深海不确定环境条件下远程水声通信性能分析与快速预报

陈香梅; 台玉朋; 王海斌; 胡承昊; 汪俊; 王迪雅

doi:10.11999/JEIT251244

深海不确定环境条件下远程水声通信性能分析与快速预报

doi: 10.11999/JEIT251244 cstr: 32379.14.JEIT251244

陈香梅^{1, 2},
台玉朋¹,
王海斌^1, ,,
胡承昊¹,
汪俊¹,
王迪雅¹

1.
中国科学院声学研究所声学与海洋信息全国重点实验室北京 100190
2.
中国科学院大学北京 100049

基金项目: 国家自然科学基金 (62301551)

详细信息

作者简介:
陈香梅：女，博士生，研究方向为水声通信

台玉朋：男，研究员，研究方向为水声通信

王海斌：男，研究员，研究方向为水声通信

胡承昊：男，助理研究员，研究方向为水声通信

汪俊：男，研究员，研究方向为水声通信

王迪雅：女，博士后，研究方向为水声通信

通讯作者:
王海斌　whb@mail.ioa.ac.cn

中图分类号: TN929.3
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- 文章访问数: 55
- HTML全文浏览量: 23
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2025-11-25
- 修回日期: 2026-01-07
- 录用日期: 2026-01-12
- 网络出版日期: 2026-01-27

Performance Analysis and Rapid Prediction of Long-range Underwater Acoustic Communications in Uncertain Deep-sea Environments

1.
State Key Laboratory of Acoustics and Marine Information, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (62301551)

摘要

摘要: 在复杂且动态变化的海洋环境中，通信性能起伏显著且难以预估，传统依赖反馈链路进行信道状态估计与参数调整的方法难以适用于深海远程水声通信。为此，该文提出一种基于深度学习声场不确定预估的水声通信性能分析与快速预报方法，在无反馈条件下实现通信参数与信道状态的高效匹配。该方法基于深度学习快速预测的传播损失概率分布，构建了从传播损失到信噪比，再到统计信道容量与中断容量的链式映射模型，实现环境不确定性与通信性能的量化映射。进一步结合典型深海单载波通信系统在特定信道条件下的链路性能与传播损失的统计特性，提出通信“速率-可靠性”预报方法，评估不同速率下的可靠通信概率，从而为复杂动态环境下的系统参数匹配提供依据。海上试验结果表明，所提方法在复杂信道环境下对通信“速率-可靠性”的预报与实测结果高度一致：会聚区与影区各速率点上的可靠概率偏差分别为0.9%～4%和1%～9%；以90%可靠通信概率为阈值时，预报的最大可靠速率与实测结果一致，验证了该方法在深海远程水声通信中的准确性和实用性。
- 水声通信 /
- 性能预报 /
- 深度学习 /
- 不确定性 /
- 信道容量
Abstract: Objective In complex and dynamically changing deep-sea environments, the performance of underwater acoustic communications shows substantial variability. Feedback-based channel estimation and parameter adaptation are impractical in long-range scenarios because platform constraints prevent reliable feedback channels and the slow propagation of sound introduces significant delay. In typical long-range systems, environmental dynamics are often ignored and communication parameters are selected heuristically, which frequently leads to mismatches with actual channel conditions and causes communication failures or reduced efficiency. Predictive methods able to assess performance in advance and support feed-forward parameter adjustment are therefore required. This study proposes a deep-learning-based framework for performance analysis and rapid prediction of long-range underwater acoustic communications under uncertain environmental conditions to enable efficient and reliable parameter–channel matching without feedback. Methods A feed-forward method for underwater acoustic communication performance analysis and rapid prediction is developed using deep-learning-based sound-field uncertainty estimation. A neural network is first used to estimate probability distributions of Transmission Loss (TL PDFs) at the receiver under dynamic environments. TL PDFs are then mapped to probability distributions of the Signal-to-Noise Ratio (SNR PDFs), enabling communication performance evaluation without real-time feedback. Statistical channel capacity and outage capacity are analyzed to characterize the theoretical upper limits of achievable rates in dynamic conditions. Finally, by integrating the SNR distribution with the bit-error-rate characteristics of a representative deep-sea single-carrier communication system under the corresponding channel, a rate–reliability prediction model is constructed. This model estimates the probability of reliable communication at different data rates and serves as a practical tool for forecasting link performance in highly dynamic and feedback-limited underwater acoustic environments. Results and Discussions The method is validated using simulation data and sea trial data. The TL PDFs predicted by the deep learning model show strong consistency with the traditional Monte Carlo (MC) method across multiple receiver locations (Fig. 6). Under identical computational settings, deep-learning-based TL PDF prediction reduces computation time by 2–3 orders of magnitude compared with the MC method. The chained mapping from TL PDFs to SNR PDFs and then to channel capacity metrics accurately represents the probabilistic features of communication performance under uncertain conditions (Fig. 7 and Fig. 8). The rate–reliability curves derived from the deep-learning-based TL PDFs are highly consistent with MC-based results. In the high sound-intensity region, prediction errors for reliable communication probabilities across data rates range from 0.1% to 3%, and in the low sound-intensity region errors are approximately 0.3% to 5% (Fig. 12). Sea trial results further indicate that predicted rate–reliability performance agrees well with measured data. In the convergence zone, deviations between predicted and measured reliability probabilities at each rate range from 0.9% to 4%, and in the shadow zone from 1% to 9% (Fig. 18). Under a 90% reliability requirement, the maximum achievable rates predicted by the method match the measurements in both the convergence and shadow zones, demonstrating accuracy and practical applicability in complex channel environments. Conclusions A deep-learning-based framework for performance analysis and rapid prediction of long-range underwater acoustic communications in uncertain deep-sea environments is developed and validated. The framework builds a chained mapping from environmental parameters to TL PDFs, SNR PDFs, and communication performance metrics, enabling quantitative capacity assessment under dynamic ocean conditions. Predictive “rate–reliability’’ profiles are obtained by integrating probabilistic propagation characteristics with the performance of a representative deep-sea single-carrier system under the corresponding channel, providing guidance for parameter selection without feedback. Sea trial results confirm strong agreement between predicted and measured performance. The proposed approach offers a technical pathway for feed-forward performance analysis and dynamic adaptation in long-range deep-sea communication systems, and can be extended to other communication scenarios in dynamic ocean environments.
- Underwater acoustic communication /
- Performance forecasting /
- Deep learning /
- Uncertainty /
- Channel capacity

HTML全文

图 1 基于声场不确定性预估的通信前馈式性能分析和快速预报方法

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图 2 基于DL的声场不确定性快速预测

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图 3 DL模型结构

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图 4 用于测试的基础环境

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图 5 基础环境对应的TL场

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图 6 两个方法在不同接收点得到的TL-PDF

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图 7 不同接收深度和距离处统计信道容量

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图 8 不同深度下统计容量和中断容量随距离的变化

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图 9 不同接收点处的仿真信道

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图 10 不同接收点处的SNR-PDF

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图 11 不同接收点处在各调制编码方案下的误码率曲线

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图 12 不同接收点处可靠通信概率

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图 13 实验海区的地形、声速剖面和沉积层参数

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图 14 声源和接收点在TL场中的位置

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图 15 两个接收点处的TL-PDF和SNR-PDF

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图 16 A点处的信道和误码率曲线

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图 17 B点处的信道和误码率曲线

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图 18 两个接收点处不同通信速率的可靠通信概率

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表 1 通信系统采用的调制编码方案

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
调制阶数	2	2	2	2	2	4	4	4	4	8	8	8	16	16	16
编码码率	1/4	1/2	2/3	3/4	5/6	1/2	2/3	3/4	5/6	2/3	3/4	5/6	2/3	3/4	5/6
速率(bit/s)	17	34	45	50	56	67	89	100	112	135	150	167	177	200	222

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表 2 试验参数

试验参数	接收机采样频率 (kHz)	信号带宽 (Hz)	中心频率 (Hz)	通信距离 (km)	符号宽度 (ms)	发射声源级 (dB)	发射深度 (m)	信号长度 (bit)	通信速率 (bit/s)
取值	4	100	500	72.6	15	190	500	192	20,25, 50,100,200

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