# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 5 The Experimental Results and Evaluation Metrics
### Model Comparison  
To verify the performance of the CNN-BiLSTM-AM fusion model proposed in this study, we conducted comparative experiments between the proposed model and a single CNN model, a single BiLSTM model, the traditional machine learning method Support Vector Machine (SVM), and other deep learning models. A total of 2K samples were extracted from the test set (the marine communication link dataset) as the experimental data, and all of the models were evaluated under the same experimental conditions and parameter settings for prediction. The performance in link prediction was assessed using three metrics, the mean squared error (MSE), the mean absolute error (MAE), and the coefficient of determination (R\({}^{2}\)), while the inference time was used to evaluate the computational complexity and performance cost of the models. The calculation formulas for each error index are presented as follows:  
\[MAE(y,\hat{y})=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{34}\]  
Figure 11: MSE versus wave height in Rayleigh fading.  
\[R^{2}=1-\frac{\sum_{j=1}^{N}[\beta(i)-y(i)]^{2}}{\sum_{j=1}^{N}[y(i)-\hat{y}]^{2}} \tag{35}\]  
As shown in Table 3, the MSE of the proposed fusion model improved by 0.2123 and 0.0887 compared to that of the single CNN and single BiLSTM models, respectively. Furthermore, the R\({}^{2}\) value of the proposed model reached 97.98%, close to 1, indicating a high level of fit. By leveraging the CNN to extract the local features of the sequence and BiLSTM for global temporal modeling, the fusion of these two complementary models resulted in a higher prediction accuracy than that of any single model.  
This paper conducts a comparison not only with a single model but also traditional machine learning algorithms, such as SVM and other deep learning models. The experimental results are shown in Table 4. The MSE, MAE, and R\({}^{2}\) of the CNN-BiLSTM-AM model are 0.0126, 0.1023, and 97.98%, respectively, demonstrating the best prediction accuracy and fitting performance. Specifically, compared to the CNN-GRU and CNN-RNN models, the MSE is reduced by 0.0521 and 0.0713, respectively. The prediction performance of the CNN-RNN model is relatively poor due to the vanishing gradient problem in the RNN model, which makes it difficult to learn long-term dependency information. Although the CNN-GRU model alleviates the vanishing gradient problem to some extent, the GRU lacks the bidirectional reading capability of BiLSTM, making it unable to fully utilize the forward and backward information on the sequence, thus leading to an inferior prediction performance compared to that of bidirectional LSTM. Additionally, the MSE of the SVM model is the highest at 0.4127, with an R\({}^{2}\) of 78.61%, indicating that the prediction accuracy and fitting performance of the SVM model are relatively poor. As a traditional machine learning method, SVM has a limited generalization ability when handling high-dimensional complex data and cannot automatically learn the data features as deep learning models can. This further validates the reasonableness and effectiveness of the method proposed in this paper. Due to the higher complexity of the CNN-BiLSTM-AM model, its inference time is 101 s, which is relatively conservative compared to that of other network models. Despite this, the higher computational cost significantly enhances the accuracy of its link predictions.