Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System
In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by no...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8041 |
| _version_ | 1797575242424516608 |
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| author | Belayneh Abebe Tesfaw Rong-Terng Juang Li-Chia Tai Hsin-Piao Lin Getaneh Berie Tarekegn Kabore Wendenda Nathanael |
| author_facet | Belayneh Abebe Tesfaw Rong-Terng Juang Li-Chia Tai Hsin-Piao Lin Getaneh Berie Tarekegn Kabore Wendenda Nathanael |
| author_sort | Belayneh Abebe Tesfaw |
| collection | DOAJ |
| description | In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as helpful technologies to enhance UAV communication networks. However, due to the high mobility of UAVs, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. The simulation results showed that the proposed GRU model can effectively and accurately estimate the link quality of ground users in the RIS-assisted UAV-enabled wireless communication network. |
| first_indexed | 2024-03-10T21:35:56Z |
| format | Article |
| id | doaj.art-9296178076ec495688b37af31d3e6d09 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:35:56Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9296178076ec495688b37af31d3e6d092023-11-19T15:01:52ZengMDPI AGSensors1424-82202023-09-012319804110.3390/s23198041Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications SystemBelayneh Abebe Tesfaw0Rong-Terng Juang1Li-Chia Tai2Hsin-Piao Lin3Getaneh Berie Tarekegn4Kabore Wendenda Nathanael5Department of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, TaiwanInstitute of Aerospace and System Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanInstitute of Aerospace and System Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Electronic Engineering, National Taipei University of Technology, Taipei 10608, TaiwanIn recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as helpful technologies to enhance UAV communication networks. However, due to the high mobility of UAVs, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. The simulation results showed that the proposed GRU model can effectively and accurately estimate the link quality of ground users in the RIS-assisted UAV-enabled wireless communication network.https://www.mdpi.com/1424-8220/23/19/8041link quality estimationreconfigurable intelligent surfaces (RIS)gated recurrent unit (GRU)unmanned aerial vehicle (UAV) |
| spellingShingle | Belayneh Abebe Tesfaw Rong-Terng Juang Li-Chia Tai Hsin-Piao Lin Getaneh Berie Tarekegn Kabore Wendenda Nathanael Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System Sensors link quality estimation reconfigurable intelligent surfaces (RIS) gated recurrent unit (GRU) unmanned aerial vehicle (UAV) |
| title | Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System |
| title_full | Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System |
| title_fullStr | Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System |
| title_full_unstemmed | Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System |
| title_short | Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System |
| title_sort | deep learning based link quality estimation for ris assisted uav enabled wireless communications system |
| topic | link quality estimation reconfigurable intelligent surfaces (RIS) gated recurrent unit (GRU) unmanned aerial vehicle (UAV) |
| url | https://www.mdpi.com/1424-8220/23/19/8041 |
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