# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 5 The Experimental Results and Evaluation Metrics
### Analysis of the Results  
The simulation was performed using MATLAB R2022b to generate the raw data, which were derived by modeling essential channel characteristics in the maritime communication environment, including the large-scale path loss and Rayleigh fading. Table 1 lists the configuration of the main communication parameters and dataset parameters. Before model training, each data group must be labeled with its corresponding connection probability as the target. The complete dataset is then split into training and testing sets at an 8:2 ratio. The specific network parameters and hyperparameter settings of the CNN-BiLSTM-AM network are presented in Table 2. The Adam optimizer was employed to optimize the network parameters. The batch size is set to 32 to reduce the memory usage and prevent overfitting. The maximum number of training epochs is set to 200, with an initial learning rate of 0.01. A step-by-step decay strategy is adopted, halving the learning rate every 20 epochs. This strategy helps to accelerate convergence in the early stages of training, avoid gradient explosion, and stabilize the model training process by gradually decreasing the learning rate in the later stages [21]. To verify the congruence between the theoretical values and the simulation results regarding the connectivity probability, Figure 8 depicts the connectivity probability under diverse system parameters. In the two figures, the solid and dashed lines represent the theoretical values of the results obtained when the distribution parameters of the signal-to-noise ratio follow different distributions in order to simulate various channel conditions. The triangle and circle markers correspond to the simulation results obtained through the data generation method. In Figure 8a, where \(\lambda\) follows a uniform distribution \(U(1,2)\) and the transmission power \(\lambda\) is 24 dBm, the theoretical values and the simulated results show a maximum deviation of only 1.22%. In contrast, when \(\lambda\) follows a uniform distribution \(U(1,10)\), the transmission power \(\lambda\) is 27 dBm, and the theoretical values show a maximum deviation of 1.81% compared to the simulated results. In Figure 8b, the maximum difference between the theoretical analysis and the simulation results is only 1.65%. Overall, the high degree of consistency observed between the theoretical and simulation results attests to the precision of the formulas and the dependability of the methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameter** & **Value** \\ \hline Marine communication & Maximum sea wave height & 2 m \\  & Average sea wave height & 1.5 m \\ Transmitter and receiver & Transmission power & 15–30 dBm \\  & Carrier frequency & 5.8 GHz \\  & Transmit antenna height & 12 m \\  & Receive antenna height & 8 m \\  & Sound power & 2–4 dBm \\ Ship movement & Sampling interval & 120 s \\  & Distance & 10–20 km \\  & Speed & \(0\leq v(t)\leq 30\) km/h \\  & Direction & \(-\frac{\pi}{6}\leq\theta(t)\leq\frac{\pi}{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Marine wireless communication environment and dataset parameters. In Figure 8a, when \(\lambda\) follows a uniform distribution \(U(1,2)\), as the transmission power \(P_{\text{s}}\) increases from 5 to 15 dBm, the connection probability \(P_{2 hop}\) shows a rapid increase. Especially at lower transmission powers, the system's performance improves significantly. However, when \(\lambda\) follows \(U(1,10)\), as the transmission power \(P_{\text{s}}\) increases from 5 to 15 dBm, the connection probability \(P_{2 hop}\) shows a more gradual increase, indicating that the expansion of the channel attenuation range has a more significant negative impact on the system performance.