Journal Article10.1109/JPROC.2019.2954595
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches
Fengxiao Tang,Yuichi Kawamoto,Nei Kato,Jiajia Liu +3 more
- 01 Feb 2020
- Vol. 108, Iss: 2, pp 292-307
606
TL;DR: A survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.
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Abstract: As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.
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Citations
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Security of 6G-Enabled Vehicle-to-Everything Communication in Emerging Federated Learning and Blockchain Technologies
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TL;DR: The security challenges associated with and solutions for Vehicle-to-Everything (V2X) communication in the upcoming 6G era are explored. The paper discusses the architecture and standards utilized in 6G-enabled V2X communications and analyzes the impact of emerging technologies like Blockchain and Federated Learning (FL) on V2X security. A Blockchain-enabled FL-based generic security architecture for V2X communication in 6G networks is proposed.
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Deep Reinforcement Learning Based Joint Beam Allocation and Relay Selection in mmWave Vehicular Networks
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