Xu, Jing and Tang, Jiarun and Zou, Yuze and Wen, Ruikai and Liu, Wei and He, Jianhua (2023) Sum throughput optimization of wireless powered IRS-assisted multi-user MISO system. Computer Networks, 236. p. 109984. DOI https://doi.org/10.1016/j.comnet.2023.109984
Xu, Jing and Tang, Jiarun and Zou, Yuze and Wen, Ruikai and Liu, Wei and He, Jianhua (2023) Sum throughput optimization of wireless powered IRS-assisted multi-user MISO system. Computer Networks, 236. p. 109984. DOI https://doi.org/10.1016/j.comnet.2023.109984
Xu, Jing and Tang, Jiarun and Zou, Yuze and Wen, Ruikai and Liu, Wei and He, Jianhua (2023) Sum throughput optimization of wireless powered IRS-assisted multi-user MISO system. Computer Networks, 236. p. 109984. DOI https://doi.org/10.1016/j.comnet.2023.109984
Abstract
Intelligent reflecting surface (IRS) is a promising technology for beyond-5G wireless communication systems. However, the energy demand of IRS is often overlooked in existing works, leading to performance issues in practical scenarios. To address this issue, this paper proposes an operating model based on time switching (TS) protocol for an IRS-assisted multi-user multiple-input single-output (MISO) system, which can provide energy for IRS through wireless power transfer (WPT) technology. The system throughput maximization problem is addressed to improve performance. Specifically, a two-stage algorithm combined with alternating optimization, denoted as TAO, is proposed. To further improve the optimization process in large-size IRS scenarios, an improved deep deterministic policy gradient (DDPG) method combined with TAO, denoted as TAO-DDPG, is also proposed. Numerical results demonstrate that the proposed TAO-DDPG algorithm achieves similar performance to TAO while greatly reducing the optimization time.
Item Type: | Article |
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Uncontrolled Keywords: | Intelligent reflecting surface; Wireless communication; Time switching; Deep reinforcement learning; MU-MISO system |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 22 Sep 2023 09:25 |
Last Modified: | 07 Aug 2024 15:35 |
URI: | http://repository.essex.ac.uk/id/eprint/36222 |
Available files
Filename: cn23-ris-accepted_ieee.pdf
Licence: Creative Commons: Attribution 4.0