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

doi:10.23919/ICN.2020.0009

2020, Vol. 1

Issue (2): 142-169 doi: 10.23919/ICN.2020.0009

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

Zhisheng Niu^*(

),Xuemin S. Shen(

),Qinyu Zhang(

),Yuliang Tang(

)

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.

Key words： space information network vehicular network space-air-ground integrated network autonomous driving context information Internet-of-Vehicles

Received: 14 May 2020 Online: 19 August 2021

Fund: National Natural Science Foundation of China(91638204)

Corresponding Authors: Zhisheng Niu E-mail: niuzhs@tsinghua.edu.cn;sshen@uwaterloo.ca;zqy@hit.edu.cn;tyl@xmu.edu.cn

About author: Zhisheng Niu graduated from Northern Jiaotong University (currently Beijing Jiaotong University), China in 1985, and got the MEng and PhD degrees from Toyohashi University of Technology, Japan, in 1989 and 1992, respectively. During 1992-1994, he worked for Fujitsu Laboratories Ltd., Japan and in 1994 joined with Tsinghua University, Beijing, China, where he is now a professor at the Department of Electronic Engineering. His major research interests include queueing theory, traffic engineering, mobile Internet, radio resource management of wireless networks, and green communication and networks. He is a fellow of IEEE and IEICE. He is the editor-in-chief of IEEE Transactions on Green Communications and Networking (TGCN).|Xuemin S. Shen received the PhD degree in electrical engineering from Rutgers University, New Brunswick, NJ, USA in 1990. He is currently a professor of University of Waterloo. His research focuses on network resource management, wireless network security, Internet of Things, 5G and beyond, and vehicular ad hoc and sensor networks. He is a fellow of IEEE, Engineering Institute of Canada, Canadian Academy of Engineering, and Royal Society of Canada. He is the editor-in-chief of IEEE Internet of Things.|Qinyu Zhang received the bachelor degree in communication engineering from the Harbin Institute of Technology (HIT), Harbin, China in 1994, and the PhD degree in biomedical and electrical engineering from the University of Tokushima, Tokushima, Japan in 2003. Since 2005, he is a full professor and serves as the dean of the School of Electronic and Information Engineering, Harbin Institute of Technology. His research interests include aerospace communications and networks, wireless communications and networks, cognitive radios, signal processing, and biomedical engineering.|Yuliang Tang received the MS degree from Beijing University of Posts and Telecommunications, China in 1996. He received the PhD degree in information and communication engineering from Xiamen University, China in 2009, where he is a professor in the Department of Information and Communication Engineering, Xiamen University. He has published more than 90 papers in journals and international conferences, and has been granted over 20 patents in his research areas. His research interests include wireless communication, 5G and beyond, and vehicular ad-hoc networks.


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Cite this article:

Zhisheng Niu,Xuemin S. Shen,Qinyu Zhang,Yuliang Tang. Space-air-ground integrated vehicular network for connected and automated vehicles: Challenges and solutions. , 2020, 1: 142-169.

URL:

http://icn.tsinghuajournals.com/10.23919/ICN.2020.0009 OR http://icn.tsinghuajournals.com/Y2020/V1/I2/142

Table 1 Comparison of different network segments.

Fig. 1 SFC-based service management and reconfiguration framework for SAGiven.

Fig. 2 Bidirectional mission offloading in SAGvien.

Fig. 3 Integration of radar and communication systems.

Fig. 4 Range and velocity estimations in practical RadCom systems.

Fig. 5 An illustration of proposed relative vehicular positioning architecture in SAGiven.

𝒑^: estimated position of UAV).
">

Fig. 6 An illustration of absolute and relative position errors (p: real position of UAV; $𝒑^$ : estimated position of UAV).

Fig. 7 (a) Three-dimensional Cramér-Rao Low Bound (CRLB) of vehicle positioning error with respect to the number of base stations. (b) Average root Squared Position Error Bound (SPEB) with respect to the energy of each link.

𝝀 represents the wavelength of signal.
">

Fig. 8 (a) Average Orientation Error Bound (OEB) with respect to the number of vehicles. M represents the number of antema elements. (b) Average Position Error Bound (PEB) with respect to the number of array elements. d represents the distance of antenna arrays and $𝝀$ represents the wavelength of signal.

Fig. 9 (a) Two-dimensional CRLB of vehicle positioning error with respect to the clock noise standard deviation of anchors. (b) Failure probability of the network with respect to different NLOS probabilities. Here, RMSE represents root mean square error.

𝟏 and the UAV.
">

Fig. 10 Illustration of a UAV-aided VANET, in which the OBU moving with speed v sends a message to a server via the serving RSU $𝟏$ and the UAV.

Fig. 11 Illustration of the Reinforcement Learning (RL) based UAV relay anti-jamming scheme.

Fig. 12 Experimental settings in which the mobile device Bob and its serving edge Alice in vehicle resisted a rogue edge Eve outside the vehicle, and both Alice and Eve sent packets with Alice’s identity.

Fig. 13 Authentication performance of the physical-layer authentication scheme against a smart rogue edge attacker.

Fig. 14 Overview of SAGiven simulation platform. Here, API represents application programming interface.

Fig. 15 Implementation of SAGiven simulation platform.

Layer	Parameter	Value
Simulation scenario	Number of vehicular users	100
	Number of UAVs	3
	Number of LTE BSs	1
	Number of satellites	39
	Simulation time (min)	10
	Data packet length (byte)	1024
	App data rate (Mbps)	8.192
UAV to ground communication	802.11 protocol	802.11 p
	Propagation model	Log distance
	Frequency (MHz)	5.9
	Bandwidth (MHz)	10
	Transmission power (dBm)	40
	Antenna gains (transmit & receive) (dB)	1
	Carrier sensing threshold (dBm)	-96
	Receiver noise figure (dB)	7
	DCF inter-frame space (DIFS) ( $μ$ s)	58
	Short inter-frame space (SIFS) ( $μ$ s)	32
	CWmin (time slot)	15
	CWmax (time slot)	1023
LTE to ground user communication	Propagation model	Log distance
	Frequency (MHz)	850
	Bandwidth (resource block)	25
	Transmission power (dBm)	30
	Antenna gains (transmit & receive) (dB)	0
	Receive threshold for PSS on RSRQ (dB)	-1000
	Receiver noise figure (dB)	9
Satellite to ground communication	Satellite type	LEO
	Orbit height (km)	717-787
	Frequency band	L-band

Table 2 SAGiven simulation platform parameters.

Fig. 16 Demonstration of SAGiven simulation platform.

Fig. 17 RAT selection results.

Fig. 18 Computing offloading results. Here, AC represents actor-critic.


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