Secure Transmission in Cellular V2X Communications Using Deep Q-Learning

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2022-10-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
17167-17176
Series
IEEE Transactions on Intelligent Transportation Systems, Volume 23, issue 10
Abstract
Cellular vehicle-to-everything (V2X) communication is emerging as a feasible and cost-effective solution to support applications such as vehicle platooning, blind spot detection, parking assistance, and traffic management. To support these features, an increasing number of sensors are being deployed along the road in the form of roadside objects. However, despite the hype surrounding cellular V2X networks, the practical realization of such networks is still hampered by under-developed physical security solutions. To solve the issue of wireless link security, we propose a deep Q-learning-based strategy to secure V2X links. Since one of the main responsibilities of the base station (BS) is to manage interference in the network, the link security is ensured without compromising the interference level in the network. The formulated problem considers both the power and interference constraints while maximizing the secrecy rate of the vehicles. Subsequently, we develop the reward function of the deep Q-learning network for performing efficient power allocation. The simulation results obtained demonstrate the effectiveness of our proposed learning approach. The results provided here will provide a strong basis for future research efforts in the domain of vehicular communications.
Description
Publisher Copyright: IEEE
Keywords
Deep Q-learning, Interference, interference management, physical layer security, Q-learning, Resource management, Security, Signal to noise ratio, V2X communications., Vehicle-to-everything, Wireless communication
Other note
Citation
Jameel, F, Javed, M A, Zeadally, S & Jantti, R 2022, ' Secure Transmission in Cellular V2X Communications Using Deep Q-Learning ', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 17167-17176 . https://doi.org/10.1109/TITS.2022.3165791