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

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...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the IEEE 2020-02, Vol.108 (2), p.292-307
Main Authors: Tang, Fengxiao, Kawamoto, Yuichi, Kato, Nei, Liu, Jiajia
Format: Article
Language:English
Subjects:
6G mobile communication
Ad hoc networks
Array signal processing
Artificial intelligence
Communication
deep learning
Human communication
intelligent radio (IR)
intelligentization
Internet of Vehicles (IoV)
Machine learning
machine learning (ML)
Network reliability
OFDM
resource allocation
Resource management
routing
Security
space–air–ground
traffic control
Vehicle dynamics
Vehicle-to-everything
vehicle-to-everything (V2X)
vehicle-to-vehicle (V2V)
Vehicles
Vehicular ad hoc networks
vehicular network
Wireless communications
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2019.2954595