Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication
Marine environmental noise is influenced by many factors such as ocean waves, wind, rain, marine organisms, ships, and industrial activities. Its power is highly random. However, the continuous effect of factors such as sea surface temperature and tidal height can also make the power have certain pe...
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| Format: | Article |
| Language: | zho |
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Science Press (China)
2025-04-01
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| Series: | 水下无人系统学报 |
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| Online Access: | https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2024-0180 |
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| author | Jixing ZHENG Yufan YUAN Xiaoxiao ZHUO Xuesong LU Fengzhong QU Yan WEI |
| author_facet | Jixing ZHENG Yufan YUAN Xiaoxiao ZHUO Xuesong LU Fengzhong QU Yan WEI |
| author_sort | Jixing ZHENG |
| collection | DOAJ |
| description | Marine environmental noise is influenced by many factors such as ocean waves, wind, rain, marine organisms, ships, and industrial activities. Its power is highly random. However, the continuous effect of factors such as sea surface temperature and tidal height can also make the power have certain periodic characteristics. Underwater environmental noise can directly affect the communication packet error rate during underwater acoustic communication. Although increasing the transmission power can raise the received signal-to-noise ratios and decrease the packet error rate, it also enhances the average energy consumption of communication. Therefore, in order to reduce the packet error rate and average energy consumption of underwater acoustic communication, this paper analyzed and predicted the signal-to-noise ratios time series based on the support vector regression(SVR) algorithm and proposed an adaptive transmission power method for underwater acoustic communication based on signal-to-noise ratio prediction. The simulation results show that compared with the exponential smoothing and autoregressive integrated moving average model(ARIMA) methods, the SVR algorithm based on the linear kernel function has the best performance in predicting signal-to-noise ratios and the smallest prediction error on test data. Under different modulation methods, the proposed adaptive transmission power method for underwater acoustic communication can improve the success rate of data packet transmission while reducing energy consumption per kilobyte. |
| format | Article |
| id | doaj-art-6fa97cbc18d54181b8c237284fa5855d |
| institution | Kabale University |
| issn | 2096-3920 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Science Press (China) |
| record_format | Article |
| series | 水下无人系统学报 |
| spelling | doaj-art-6fa97cbc18d54181b8c237284fa5855d2025-08-20T03:29:10ZzhoScience Press (China)水下无人系统学报2096-39202025-04-0133229129810.11993/j.issn.2096-3920.2024-01802024-0180Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic CommunicationJixing ZHENG0Yufan YUAN1Xiaoxiao ZHUO2Xuesong LU3Fengzhong QU4Yan WEI5Ocean College, Zhejiang University, Zhoushan 316021, ChinaOcean College, Zhejiang University, Zhoushan 316021, ChinaShanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, ChinaSpace Information Research Institute, Hangzhou Dianzi University, Hangzhou 310018, ChinaOcean College, Zhejiang University, Zhoushan 316021, ChinaOcean College, Zhejiang University, Zhoushan 316021, ChinaMarine environmental noise is influenced by many factors such as ocean waves, wind, rain, marine organisms, ships, and industrial activities. Its power is highly random. However, the continuous effect of factors such as sea surface temperature and tidal height can also make the power have certain periodic characteristics. Underwater environmental noise can directly affect the communication packet error rate during underwater acoustic communication. Although increasing the transmission power can raise the received signal-to-noise ratios and decrease the packet error rate, it also enhances the average energy consumption of communication. Therefore, in order to reduce the packet error rate and average energy consumption of underwater acoustic communication, this paper analyzed and predicted the signal-to-noise ratios time series based on the support vector regression(SVR) algorithm and proposed an adaptive transmission power method for underwater acoustic communication based on signal-to-noise ratio prediction. The simulation results show that compared with the exponential smoothing and autoregressive integrated moving average model(ARIMA) methods, the SVR algorithm based on the linear kernel function has the best performance in predicting signal-to-noise ratios and the smallest prediction error on test data. Under different modulation methods, the proposed adaptive transmission power method for underwater acoustic communication can improve the success rate of data packet transmission while reducing energy consumption per kilobyte.https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2024-0180underwater acoustic communicationsignal-to-noise ratio predictionadaptive transmission powersupport vector regression |
| spellingShingle | Jixing ZHENG Yufan YUAN Xiaoxiao ZHUO Xuesong LU Fengzhong QU Yan WEI Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication 水下无人系统学报 underwater acoustic communication signal-to-noise ratio prediction adaptive transmission power support vector regression |
| title | Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication |
| title_full | Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication |
| title_fullStr | Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication |
| title_full_unstemmed | Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication |
| title_short | Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication |
| title_sort | prediction of snr based on svr and adaptive transmission power method for underwater acoustic communication |
| topic | underwater acoustic communication signal-to-noise ratio prediction adaptive transmission power support vector regression |
| url | https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2024-0180 |
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