2D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor Network
In underwater acoustic (UWA) sensor network, the channel impulse response (CIR) at the transmitter is important to increase the link reliability and the throughput. The CIR feedback to the transmitter decreases the throughput due to the feedback propagation delay, and the estimation of the CIR at th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9785625/ |
| _version_ | 1818206801597825024 |
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| author | Yongcheol Kim Hojun Lee Seunghwan Seol Jaehak Chung |
| author_facet | Yongcheol Kim Hojun Lee Seunghwan Seol Jaehak Chung |
| author_sort | Yongcheol Kim |
| collection | DOAJ |
| description | In underwater acoustic (UWA) sensor network, the channel impulse response (CIR) at the transmitter is important to increase the link reliability and the throughput. The CIR feedback to the transmitter decreases the throughput due to the feedback propagation delay, and the estimation of the CIR at the transmitter is also difficult since the sound of speed profile (SSP) may not be continuously measured. This paper proposes a deep learning based CIR estimator that estimates the SSP from only one water temperature sensor at a depth of the transmitter. The proposed CIR estimator consists of a 2-dimensional temperature network, 2-dimensional bidirectional long short term memory (2D BiLSTM), and a fully connected layer. The proposed algorithm learns the SSP variation with the depth and the time using 2D BiLSTM and estimates the CIR from the SSP. The estimated CIRs are utilized for the multi-user diversity to increase the link reliability and the throughput of the UWA sensor network. The computer simulations and practical ocean experiments were executed to evaluate the estimation error, the bit error rate and the throughput of the proposed algorithm. The proposed CIR estimator demonstrated better performance than other 1D conventional algorithms. |
| first_indexed | 2024-12-12T04:18:48Z |
| format | Article |
| id | doaj.art-f1659a505b674461a1915590599b2782 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T04:18:48Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f1659a505b674461a1915590599b27822022-12-22T00:38:23ZengIEEEIEEE Access2169-35362022-01-0110572275723310.1109/ACCESS.2022.317900197856252D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor NetworkYongcheol Kim0https://orcid.org/0000-0002-2475-5147Hojun Lee1https://orcid.org/0000-0003-4565-8802Seunghwan Seol2https://orcid.org/0000-0001-5383-359XJaehak Chung3https://orcid.org/0000-0001-5993-3698Department of Electronics Engineering, Inha University, Incheon, Republic of KoreaDepartment of Electronics Engineering, Inha University, Incheon, Republic of KoreaDepartment of Electronics Engineering, Inha University, Incheon, Republic of KoreaDepartment of Electronics Engineering, Inha University, Incheon, Republic of KoreaIn underwater acoustic (UWA) sensor network, the channel impulse response (CIR) at the transmitter is important to increase the link reliability and the throughput. The CIR feedback to the transmitter decreases the throughput due to the feedback propagation delay, and the estimation of the CIR at the transmitter is also difficult since the sound of speed profile (SSP) may not be continuously measured. This paper proposes a deep learning based CIR estimator that estimates the SSP from only one water temperature sensor at a depth of the transmitter. The proposed CIR estimator consists of a 2-dimensional temperature network, 2-dimensional bidirectional long short term memory (2D BiLSTM), and a fully connected layer. The proposed algorithm learns the SSP variation with the depth and the time using 2D BiLSTM and estimates the CIR from the SSP. The estimated CIRs are utilized for the multi-user diversity to increase the link reliability and the throughput of the UWA sensor network. The computer simulations and practical ocean experiments were executed to evaluate the estimation error, the bit error rate and the throughput of the proposed algorithm. The proposed CIR estimator demonstrated better performance than other 1D conventional algorithms.https://ieeexplore.ieee.org/document/9785625/Underwater communicationchannel state estimationrecurrent neural networksresource management |
| spellingShingle | Yongcheol Kim Hojun Lee Seunghwan Seol Jaehak Chung 2D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor Network IEEE Access Underwater communication channel state estimation recurrent neural networks resource management |
| title | 2D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor Network |
| title_full | 2D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor Network |
| title_fullStr | 2D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor Network |
| title_full_unstemmed | 2D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor Network |
| title_short | 2D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor Network |
| title_sort | 2d bilstm based channel impulse response estimator for improving throughput in underwater sensor network |
| topic | Underwater communication channel state estimation recurrent neural networks resource management |
| url | https://ieeexplore.ieee.org/document/9785625/ |
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