Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication

ZHENG Jixing; YUAN Yufan; ZHUO Xiaoxiao; LU Xuesong; QU Fengzhong; WEI Yan

doi:10.11993/j.issn.2096-3920.2024-0180

Volume 33 Issue 2

May 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(2): 291-298

ZHENG Jixing, YUAN Yufan, ZHUO Xiaoxiao, LU Xuesong, QU Fengzhong, WEI Yan. Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication[J]. Journal of Unmanned Undersea Systems, 2025, 33(2): 291-298. doi: 10.11993/j.issn.2096-3920.2024-0180

Citation:

ZHENG Jixing, YUAN Yufan, ZHUO Xiaoxiao, LU Xuesong, QU Fengzhong, WEI Yan. Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication[J]. Journal of Unmanned Undersea Systems, 2025, 33(2): 291-298. doi: 10.11993/j.issn.2096-3920.2024-0180

Citation:

ZHENG Jixing, YUAN Yufan, ZHUO Xiaoxiao, LU Xuesong, QU Fengzhong, WEI Yan. Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication[J]. Journal of Unmanned Undersea Systems, 2025, 33(2): 291-298. doi: 10.11993/j.issn.2096-3920.2024-0180

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Prediction of SNR Based on SVR and Adaptive Transmission Power Method for Underwater Acoustic Communication

doi: 10.11993/j.issn.2096-3920.2024-0180

1.
Ocean College, Zhejiang University, Zhoushan 316021, China
2.
Hainan Institute of Zhejiang University, Sanya 572025, China
3.
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
4.
Space Information Research Institute, Hangzhou Dianzi University, Hangzhou 310018, China

Received Date: 2024-12-30
Accepted Date: 2025-02-08
Rev Recd Date: 2025-02-04

Available Online: 2025-03-10

Abstract

Abstract

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.
- underwater acoustic communication,
- signal-to-noise ratio prediction,
- adaptive transmission power,
- support vector regression

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References(11)

References

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