# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 6 Conclusions  
In this study, ship cooperation is used for broadband maritime communication, extending the coverage of terrestrial networks. Additionally, a communication channel modeling method is designed to approximate real marine environments. Finally, a CNN-BiLSTM  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MSE (10\({}^{-2}\))** & **MAE (10\({}^{-2}\))** & **R\({}^{2}\) (\%)** & **Inference Time (s)** \\ \hline SVM & 41.27 & 62.20 & 78.61 & 15 \\ CNN-RNN & 8.39 & 25.97 & 96.95 & 75 \\ CNN-GRU & 6.47 & 23.44 & 97.04 & 83 \\ CNN-BiLSTM-AM & 1.26 & 10.23 & 97.98 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of other models with the fusion model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MSE (10\({}^{-2}\))** & **MAE (10\({}^{-2}\))** & **R\({}^{2}\) (\%)** & **Inference Time (s)** \\ \hline CNN & 22.49 & 45.12 & 87.24 & 34 \\ BiLSTM & 10.13 & 29.78 & 95.13 & 90 \\ CNN-BiLSTM-AM & 1.26 & 10.23 & 97.98 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of single models with the fusion model.  
AM-based approach is proposed to calculate the connection probabilities over time series in the absence of instantaneous channel state information.  
The simulation results validate the accuracy of the theoretical analysis and show that reasonable parameter adjustments under different environmental conditions can improve the reliability of communication systems. Compared with the single CNN model, the single BiLSTM model, the traditional machine learning method SVM, and other deep learning models, according to our experimental evaluation, the proposed CNN-BiLSTM-AM approach achieves a lower mean squared error. Moreover, the experiment reveals how the MSE fluctuates with variations in the system parameters and hydrological factors. These results confirm that this approach demonstrates strong robustness in complex marine environments and offers the potential for reliable communication under varying maritime conditions. Equally, the effectiveness and advantages of deep learning techniques in addressing such complex problems are showcased. In future research, it would be advisable to conduct a more in-depth exploration of the impacts of the weather and hydrological conditions in a variety of harsh marine environments on the communication performance. Meanwhile, specific algorithms can be considered for hyperparameter tuning, and the architecture of deep learning models can be optimized to enhance the prediction accuracy and computational efficiency.  
Conceptualization: S.C. and X.W. Methodology: X.W. Software: S.C. Validation: S.C. Formal analysis: S.C. Investigation: S.C. Writing--original draft preparation: S.C. Writing--review and editing: X.W. and S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Innovation Program of the Shanghai Municipal Education Commission of China under grant 2021-01-07-00-10-E00121.  
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.