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

CHEN Xiangmei; TAI Yupeng; WANG Haibin; HU Chenghao; WANG Jun; WANG diya

doi:10.11999/JEIT251244

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CHEN Xiangmei, TAI Yupeng, WANG Haibin, HU Chenghao, WANG Jun, WANG diya. Performance Analysis and Rapid Prediction of Long-range Underwater Acoustic Communications in Uncertain Deep-sea Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251244

Citation:

CHEN Xiangmei, TAI Yupeng, WANG Haibin, HU Chenghao, WANG Jun, WANG diya. Performance Analysis and Rapid Prediction of Long-range Underwater Acoustic Communications in Uncertain Deep-sea Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251244

Citation:

CHEN Xiangmei, TAI Yupeng, WANG Haibin, HU Chenghao, WANG Jun, WANG diya. Performance Analysis and Rapid Prediction of Long-range Underwater Acoustic Communications in Uncertain Deep-sea Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251244

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

doi: 10.11999/JEIT251244 cstr: 32379.14.JEIT251244

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)

Received Date: 2025-11-25
Accepted Date: 2026-01-12
Rev Recd Date: 2026-01-07

Available Online: 2026-01-27

Abstract

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

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References(22)

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