Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

By Fengxiao Tang, Yuichi Kawamoto, Nei Kato, Jiajia Liu. Proceedings of the IEEE, 2020

499 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f938a29f108fe901ba4bc9162bbece909e8940f1

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Citations in the corpus

Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey (2020, background): “The feasibility and benefits of applying machine learning technologies to communication systems have been well demonstrated, for example, fast beamforming design via deep learning [206], behavioral modeling and linearization of wideband RF power amplifiers in 5G system [207], vehicular network model”
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The Road Towards 6G: A Comprehensive Survey (2021, background): “Then, a survey on various ML technologies applied for communication, networking, and security aspects of vehicular networks, and a vision of the ways toward an intelligent 6G vehicular network were provided in [21].”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, background): “A general network architecture for various application scenarios (such as IoT networks [87] and vehicular networks [88]) in 6G networks is illustrated in Fig.”
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial (2020, background): “In [15], the authors reviewed the applications of ML, particularly DRL, in vehicular networks with a discussion on the role of AI in the future 6G vehicular networks.”
Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges (2021, background): “Internet [2], [3], and autonomous driving [4], [5].”
6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities (2020, background): “In view of this background, ML has become increasingly indispensable and instrumental towards highly autonomous and intelligent operation of tomorrow’s 6G vehicular networks [129].”
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