Research Article
Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications
@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
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.
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