142-169
Cite this article:
Niu Z, Shen XS, Zhang Q, et al. Space-air-ground integrated vehicular network for connected and automated vehicles: Challenges and solutions. Intelligent and Converged Networks, 2020, 1(2): 142-169. https://doi.org/10.23919/ICN.2020.0009

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Received: 14 May 2020
Revised: 01 August 2020
Accepted: 07 September 2020
Published: 01 December 2020
© All articles included in the journal are copyrighted to the ITU and TUP 2020

© All articles included in the journal are copyrighted to the ITU and TUP. This work is available under the CC BY-NC-ND 3.0 IGO license: https://creativecommons.org/licenses/by-nc-nd/3.0/igo/.

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