Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Advances in Computer, Signals and Systems
  4. Vol 9, Issue 2, 2025

Research on Ocean Path Loss Prediction Method Based on Diffusion Model

Download as PDF

DOI: 10.23977/acss.2025.090210 | Downloads: 16 | Views: 473

Author(s)

Ruiting Xu 1

Affiliation(s)

1 Changwang School of Honors, Nanjing University of Information Science & Technology, Nanjing, 210044, China

Corresponding Author

Ruiting Xu

ABSTRACT

With the rapid development of the Internet of Underwater Things (IoUT) and underwater communication technologies, the accurate prediction of path loss in complex ocean environments has become a key challenge. Aiming at the problem of insufficient prediction accuracy of traditional empirical models in complex ocean channels, this paper proposes, for the first time, a fast ocean path loss prediction method based on a diffusion model with physical constraints. A path loss dataset that couples multiple physical fields such as temperature, salinity, depth, and wave height is generated through the BELLHOP model. A conditional diffusion model framework with UNet as the core is constructed. The cross-attention mechanism is used to dynamically integrate environmental parameters, and the sound speed equation constraint is introduced to optimize the loss function, enhancing physical consistency. Experiments show that the mean squared error (4.3 dB²) of this method on the test set is significantly better than that of the two-ray model (12.5 dB²) and deep learning models (6.8 dB² for ResNet18). The coefficient of determination reaches 0.92, and it can accurately capture the sound field attenuation in the thermocline and the characteristics of seabed reflection. The research reveals the advantages of diffusion models in quantifying the uncertainty of ocean channels and high-fidelity modeling, providing theoretical support for the design of underwater communication systems. However, it is necessary to further optimize the generalization ability under extreme sea conditions and the edge deployment efficiency. This achievement opens up a new path for ocean Internet of Things channel modeling.

KEYWORDS

Ocean Path Loss; Diffusion Model; Physical Constraints; BELLHOP

CITE THIS PAPER

Ruiting Xu, Research on Ocean Path Loss Prediction Method Based on Diffusion Model. Advances in Computer, Signals and Systems (2025) Vol. 9: 76-86. DOI: http://dx.doi.org/10.23977/acss.2025.090210.

REFERENCES

[1] Khalil, R. A., Saeed, N., Babar, M. I.& Jan, T. (2021). Toward the Internet of Underwater Things: Recent Developments and Future Challenges. IEEE Consumer Electronics Magazine, 10(6), 32-37. doi: 10.1109/MCE.2020.2988441.
[2] Zhao, X. W., Chen, Z., Wang, Z. W., et al. Modeling and Simulation of Echo Signals of Moving Targets in Complex Ocean Environments [J]. Mobile Communications, 2022, 46(7): 82-87.
[3] Yang, X. L. Research on Wireless Channel Modeling of Ocean Surface Based on Ray Tracing [D]. Fujian: Xiamen University, 2014.
[4] Yang, L., Zhang, Z., Song, Y., et al. Diffusion Models: A Comprehensive Survey of Methods and Applications [J]. ACM Computing Surveys, 2022, 55(13): 1-58. arXiv: 2209.00796.
[5] Durand, J. C. Granier, P. Radar coverage assessment in nonstandard and ducting conditions: A geometrical optics approach [C]/IEE Proceedings F (Radar and Signal Processing). IET Digital Library, Wales and Scotland, UK, 1990: 95-101.
[6] Doerry, A. Earth Curvature and Atmospheric Refraction Effects on Radar Signal Propagation[R]. Sandia National Lab., Albuquerque, NM, USA, 2013: 1088060.
[7] Hata, M. Empirical Formula for Propagation Loss in Land Mobile Radio Services[J]. IEEE Transactions on Vehicular Technology, 1980, 29: 317-325.
[8] Isabona, J., & Imoize, A. L. Terrain-Based Adaption of Propagation Model Loss Parameters Using Non-Linear Square Regression [J]. Journal of Engineering and Applied Sciences, 2021, 68: 33.
[9] Ying, Q.& Zhou, Y. Research on Electromagnetic Wave Propagation Model of Sea Area [M]. Master's Thesis, Hainan University, Haikou, China, 2015.
[10] Mom, J. M., Mgbe, C. O,& Igwue, G. A. Igwue. Application of artificial neural network for path loss prediction in urban macrocellular environment [J]. American Journal of Engineering Research, 2014, 3: 270-275.
[11] Ostlin, E., Zepernick, H.-J., & Suzuki, H. Macrocell Path-Loss Prediction Using Artificial Neural Networks [J]. IEEE Transactions on Vehicular Technology, 2010, 59: 2735-2747. 
[12] Chen, L., Shaogui, D., Zhiqiang, L., et al. Simulation and Application of Electromagnetic Wave Propagation Logging Tool in Microwave Band Based on CST [C]/Proceedings of the 2021 13th International Symposium on Antennas, Propagation and EM Theory (ISAPE), Zhuhai, China, 1-4 December 2021. IEEE, Zhuhai, China, 2021: 1-3.
[13] Cheerla, S., Ratnam, D. V., & Borra, H. S. Neural Network-Based Path Loss Model for Cellular Mobile Networks at 800 and 1800 MHz Bands [J]. AEU-International Journal of Electronics and Communications, 2018, 94: 179-186.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 38554
Visits: 698001

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.