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Meta-Reinforcement Learning Based Resource Allocation for Dynamic V2X Communications

Yuan, Y; Zheng, G; Wong, KK; Letaief, KB; (2021) Meta-Reinforcement Learning Based Resource Allocation for Dynamic V2X Communications. IEEE Transactions on Vehicular Technology 10.1109/TVT.2021.3098854. (In press). Green open access

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Abstract

This paper studies the allocation of shared resources between vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing algorithms, dynamic vehicular environments and quanti- zation of continuous power become the bottlenecks for providing an effective and timely resource allocation policy. In this paper, we develop two algorithms to deal with these difficulties. First, we propose a deep reinforcement learning (DRL)-based resource allocation algorithm to improve the performance of both V2I and V2V links. Specifically, the algorithm uses deep Q-network (DQN) to solve the sub-band assignment and deep deterministic policy-gradient (DDPG) to solve the continuous power allocation problem. Second, we propose a meta-based DRL algorithm to enhance the fast adaptability of the resource allocation policy in the dynamic environment. Numerical results demonstrate that the proposed DRL-based algorithm can significantly improve the performance compared to the DQN-based algorithm that quantizes continuous power. In addition, the proposed meta- based DRL algorithm can achieve the required fast adaptation in the new environment with limited experiences.

Type: Article
Title: Meta-Reinforcement Learning Based Resource Allocation for Dynamic V2X Communications
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TVT.2021.3098854
Publisher version: https://doi.org/10.1109/TVT.2021.3098854
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10133204
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