Active Element Arrangement for Deep Learning-Based CSI Prediction in IRS-Assisted Systems
Recently, intelligent reflecting surface (IRS) components have garnered attention as a technology for advancing next-generation wireless communications. For effective IRS control with minimized overhead, a scheme is needed for estimating partial channels using active elements. Given that the number...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10818463/ |
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| author | Yoshihiko Tsuchiya Norisato Suga Kazunori Uruma Masaya Fujisawa |
| author_facet | Yoshihiko Tsuchiya Norisato Suga Kazunori Uruma Masaya Fujisawa |
| author_sort | Yoshihiko Tsuchiya |
| collection | DOAJ |
| description | Recently, intelligent reflecting surface (IRS) components have garnered attention as a technology for advancing next-generation wireless communications. For effective IRS control with minimized overhead, a scheme is needed for estimating partial channels using active elements. Given that the number of active elements affects production costs and energy consumption, various methods have been proposed for predicting channels with the fewest elements. However, despite the significant impact of active element arrangement on prediction accuracy, as corroborated in this study, there has been limited discussion on this aspect, especially in deep learning (DL)-based prediction methods. In this study, we conducted experimental investigations to explore the conditions under which an active element arrangement resulting in accurate DL based predictions. Subsequently, we developed a novel active element arrangement based on three policies extracted from the simulation results. Through numerical experiments, we demonstrated that the proposed arrangement facilitates accurate prediction regardless of the number of active elements. |
| format | Article |
| id | doaj-art-0ca99d607afe48d8b137c11a3c2a37ec |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0ca99d607afe48d8b137c11a3c2a37ec2025-01-07T00:02:34ZengIEEEIEEE Access2169-35362025-01-01132829284310.1109/ACCESS.2024.352421810818463Active Element Arrangement for Deep Learning-Based CSI Prediction in IRS-Assisted SystemsYoshihiko Tsuchiya0https://orcid.org/0009-0005-4596-6436Norisato Suga1https://orcid.org/0000-0001-5127-224XKazunori Uruma2https://orcid.org/0000-0002-3915-6796Masaya Fujisawa3https://orcid.org/0000-0001-8385-5781Department of Information and Computer Technology, Tokyo University of Science, Katsushika, Tokyo, JapanCollege of Engineering, Shibaura Institute of Technology, Koto, Tokyo, JapanDepartment of Computer Science, Kogakuin University, Shinjuku, Tokyo, JapanDepartment of Information and Computer Technology, Tokyo University of Science, Katsushika, Tokyo, JapanRecently, intelligent reflecting surface (IRS) components have garnered attention as a technology for advancing next-generation wireless communications. For effective IRS control with minimized overhead, a scheme is needed for estimating partial channels using active elements. Given that the number of active elements affects production costs and energy consumption, various methods have been proposed for predicting channels with the fewest elements. However, despite the significant impact of active element arrangement on prediction accuracy, as corroborated in this study, there has been limited discussion on this aspect, especially in deep learning (DL)-based prediction methods. In this study, we conducted experimental investigations to explore the conditions under which an active element arrangement resulting in accurate DL based predictions. Subsequently, we developed a novel active element arrangement based on three policies extracted from the simulation results. Through numerical experiments, we demonstrated that the proposed arrangement facilitates accurate prediction regardless of the number of active elements.https://ieeexplore.ieee.org/document/10818463/Intelligent reflecting surfaceIRSreconfigurable intelligent surfaceRISactive element arrangementdeep learning |
| spellingShingle | Yoshihiko Tsuchiya Norisato Suga Kazunori Uruma Masaya Fujisawa Active Element Arrangement for Deep Learning-Based CSI Prediction in IRS-Assisted Systems IEEE Access Intelligent reflecting surface IRS reconfigurable intelligent surface RIS active element arrangement deep learning |
| title | Active Element Arrangement for Deep Learning-Based CSI Prediction in IRS-Assisted Systems |
| title_full | Active Element Arrangement for Deep Learning-Based CSI Prediction in IRS-Assisted Systems |
| title_fullStr | Active Element Arrangement for Deep Learning-Based CSI Prediction in IRS-Assisted Systems |
| title_full_unstemmed | Active Element Arrangement for Deep Learning-Based CSI Prediction in IRS-Assisted Systems |
| title_short | Active Element Arrangement for Deep Learning-Based CSI Prediction in IRS-Assisted Systems |
| title_sort | active element arrangement for deep learning based csi prediction in irs assisted systems |
| topic | Intelligent reflecting surface IRS reconfigurable intelligent surface RIS active element arrangement deep learning |
| url | https://ieeexplore.ieee.org/document/10818463/ |
| work_keys_str_mv | AT yoshihikotsuchiya activeelementarrangementfordeeplearningbasedcsipredictioninirsassistedsystems AT norisatosuga activeelementarrangementfordeeplearningbasedcsipredictioninirsassistedsystems AT kazunoriuruma activeelementarrangementfordeeplearningbasedcsipredictioninirsassistedsystems AT masayafujisawa activeelementarrangementfordeeplearningbasedcsipredictioninirsassistedsystems |