期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 2, 页码 701-721出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3019322
关键词
Resource management; Wireless communication; Machine learning; Long Term Evolution; Intelligent transportation systems; Quality of service; Technological innovation; Intelligent transportation system; vehicular network; autonomous driving; DSRC V2X; cellular V2X; resource allocation; network slicing; machine learning
资金
- Science Foundation Ireland (SFI)
- European Regional Development Fund [13/RC/2077]
- European Union's Horizon 2020 Research and Innovation Programme through the EDGE CO-FUND Marie Sklodowska Curie Grant [713567]
- U.K. Engineering and Physical Sciences Research Council [EP/P03456X/1]
- European Union [857034]
This paper presents a comprehensive survey on resource allocation schemes for vehicular networks, discussing the challenges and opportunities for resource allocations in modern vehicular networks, and outlining some promising future research directions.
Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory Quality of Service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as erroneous and congested wireless channels (due to high mobility or uncoordinated channel-access), increasingly fragmented and congested spectrum, hardware imperfections, and anticipated growth of vehicular communication devices. Therefore, it will be critical to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation schemes for the two dominant vehicular network technologies, e.g. Dedicated Short Range Communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.
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