Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer
Reconfigurable intelligent surface (RIS) employs passive beamforming to control the wireless propagation channel, which benefits the wireless communication capacity and the received energy efficiency of wireless power transfer (WPT) systems. Such beamforming schemes are classified as discrete and no...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-06-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864822002401 |
| _version_ | 1797796416546930688 |
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| author | Ziyang Lu Yubin Zhao Xiaofan Li |
| author_facet | Ziyang Lu Yubin Zhao Xiaofan Li |
| author_sort | Ziyang Lu |
| collection | DOAJ |
| description | Reconfigurable intelligent surface (RIS) employs passive beamforming to control the wireless propagation channel, which benefits the wireless communication capacity and the received energy efficiency of wireless power transfer (WPT) systems. Such beamforming schemes are classified as discrete and non-convex integer programming problems. In this paper, we propose a Monte-Carlo (MC) based random energy passive beamforming of RIS to achieve the maximum received power of electromagnetic (EM) WPT systems. Generally, the Gibbs sampling and re-sampling methods are employed to generate phase shift vector samples. And the sample with the maximum received power is considered the optimal solution. In order to adapt to the application scenarios, we develop two types of passive beamforming algorithms based on such MC sampling methods. The first passive beamforming uses an approximation of the integer programming as the initial sample, which is calculated based on the channel information. And the second one is a purely randomized algorithm with the only total received power feedback. The proposed methods present several advantages for RIS control, e.g., fast convergence, easy implementation, robustness to the channel noise, and limited feedback requirement, and they are applicable even if the channel information is unknown. According to the simulation results, our proposed methods outperform other approximation and genetic algorithms. With our methods, the WPT system even significantly improves the power efficiency in the nonline-of-sight (NLOS) environment. |
| first_indexed | 2024-03-13T03:32:46Z |
| format | Article |
| id | doaj.art-be784e5d0b4e476ab24787ab0cf858c8 |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-03-13T03:32:46Z |
| publishDate | 2023-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-be784e5d0b4e476ab24787ab0cf858c82023-06-24T05:18:03ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482023-06-0193667676Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transferZiyang Lu0Yubin Zhao1Xiaofan Li2School of Microelectronics Science and Technology, Sun Yat-Sen University, Zhuhai, 519082, ChinaSchool of Microelectronics Science and Technology, Sun Yat-Sen University, Zhuhai, 519082, China; Corresponding author.School of Intelligent System Science and Engineering, Jinan University, Zhuhai, 519070, ChinaReconfigurable intelligent surface (RIS) employs passive beamforming to control the wireless propagation channel, which benefits the wireless communication capacity and the received energy efficiency of wireless power transfer (WPT) systems. Such beamforming schemes are classified as discrete and non-convex integer programming problems. In this paper, we propose a Monte-Carlo (MC) based random energy passive beamforming of RIS to achieve the maximum received power of electromagnetic (EM) WPT systems. Generally, the Gibbs sampling and re-sampling methods are employed to generate phase shift vector samples. And the sample with the maximum received power is considered the optimal solution. In order to adapt to the application scenarios, we develop two types of passive beamforming algorithms based on such MC sampling methods. The first passive beamforming uses an approximation of the integer programming as the initial sample, which is calculated based on the channel information. And the second one is a purely randomized algorithm with the only total received power feedback. The proposed methods present several advantages for RIS control, e.g., fast convergence, easy implementation, robustness to the channel noise, and limited feedback requirement, and they are applicable even if the channel information is unknown. According to the simulation results, our proposed methods outperform other approximation and genetic algorithms. With our methods, the WPT system even significantly improves the power efficiency in the nonline-of-sight (NLOS) environment.http://www.sciencedirect.com/science/article/pii/S2352864822002401Reconfigurable intelligence surfaceWireless power transferMonte-carlo algorithmPassive beamformingGibbs sampling |
| spellingShingle | Ziyang Lu Yubin Zhao Xiaofan Li Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer Digital Communications and Networks Reconfigurable intelligence surface Wireless power transfer Monte-carlo algorithm Passive beamforming Gibbs sampling |
| title | Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer |
| title_full | Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer |
| title_fullStr | Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer |
| title_full_unstemmed | Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer |
| title_short | Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer |
| title_sort | monte carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer |
| topic | Reconfigurable intelligence surface Wireless power transfer Monte-carlo algorithm Passive beamforming Gibbs sampling |
| url | http://www.sciencedirect.com/science/article/pii/S2352864822002401 |
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