# Deep Learning-Based Optimization for Maritime Relay Networks
## 3 Relay Network Based on Deep Learning
To effectively address the uncertainties associated with maritime conditions, we introduce a deep learning-based optimization scheme for maritime relay networks. This scheme predicts relay selection and calculates connectivity probabilities over multiple time points without relying on instantaneous CSI. Through simulation experiments and analysis, the proposed scheme demonstrates high accuracy and low MSE when compared to the optimal strategy. The model effectively captures the impact of system and environmental variations on performance, especially under different conditions of wave height and Rayleigh fading. Although the MSE fluctuates with changes in wave height and fading fluctuation range, the overall accuracy remains stable, validating the effectiveness and robustness of the method and demonstrating its reliability in complex maritime communication environments. However, the current model does not explore in depth the potential impact of extreme weather conditions (such as storms, fog, etc.) on performance. Future research can further investigate the performance under these extreme environmental conditions and enhance the model's robustness in such scenarios. Furthermore, the real-world variability of the dataset may affect the model's generalization ability, so future work will focus on optimizing the scheme in scenarios with higher data variability. Conceptualization and formal analysis, N.G. and X.W. ; methodology, X.W. ; software, N.G. ; validation, N.G. ; investigation, N.G. ; writing--original draft preparation, N.G. ; writing--review and editing, N.G. and X.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the Innovation Program of the Shanghai Municipal Education Commission of China under grant 2021-01-07-373 00-10-E00121 and the Shanghai Sailing Program under grant 20 YF1416700. Not applicable

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. Figure 9: MSE relative to wave height in Rayleigh fading. ## References

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