Integrated sensing and communications enabling Internet of Vehicles: application scenarios and key technologies

doi:10.12267/j.issn.2096-5931.2024.03.002

Abstract

Abstract:

Building an efficient, secure, and intelligent Internet of Vehicles (IoV) system is a key step towards realizing smart transportation in the future. The limited frequency band resources and highly redundant hardware design also have some impacts on the deepening process of IoV system. With the development of 6G communication technology, the application of integrated sensing and communications (ISAC) technology has become a new research direction for the construction of IoV system. By incorporating the ISAC technology, the hardware design in IoV system can be simplified, while realizing the intelligent rationing and efficient use of resources, which can actively promote the development of IoV system. Firstly, the main application scenarios of ISAC technology in IoV system are listed. Then, the infrastructure and key technologies of IoV enabled by ISAC are introduced. Finally, the challenges faced in the construction of IoV system enabled by ISAC are summarized and future development directions are proposed.

Key words: Internet of Vehicles, integrated sensing and communications, application scenario, intelligence

CLC Number:

U495

LIN Fangyuan, MA Lidong, XU Chen, ZHU Konglin. Integrated sensing and communications enabling Internet of Vehicles: application scenarios and key technologies[J]. Information and Communications Technology and Policy, 2024, 50(3): 9-17.

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http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2024.03.002

http://ictp.caict.ac.cn/EN/Y2024/V50/I3/9

Figures/Tables 3

References 19

[1]	程翔, 张浩天, 杨宗辉, 等. 车联网通信感知一体化研究:现状与发展趋势. 通信学报[J], 2022, 43(8): 188-202.
[2]	LIU F, YUAN W J, MASOUROS C, et al. Radar-assisted predictive beamforming for vehicular links: communication served by sensing[J]. IEEE Transactions on Wireless Communications, 2020, 19(11):7704-7719. doi: 10.1109/TWC.7693 URL
[3]	YING Z, CUI Y, MU J, et al. Particle filter based predictive beamforming for integrated vehicle sensing and communication[C]// 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). IEEE, 2021: 1-5.
[4]	ZHAO Y, XU X, ZENG Y, et al. Sensing-assisted predictive beamforming with NLoS identification[C]// ICC 2023 - IEEE International Conference on Communications. IEEE, 2023: 6455-6460.
[5]	MU J, GONG Y, ZHANG F, et al. Integrated sensing and communication-enabled predictive beamforming with deep learning in vehicular networks[J]. IEEE Communications Letters, 2021, 25(10):3301-3304. doi: 10.1109/LCOMM.2021.3098748 URL
[6]	CONG D, GUO S, DANG S, et al. Vehicular behavior-aware beamforming design for integrated sensing and communication systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6): 5923-5935. doi: 10.1109/TITS.2023.3251303 URL
[7]	CHEN K, QI C, DOBRE O A. Simultaneous Beam Training and Target Sensing for RIS-Aided Integrated Sensing and Communication[C]// ICC 2023 - IEEE International Conference on Communications. IEEE, 2023: 4242-4248.
[8]	LUAN M, WANG B, CHANG Z, et al. Robust beamforming design for RIS-aided integrated sensing and communication system[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6): 6227-6243. doi: 10.1109/TITS.2023.3248145 URL
[9]	WANG Y, ZHANG W, LIU C, et al. Reconfigurable intelligent surface for NLOS integrated sensing and communications[C]// 2022 IEEE/CIC International Conference on Communications in China (ICCC), 2022: 708-712.
[10]	CHEN Z, CHEN P, GUO Z, et al. A RIS-based vehicle DOA estimation method with integrated sensing and communication system[J]. IEEE Transactions on Intelligent Transportation Systems.
[11]	BARREMECHEA P, ELFERINK F, JANSSEN J. FMCW radar with broad-band communication capability[C]// European Radar Conference. IEEE, 2007: 130-133.
[12]	WINKLER V, DETLEFSEN J. Automotive 24 GHz pulse radar extended by a DQPSK communication channel[C]// European Radar Conference. IEEE, 2007: 138-141.
[13]	LINDENMEIER S, BOEHM K, LUY J F. A wireless data link for mobile applications[J]. IEEE Microwave and Wireless Components Letters, 2003, 13(8): 326-328. doi: 10.1109/LMWC.2003.815706 URL
[14]	YIN X, BAUWELINCK J, TORFS G, et al. Embedded ranging system in ISM band[J]. Electronics Letters, 2008, 44(17):1043-1045. doi: 10.1049/el:20081157 URL
[15]	ZHANG Q, SUN H, GAO X, et al. Time-division ISAC enabled connected automated vehicles cooperation algorithm design and performance evaluation[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(7): 2206-2218. doi: 10.1109/JSAC.2022.3155506 URL
[16]	GARMATYUK D, KAUFFMAN K. Radar and data communication fusion with UWB-OFDM software-defined system[C]// 2009 IEEE International Conference on Ultra-Wideband. IEEE, 2009: 454-458.
[17]	HIEU N Q, HOANG D T, LUONG N C, et al. iRDRC: an intelligent real-time dual-functional radar-communication system for automotive vehicles[J]. IEEE Wireless Communications Letters, 2020, 9(12): 2140-2143. doi: 10.1109/LWC.5962382 URL
[18]	LEE J, CHENG Y, NIYATO D, et al. Deep reinforcement learning for time allocation and directional transmission in joint radar-communication[C]// 2022 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2022: 2559-2564.
[19]	ZHANG Z, CHANG Q, YANG S, et al. Sensing-communication bandwidth allocation in vehicular links based on reinforcement learning[J]. IEEE Wireless Communications Letters, 2023, 12(1): 11-15. doi: 10.1109/LWC.2022.3214071 URL