The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Vehicular Communications: A Physical Layer Perspective
Open accessAbstract
Vehicular communications have attracted more and more attention recently from both industry and academia due to their strong potential to enhance road safety, improve traffic efficiency, and provide rich on-board information and entertainment services. In this paper, we discuss fundamental physical layer issues that enable efficient vehicular communications and present a comprehensive overview of the state-of-the-art research. We first introduce vehicular channel characteristics and modeling, which are the key underlying features differentiating vehicular communications from other types of wireless systems. We then present schemes to estimate the time-varying vehicular channels and various modulation techniques to deal with high-mobility channels. After reviewing resource allocation for vehicular communications, we discuss the potential to enable vehicular communications over the millimeter wave bands. Finally, we identify the challenges and opportunities associated with vehicular communications.
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Cited by
Deep Reinforcement Learning Based Resource Allocation for V2V Communications
Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2897134 · Cited by 870
Self-citationSpectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Liang, L., Ye, H., & Li, G. Y. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933962 · Cited by 559
Self-citationMobile Edge Intelligence and Computing for the Internet of Vehicles
Zhang, J., & Letaief, K. B. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2947490 · Cited by 537
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Self-citationSurvey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs)
Mchergui, A., Moulahi, T., & Zeadally, S. (2022) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100403 · Cited by 165
Metadata
- Authors
- Liang, L., Peng, H., Li, G. Y., & Shen, X.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2017-12
- Volume
- 66
- Issue
- 12
- Pages
- 10647-10659
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2609289242
- Cited by
- 267
- References
- 68
- FWCI
- 12.08
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- Funders
- Research Gift from Intel Corporation
- National Science Foundation (1405116, 1443894)
- National Natural Science Foundation of China (91638204)
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