Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and sh...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/9/11/1252 |
| _version_ | 1797509807614197760 |
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| author | Yufei Liu Feng Zhou Gang Qiao Yunjiang Zhao Guang Yang Xinyu Liu Yinheng Lu |
| author_facet | Yufei Liu Feng Zhou Gang Qiao Yunjiang Zhao Guang Yang Xinyu Liu Yinheng Lu |
| author_sort | Yufei Liu |
| collection | DOAJ |
| description | A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and short-term memory (LSTM) architecture-based neural network model as the receiving module of the system. The neural network is fed with the communication signals passing through known channel impulse responses in the offline stage, and then directly used to demodulate the received signal in the online stage to reduce the influence of the above factors. Numerical simulation and actual data results suggest that the deep learning-based CSK-SS UWA communication system is more reliable communication than a conventional system. In particular, the collected experimental data show that after preprocessing, when the communication rate is less than 180 bps, a bit error rate of less than 10<sup>−3</sup> can be obtained at a signal-to-noise ratio of −8 dB. |
| first_indexed | 2024-03-10T05:22:59Z |
| format | Article |
| id | doaj.art-c96b736ec43c41df9a58407429dbf879 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T05:22:59Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-c96b736ec43c41df9a58407429dbf8792023-11-22T23:54:03ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-11-01911125210.3390/jmse9111252Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic CommunicationYufei Liu0Feng Zhou1Gang Qiao2Yunjiang Zhao3Guang Yang4Xinyu Liu5Yinheng Lu6Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaAcoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaAcoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaYichang Testing Technique Research Institute, Yichang 443003, ChinaAcoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaAcoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaAcoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaA deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and short-term memory (LSTM) architecture-based neural network model as the receiving module of the system. The neural network is fed with the communication signals passing through known channel impulse responses in the offline stage, and then directly used to demodulate the received signal in the online stage to reduce the influence of the above factors. Numerical simulation and actual data results suggest that the deep learning-based CSK-SS UWA communication system is more reliable communication than a conventional system. In particular, the collected experimental data show that after preprocessing, when the communication rate is less than 180 bps, a bit error rate of less than 10<sup>−3</sup> can be obtained at a signal-to-noise ratio of −8 dB.https://www.mdpi.com/2077-1312/9/11/1252cyclic shift keying spread spectrumlow signal-to-noise ratiomultipath effectsneural network modellong- and short-term memory |
| spellingShingle | Yufei Liu Feng Zhou Gang Qiao Yunjiang Zhao Guang Yang Xinyu Liu Yinheng Lu Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication Journal of Marine Science and Engineering cyclic shift keying spread spectrum low signal-to-noise ratio multipath effects neural network model long- and short-term memory |
| title | Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| title_full | Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| title_fullStr | Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| title_full_unstemmed | Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| title_short | Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| title_sort | deep learning based cyclic shift keying spread spectrum underwater acoustic communication |
| topic | cyclic shift keying spread spectrum low signal-to-noise ratio multipath effects neural network model long- and short-term memory |
| url | https://www.mdpi.com/2077-1312/9/11/1252 |
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