Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway Environments
In OFDM wireless communications, channel estimation performance is compromised in high-speed railway environments owing to extremely fast multipath fading and severe Doppler effect. Recently, a deep learning approach has been employed to improve the channel estimation performance, however it encount...
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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/10844284/ |
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| author | Aphitchaya Siriwanitpong Kosuke Sanada Hiroyuki Hatano Kazuo Mori Pisit Boonsrimuang |
| author_facet | Aphitchaya Siriwanitpong Kosuke Sanada Hiroyuki Hatano Kazuo Mori Pisit Boonsrimuang |
| author_sort | Aphitchaya Siriwanitpong |
| collection | DOAJ |
| description | In OFDM wireless communications, channel estimation performance is compromised in high-speed railway environments owing to extremely fast multipath fading and severe Doppler effect. Recently, a deep learning approach has been employed to improve the channel estimation performance, however it encounters significant challenges due to its high computational complexity. In order to deal with these challenges, this paper proposes channel estimation employing deep learning with one-dimensional convolutional neural network (1D CNN) schemes to enhance conventional least squares (LS) estimation. The first scheme provides better performance compared to conventional LS estimation. However, it is only suitable for OFDM systems with full pilot symbols, leading to decreased transmission efficiency and high complexity. In order to address those problems, the second scheme develops 1D CNN-based channel estimation employing scattered pilot symbols to enhance transmission efficiency and reduce computational complexity. In comparison to conventional LS estimation and deep learning-based channel estimation with bi-gated recurrent unit (bi-GRU), the performance evaluation demonstrates that the proposed 1D CNN-based schemes simultaneously improve channel estimation performance, transmission efficiency, and reduce computational complexity. |
| format | Article |
| id | doaj-art-ccdeff9875644fcdace8b405442c3f45 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ccdeff9875644fcdace8b405442c3f452025-08-20T02:15:23ZengIEEEIEEE Access2169-35362025-01-0113131281314210.1109/ACCESS.2025.353100910844284Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway EnvironmentsAphitchaya Siriwanitpong0https://orcid.org/0009-0008-0943-336XKosuke Sanada1https://orcid.org/0000-0001-5440-0868Hiroyuki Hatano2https://orcid.org/0000-0001-5822-8065Kazuo Mori3https://orcid.org/0000-0002-9813-4853Pisit Boonsrimuang4https://orcid.org/0000-0002-2408-2325Department of System Engineering, Graduate School of Engineering, Mie University, Mie, JapanDepartment of System Engineering, Graduate School of Engineering, Mie University, Mie, JapanDepartment of System Engineering, Graduate School of Engineering, Mie University, Mie, JapanDepartment of System Engineering, Graduate School of Engineering, Mie University, Mie, JapanDepartment of Telecommunication Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, ThailandIn OFDM wireless communications, channel estimation performance is compromised in high-speed railway environments owing to extremely fast multipath fading and severe Doppler effect. Recently, a deep learning approach has been employed to improve the channel estimation performance, however it encounters significant challenges due to its high computational complexity. In order to deal with these challenges, this paper proposes channel estimation employing deep learning with one-dimensional convolutional neural network (1D CNN) schemes to enhance conventional least squares (LS) estimation. The first scheme provides better performance compared to conventional LS estimation. However, it is only suitable for OFDM systems with full pilot symbols, leading to decreased transmission efficiency and high complexity. In order to address those problems, the second scheme develops 1D CNN-based channel estimation employing scattered pilot symbols to enhance transmission efficiency and reduce computational complexity. In comparison to conventional LS estimation and deep learning-based channel estimation with bi-gated recurrent unit (bi-GRU), the performance evaluation demonstrates that the proposed 1D CNN-based schemes simultaneously improve channel estimation performance, transmission efficiency, and reduce computational complexity.https://ieeexplore.ieee.org/document/10844284/Channel estimationOFDMhigh-speed railway environmentdeep learningCNN |
| spellingShingle | Aphitchaya Siriwanitpong Kosuke Sanada Hiroyuki Hatano Kazuo Mori Pisit Boonsrimuang Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway Environments IEEE Access Channel estimation OFDM high-speed railway environment deep learning CNN |
| title | Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway Environments |
| title_full | Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway Environments |
| title_fullStr | Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway Environments |
| title_full_unstemmed | Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway Environments |
| title_short | Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway Environments |
| title_sort | deep learning based channel estimation with 1d cnn for ofdm systems under high speed railway environments |
| topic | Channel estimation OFDM high-speed railway environment deep learning CNN |
| url | https://ieeexplore.ieee.org/document/10844284/ |
| work_keys_str_mv | AT aphitchayasiriwanitpong deeplearningbasedchannelestimationwith1dcnnforofdmsystemsunderhighspeedrailwayenvironments AT kosukesanada deeplearningbasedchannelestimationwith1dcnnforofdmsystemsunderhighspeedrailwayenvironments AT hiroyukihatano deeplearningbasedchannelestimationwith1dcnnforofdmsystemsunderhighspeedrailwayenvironments AT kazuomori deeplearningbasedchannelestimationwith1dcnnforofdmsystemsunderhighspeedrailwayenvironments AT pisitboonsrimuang deeplearningbasedchannelestimationwith1dcnnforofdmsystemsunderhighspeedrailwayenvironments |