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inis 23(1): e1

Research Article

Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications

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  • @ARTICLE{10.4108/eetinis.v10i1.2864,
        author={Khoi Khac Nguyen and Antonino Masaracchia and Cheng Yin},
        title={Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={INIS},
        year={2023},
        month={1},
        keywords={Intelligent reflecting surface (IRS), D2D communications, deep reinforcement learning},
        doi={10.4108/eetinis.v10i1.2864}
    }
    
  • Khoi Khac Nguyen
    Antonino Masaracchia
    Cheng Yin
    Year: 2023
    Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications
    INIS
    EAI
    DOI: 10.4108/eetinis.v10i1.2864
Khoi Khac Nguyen1,*, Antonino Masaracchia1, Cheng Yin2
  • 1: Queen's University Belfast
  • 2: University of Surrey
*Contact email: knguyen02@qub.ac.uk

Abstract

In this paper, we propose a deep reinforcement learning (DRL) approach for solving the optimisation problem of the network’s sum-rate in device-to-device (D2D) communications supported by an intelligent reflecting surface (IRS). The IRS is deployed to mitigate the interference and enhance the signal between the D2D transmitter and the associated D2D receiver. Our objective is to jointly optimise the transmit power at the D2D transmitter and the phase shift matrix at the IRS to maximise the network sum-rate. We formulate a Markov decision process and then propose the proximal policy optimisation for solving the maximisation game. Simulation results show impressive performance in terms of the achievable rate and processing time.

Keywords
Intelligent reflecting surface (IRS), D2D communications, deep reinforcement learning
Received
2022-11-17
Accepted
2022-12-25
Published
2023-01-03
Publisher
EAI
http://dx.doi.org/10.4108/eetinis.v10i1.2864

Copyright © 2023 K. K. Nguyen et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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