Optimization of resource allocation for V2X security communication based on multi-agent reinforcement learning

Ji, B, Dong, B, Li, D, Wang, Y, Yang, L, Tsimenidis, C ORCID: https://orcid.org/0000-0003-2247-3397 and Menon, VG, 2023. Optimization of resource allocation for V2X security communication based on multi-agent reinforcement learning. IEEE Transactions on Vehicular Technology. ISSN 0018-9545

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Abstract

In order to address the data security and communication efficiency of vehicles during high-speed mobile communication, this paper investigates the problem of secure in-vehicle communication resource allocation based on slow-variable large-scale fading channel information, to meet the quality of service requirements of vehicular communication, i.e., to ensure the reliability of V2V communication and the time delay while maximizing the transmission rate of the cellular link. And an eavesdropping model is introduced to ensure the secure delivery of link information. Considering that the high mobility of vehicles causes rapid channel changes, we model the problem as a Markov decision process and propose a resource allocation optimization framework based on the Multi-Agent Reinforcement Learning Algorithm (MARL-DDQN), in which a large-scale neural network model is built to train vehicular to learn the optimal resource allocation strategy for optimal communication performance and security performance. Simulation results show that the load successful delivery rate and confidentiality performance of the vehicular communication network are effectively improved compared to the baseline and MADDPG strategies while ensuring link security. This study provides useful references and practical value for the optimization of secure communication resource allocation in vehicular networking.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Vehicular Technology
Creators:	Ji, B., Dong, B., Li, D., Wang, Y., Yang, L., Tsimenidis, C. and Menon, V.G.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	2023
ISSN:	0018-9545
Identifiers:	Number Type 10.1109/tvt.2023.3340424 DOI 1846800 Other
Rights:	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	09 Jan 2024 13:36
Last Modified:	09 Jan 2024 13:36
URI:	https://irep.ntu.ac.uk/id/eprint/50639

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