Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net
Channel estimation in massive-MIMO FSO systems is critical for ensuring reliable data transmission. However, conventional estimators offer limited benefits due to the computational difficulty of accurately estimating the channel. This paper presents a novel approach to estimate channels using an att...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | ICT Express |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959524001152 |
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| author | Al-Imran Md. Shahriar Nazim Huy Nguyen Yeong Min Jang |
| author_facet | Al-Imran Md. Shahriar Nazim Huy Nguyen Yeong Min Jang |
| author_sort | Al-Imran |
| collection | DOAJ |
| description | Channel estimation in massive-MIMO FSO systems is critical for ensuring reliable data transmission. However, conventional estimators offer limited benefits due to the computational difficulty of accurately estimating the channel. This paper presents a novel approach to estimate channels using an attention residual U-Net (ARU-Net) architecture which utilizes the advantages of both attention and residual connection. In the simulation, the channel matrix has been represented as a 2D image. The proposed model significantly outperforms traditional channel estimation methods and other deep learning models in terms of MSE (10−5 at 25 dB SNR), especially in atmospheric turbulence and other noises. |
| format | Article |
| id | doaj-art-18eada613a094c27a7cc41897ac2de5b |
| institution | Kabale University |
| issn | 2405-9595 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ICT Express |
| spelling | doaj-art-18eada613a094c27a7cc41897ac2de5b2025-08-20T03:44:27ZengElsevierICT Express2405-95952025-04-0111228729210.1016/j.icte.2024.09.012Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net Al-Imran0Md. Shahriar Nazim1Huy Nguyen2Yeong Min Jang3Department of Electronics Engineering, Kookmin University, Seoul 02707, South KoreaDepartment of Electronics Engineering, Kookmin University, Seoul 02707, South KoreaDepartment of Electronics Engineering, Kookmin University, Seoul 02707, South KoreaCorresponding author.; Department of Electronics Engineering, Kookmin University, Seoul 02707, South KoreaChannel estimation in massive-MIMO FSO systems is critical for ensuring reliable data transmission. However, conventional estimators offer limited benefits due to the computational difficulty of accurately estimating the channel. This paper presents a novel approach to estimate channels using an attention residual U-Net (ARU-Net) architecture which utilizes the advantages of both attention and residual connection. In the simulation, the channel matrix has been represented as a 2D image. The proposed model significantly outperforms traditional channel estimation methods and other deep learning models in terms of MSE (10−5 at 25 dB SNR), especially in atmospheric turbulence and other noises.http://www.sciencedirect.com/science/article/pii/S2405959524001152Attention residual UNet (ARU-Net)Free space optic (FSO)Massive multiple input multiple output (m-MIMO)Normalized mean square error (NMSE) |
| spellingShingle | Al-Imran Md. Shahriar Nazim Huy Nguyen Yeong Min Jang Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net ICT Express Attention residual UNet (ARU-Net) Free space optic (FSO) Massive multiple input multiple output (m-MIMO) Normalized mean square error (NMSE) |
| title | Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net |
| title_full | Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net |
| title_fullStr | Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net |
| title_full_unstemmed | Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net |
| title_short | Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net |
| title_sort | channel estimation of massive mimo fso communication system using deep attention residual u net |
| topic | Attention residual UNet (ARU-Net) Free space optic (FSO) Massive multiple input multiple output (m-MIMO) Normalized mean square error (NMSE) |
| url | http://www.sciencedirect.com/science/article/pii/S2405959524001152 |
| work_keys_str_mv | AT alimran channelestimationofmassivemimofsocommunicationsystemusingdeepattentionresidualunet AT mdshahriarnazim channelestimationofmassivemimofsocommunicationsystemusingdeepattentionresidualunet AT huynguyen channelestimationofmassivemimofsocommunicationsystemusingdeepattentionresidualunet AT yeongminjang channelestimationofmassivemimofsocommunicationsystemusingdeepattentionresidualunet |