Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit
With the development of autonomous vehicle operation, vehicle-to-vehicle (V2V) communication plays an increasingly important role. However, in high-speed mobile environments, the channel has fast time-varying, which significantly decreases the property of channel estimation. On the other hand, the f...
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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/10840182/ |
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| _version_ | 1832590307678486528 |
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| author | Jun-Han Wang He He Kosuke Tamura Shun Kojima Jaesang Cha Chang-Jun Ahn |
| author_facet | Jun-Han Wang He He Kosuke Tamura Shun Kojima Jaesang Cha Chang-Jun Ahn |
| author_sort | Jun-Han Wang |
| collection | DOAJ |
| description | With the development of autonomous vehicle operation, vehicle-to-vehicle (V2V) communication plays an increasingly important role. However, in high-speed mobile environments, the channel has fast time-varying, which significantly decreases the property of channel estimation. On the other hand, the frame structure of the IEEE 802.11p standard contains a few number of pilots and a large pilot interval, which is not sufficient to track the rapidly changing channel environment accurately. In recent years, deep learning has been widely used for channel estimation. However, these methods typically perform poorly in high-speed mobility scenarios or have excessively high computational complexity. To alleviate such issues, this study proposes a channel estimation method by combining the sparrow search algorithm (SSA) and gated recurrent unit (GRU). In addition, this paper adds the attention mechanism to GRU to improve the robustness of the model. The computer simulation results confirm that, compared to traditional schemes, the proposed estimator can achieve a lower bit error rate (BER) and normalized mean squared error (NMSE). At the same time, the computational complexity of the algorithm has been reduced to some extent, allowing the estimator to complete the channel estimation faster. This study provides a useful reference for optimizing neural networks and thus improving the performance of channel estimators. |
| format | Article |
| id | doaj-art-133a806bbffc486685643a3636853ac4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-133a806bbffc486685643a3636853ac42025-01-24T00:01:23ZengIEEEIEEE Access2169-35362025-01-0113123321234210.1109/ACCESS.2025.352976810840182Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent UnitJun-Han Wang0https://orcid.org/0009-0000-9627-8434He He1Kosuke Tamura2https://orcid.org/0009-0004-8518-7229Shun Kojima3https://orcid.org/0000-0001-9227-4663Jaesang Cha4Chang-Jun Ahn5Graduate School of Engineering, Chiba University, Chiba, JapanGraduate School of Engineering, Chiba University, Chiba, JapanGraduate School of Engineering, Chiba University, Chiba, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanGraduate School of Engineering, Chiba University, Chiba, JapanGraduate School of Engineering, Chiba University, Chiba, JapanWith the development of autonomous vehicle operation, vehicle-to-vehicle (V2V) communication plays an increasingly important role. However, in high-speed mobile environments, the channel has fast time-varying, which significantly decreases the property of channel estimation. On the other hand, the frame structure of the IEEE 802.11p standard contains a few number of pilots and a large pilot interval, which is not sufficient to track the rapidly changing channel environment accurately. In recent years, deep learning has been widely used for channel estimation. However, these methods typically perform poorly in high-speed mobility scenarios or have excessively high computational complexity. To alleviate such issues, this study proposes a channel estimation method by combining the sparrow search algorithm (SSA) and gated recurrent unit (GRU). In addition, this paper adds the attention mechanism to GRU to improve the robustness of the model. The computer simulation results confirm that, compared to traditional schemes, the proposed estimator can achieve a lower bit error rate (BER) and normalized mean squared error (NMSE). At the same time, the computational complexity of the algorithm has been reduced to some extent, allowing the estimator to complete the channel estimation faster. This study provides a useful reference for optimizing neural networks and thus improving the performance of channel estimators.https://ieeexplore.ieee.org/document/10840182/Vehicle-to-vehiclechannel estimationSSAGRUattentionIEEE 802.11p |
| spellingShingle | Jun-Han Wang He He Kosuke Tamura Shun Kojima Jaesang Cha Chang-Jun Ahn Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit IEEE Access Vehicle-to-vehicle channel estimation SSA GRU attention IEEE 802.11p |
| title | Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit |
| title_full | Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit |
| title_fullStr | Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit |
| title_full_unstemmed | Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit |
| title_short | Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit |
| title_sort | vehicle to vehicle communication channel estimator based on gate recurrent unit |
| topic | Vehicle-to-vehicle channel estimation SSA GRU attention IEEE 802.11p |
| url | https://ieeexplore.ieee.org/document/10840182/ |
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