University of Oulu

L. Su et al., "Content Distribution Based on Joint V2I and V2V Scheduling in mmWave Vehicular Networks," in IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 3201-3213, March 2022, doi: 10.1109/TVT.2022.3141415

Content distribution based on joint V2I and V2V scheduling in mmWave vehicular networks

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Author: Su, Lan1,2; Niu, Yong1,3; Han, Zhu4,5;
Organizations: 1State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
2Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing 100044, China
3National Mobile Communications Research Laboratory, Southeast University, Nanjing 211189, China
4Department of Electrical and Computer Engineering,University of Houston, Houston, TX 77004 USA
5Department of Computer Science and Engineering, Kyung Hee University, Seoul 446-701, South Korea
6School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
7Department of Computer Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway
8Center of Ubiquitous Computing, University of Oulu, 90570 Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.6 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-08-30


With the explosive growth of vehicle applications, vehicular networks based on millimeter wave (mmWave) bands have attracted interests from both academia and industry. mmWave communications are able to utilize the huge available bandwidth to provide multiple Gbps transmission rates among vehicles. In this paper, we address the content distribution scheduling problem in mmWave vehicular networks. It has been challenging for all vehicles in the same network to complete content downloading due to the limited communication resources of roadside units (RSUs) and the high mobility of vehicles. We propose a joint vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scheduling scheme to minimize the total number of content distribution time slots from a global optimization perspective. In the V2I phase, the RSU serially transmits integrity content to vehicles, which are selected according to the vehicular network topology and transmission scheduling scheme. In the V2V phase, full-duplex communications and concurrent transmissions are exploited to achieve content sharing between vehicles and improve transmission efficiency. Performance evaluations demonstrate that our proposed scheme reduces the number of time slots and significantly improves system throughput when compared with other schemes, especially under large-size file transfers and a large number of vehicles.

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Series: IEEE transactions on vehicular technology
ISSN: 0018-9545
ISSN-E: 1939-9359
ISSN-L: 0018-9545
Volume: 71
Issue: 3
Pages: 3201 - 3213
DOI: 10.1109/tvt.2022.3141415
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the National Key R&D Program of China under Grant 2020YFB1806903, in part by the National Natural Science Foundation of China under Grants 61801016, 61725101, 61961130391, and U1834210, in part by the National Key Research and Development Program under Grant 2021YFB2900301, in part by the State Key Laboratory of Rail Traffic Control and Safety under Contract Number RCS2021ZT009, in part by the Beijing Jiaotong University, in part by the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University under Grant 2021D09, in part by the Fundamental Research Funds for the Central Universities, China, under Grant 2020JBZD005, in part by the Frontiers Science Center for Smart High-speed Railway System, and in part by the Fundamental Research Funds for the Central Universities under Grant 2020JBM089, in part by the Project of China Shenhua under Grant GJNY-20-01-1. This work was partially supported by NSF under Grants CNS-2128368 and CNS-2107216, Toyota and Amazon.
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