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Ju, Y., Gao, Z., Wang, H., Liu, L., Pei, Q., Dong, M., Mumtaz, S. 
ORCID: 0000-0001-6364-6149 and Leung, V.C.M.,
2024.
Energy-efficient cooperative secure communications in mmWave vehicular networks using deep recurrent reinforcement learning.
IEEE Transactions on Intelligent Transportation Systems.
ISSN 1524-9050
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Abstract
Millimeter wave (mmWave) with abundant spectrum resources can realize high-rate communications in vehicular networks. However, the mobility of vehicles and the blocking effect of mmWave propagation bring new challenges to communication security. Cooperative communication is envisioned as a promising physical layer security (PLS) approach to enhance the secrecy performance, but it will induce extra energy consumption of vehicles. This paper proposes a deep recurrent reinforcement learning (DRRL)-based energy-efficient cooperative secure transmission scheme in mmWave vehicular networks, where eavesdropping vehicles attempt to intercept the multi-user downlink communications. We jointly design the mmWave beam allocation, the cooperative nodes selection, and the transmit power of vehicles. Specifically, the mmWave base station selects idle vehicles as relays to overcome the severe blocking attenuation of legitimate transmissions and controls the transmit power to reduce energy consumption. Moreover, to ensure secure transmission, a cooperative vehicle is selected to transmit jamming signals to the eavesdropping vehicles while the legitimate users are not disturbed. We conduct comprehensive interference analysis for both direct transmission and relay-aided transmission, and derive the theoretical expressions for the secrecy capacity. We then design the Dueling Double Deep Recurrent Q-Network (D3RQN) learning algorithm to maximize the total secrecy capacity subject to the energy consumption constraint. We set the energy consumption punishment mechanism to avoid relay vehicles consuming too much power for forwarding signals. We demonstrate that the proposed scheme can rapidly adapt to the highly dynamic vehicular networks and effectively improve secrecy performance while reducing the energy consumption of vehicles.
| Item Type: | Journal article | ||||||
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| Publication Title: | IEEE Transactions on Intelligent Transportation Systems | ||||||
| Creators: | Ju, Y., Gao, Z., Wang, H., Liu, L., Pei, Q., Dong, M., Mumtaz, S. and Leung, V.C.M. | ||||||
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||
| Date: | 6 May 2024 | ||||||
| ISSN: | 1524-9050 | ||||||
| Identifiers: |
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| Rights: | © 2024 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: | Melissa Cornwell | ||||||
| Date Added: | 30 May 2024 13:56 | ||||||
| Last Modified: | 30 May 2024 13:56 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/51488 |
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