# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 5 The Experimental Results and Evaluation Metrics
### Analysis of the Results
In Figure 8b, it can be observed that regardless of whether \(\lambda\) follows \(U(1,2)\) or \(U(1,10)\), as the signal-to-noise ratio threshold \(\gamma_{0}\) increases, the connection probability gradually decreases, indicating that the system becomes more stringent for a higher \(\gamma_{0}\), and the power cost required to meet the requirements also increases. It is noteworthy that when the signal-to-noise ratio threshold \(\gamma_{0}\) exceeds 45 dBm, the decline in the connection probability tends to stabilize, suggesting that the system performance becomes stable after this point. The model's accuracy is assessed by comparing the predicted labels of the test set with the actual outputs, using the mean squared error (MSE) as the evaluation metric. For time-series prediction tasks, the error can be calculated to evaluate the final model's effectiveness. The definition of the MSE is as follows: \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline Input layer & Number of nodes & 20K \(\times\) 10 \\ CNN layer & Filter 1: size/number/stride & 3/6/1 \\  & Filter 2: size/number/stride & 3/12/1 \\  & Filter 3: size/number/stride & 3/32/1 \\  & Filter 4: size/number/stride & 3/64/1 \\  & Pooling type/kernel size/stride & AveragePooling/2/1 \\  & Activation function & ReLU \\ BiLSTM layer & Number of LSTM units (forward) & 32 \\  & Number of LSTM units (backward) & 32 \\  & Dropout parameter & 0.2 \\ Attention layer & Activation function & Softmax \\ Output layer & Number of nodes & 20K \(\times\) 1 \\ Hyperparameters & Optimizer & Adam \\  & Batch size & 32 \\  & Epochs & 200 \\  & Learning rate & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CNN-BiLSTM-AM neural network parameter settings. Figure 8: Connectivity probability for varying system parameters. \[M_{\text{MSE}}(y,\hat{y})=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{33}\]  
where \(y\) represents the true values; \(\hat{y}\) represents the predicted results of the sample; and \(n\) represents the number of samples. Three sets of experiments were designed, each with different parameter configurations, to evaluate the predictive performance of the model. The following presents a detailed analysis and the experimental results. Figure 9 illustrates the MSE of the proposed scheme under the Rayleigh fading assumption, when the channel gain coefficients follow different distributions. It can be observed that the curve reaches its minimum value at the 200 th iteration, and the MSE value gradually converges at 0.05 and 0.03, respectively. When \(\lambda\) follows a uniform distribution \(U(1,2)\), the MSE is relatively higher, which is due to the small range of fluctuations limiting the diversity of the sample, thus affecting the model's ability to generalize, leading to a reduction in the performance. Figure 10 demonstrates the trend in the variation in the MSE with respect to the output sequence's length \(N_{o}\), where \(N_{o}\) is set to 1, 4, and 8. It can be observed that when \(N_{o}=1\), the MSE is minimized. The curves gradually converge at 0.05, 0.03, and 0.01, respectively. This indicates that a larger \(N_{o}\) value results in relatively higher MSE. The reason for this lies in the extension of the prediction time span, which increases the difficulty of calculating the connection probabilities. To select the most appropriate \(N_{o}\) value, it is necessary to balance the MSE performance and maintain the prediction accuracy. Figure 10: MSE versus output length in Rayleigh fading. Figure 9: MSE under different Rayleigh fading channel parameters. In contrast to terrestrial systems, maritime communication systems are substantially more influenced by the environmental conditions. Among these, wave height stands out as one of the crucial factors that has an impact. Figure 11 shows the change in the MSE with varying wave heights. Under Rayleigh fading, the difference in the MSEs for different wave heights reaches 0.06.