Multi-agent DRL-based task offloading in multiple RIS-aided IoV networks

Hazarika, B., Singh, K., Biswas, S., Mumtaz, S. ORCID: 0000-0001-6364-6149 and Li, C.-P., 2023. Multi-agent DRL-based task offloading in multiple RIS-aided IoV networks. IEEE Transactions on Vehicular Technology. ISSN 0018-9545

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This paper considers an internet of vehicles (IoV) network, where multi-access edge computing (MAEC) servers are deployed at base stations (BSs) aided by multiple reconfigurable intelligent surfaces (RISs) for both uplink and downlink transmission. An intelligent task offloading methodology is designed to optimize the resource allocation scheme in the vehicular network which is based on the state of criticality of the network and the priority and size of tasks. We then develop a multi-agent deep reinforcement learning (MA-DRL) framework using the Markov game for optimizing the task offloading decision strategy. The proposed algorithm maximizes the mean utility of the IoV network and improves communication quality. Extensive numerical results were performed that demonstrate that the RIS-assisted IoV network using the proposed MA-DRL algorithm achieves higher utility than current state-of-the art networks (not aided by RISs) and other baseline DRL algorithms, namely soft actor-critic (SAC), deep deterministic policy gradient (DDPG), twin delayed DDPG (TD3). The proposed method improves the offloading data rate of the tasks, reduces the mean delay and ensures that a higher percentage of offloaded tasks are completed compared to that of other DRL-based and non-RIS-assisted IoV frameworks

Item Type: Journal article
Publication Title: IEEE Transactions on Vehicular Technology
Creators: Hazarika, B., Singh, K., Biswas, S., Mumtaz, S. and Li, C.-P.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 22 September 2023
ISSN: 0018-9545
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: 25 Sep 2023 11:29
Last Modified: 25 Sep 2023 11:29

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