# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 3 Principles of the Deep Learning Models  
### The CNN-BiLSTM Model  
[PERSON] et al. [19] proposed the CNN-LSTM hybrid model, where a convolutional neural network (CNN) is a multi-layer neural network structure with a deep supervised learning architecture capable of handling time-series and image data. The role of the CNN is to extract the signal features from the received signals and then integrate these extracted features into a two-dimensional array, which is subsequently passed as input to the LSTM layer to analyze the feature information in the time series of the received signals. Unlike the single CNN model, which can only extract local features, the CNN-LSTM fusion model offers more comprehensive feature extraction capabilities and demonstrates a significant improvement in time-series modeling thanks to LSTM's effective modeling of the temporal dependencies. Compared to the standalone LSTM model, CNN-LSTM shows greater applicability and higher computational efficiency when handling high-dimensional time-series data. In this architecture, the CNN first performs dimensionality reduction on the data and extracts key features, significantly reducing the input data dimensions that need to be processed by the subsequent LSTM step. This approach not only accelerates the model training process but also effectively reduces the overall computational complexity. Long short-term memory (LSTM) networks effectively regulate the flow of information by introducing three unique gating mechanisms, the forget gate, the input gate, and the output gate, which improve the network's ability to handle long-term dependencies. The specific network structure of the LSTM model is shown in Figure 3.  
Here, the input at each time step is represented by \(x_{t}\), and the core component \(C_{t}\) (the memory unit) plays a crucial role in LSTM, responsible for maintaining the state information across time steps. The dynamics of the LSTM unit can be described by the following equations:  
\[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+b_{i}) \tag{19}\]  
\[f_{t}=\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f}) \tag{20}\]  
\[o_{t}=\sigma(W_{x0}x_{t}+W_{ho}h_{t-1}+b_{o}) \tag{21}\]  
\[\zeta_{t}=\tanh(W_{xx}x_{t}+W_{hc}h_{t-1}+b_{c}) \tag{22}\]  
\[c_{t}=f_{t}ec_{t-1}+i_{t}e\zeta_{t} \tag{23}\]  
\[h_{t}=o_{t}e\tanh(c_{t}) \tag{24}\]  
Here, \(\sigma\) represents the sigmoid activation function; tanh represents the hyperbolic tangent activation function, as shown in Equations (25) and (26). The input gate controls the input of new information, and the forget gate determines which information in the memory  
Figure 3: The structure of LSTM. unit should be forgotten, while the output gate controls the output to the next state of the LSTM unit. Other variants of recurrent neural networks (RNNs), such as Gated Recurrent Units (GRUs), have a relatively simpler structure, consisting only of a reset gate and an update gate. However, due to the reduced state control capability of the gating mechanism, their performance is inferior to that of LSTM networks when handling long-sequence data. \[\sigma(x)=\frac{1}{1+e^{-x}} \tag{25}\]  
\[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{26}\]  
In comparison with the LSTM model, this paper employs the BiLSTM model, which has significant advantages in processing sequential data. The structure of the BiLSTM network is shown in \[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+b_{i}) \tag{19}\]0  
Here, \(w\) is the trainable weight matrix. As can be seen in Figure 3, the BiLSTM network is composed of two LSTM networks: one is the forward LSTM network, which calculates the forward hidden states (\(\overrightarrow{h}_{1},\ldots,\overrightarrow{h}_{s}\)) of the sequence; the other is the backward LSTM network, which calculates the backward hidden states (\(\overleftarrow{h}_{1},\ldots,\overleftarrow{h}_{s}\)). By integrating the hidden states from both directions at each time step, the network effectively combines the forward and backward feature information.