# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 3 Analysis of Simulation Results  
### Environment Configuration and Parameter Settings  
In the simulation, two neural network models are discussed. Similarly, both models comprise an input layer, hidden layer, fully connected layer, softmax layer, and output layer. Different from each other, the hidden layer is LSTM and BiLSTM, which respectively complete unidirectional propagation and bidirectional propagation of information according to the input time series. The size of the input layer of the two neural network models is 744, the number of hidden units in the LSTM cells is 30, and the output size of the fully connected layer and the number of neural units in the output layer are determined by the number of types of tags. The training dataset is generated by using the m-sequence with a spreading gain of 31 as a spreading sequence and generated under SNR = 5 dB. The training dataset and the test dataset are divided according to a ratio of 3:1. In the offline training stage, in order to sufficiently train the neural network models and make them have the ability to remember and analyze complex UWA channels, based on the SSP actually collected in a specific sea area, the spatial positions of the transmitting transducer and the receiving hydrophone are continuously adjusted according to a specific step. In addition, the BELHOP model is used to obtain multiple groups of CIRs according to the combination of different positions of the transmitting transducer and the receiving hydrophone. In addition, in the online test stage, the spatial positions of the transmitting transducer and the receiving hydrophone will be further adjusted to obtain a variety of position combinations to generate multiple groups of CIRs used in the test stage. Figure 6 shows an environmental configuration for generating CIRs. Figure 7 shows the SSP obtained through the actual collection in the Yellow Sea of China in May 2020. The depth of seawater is 26.5 m. In Figure 6, the orange square represents the position of the transmitting transducer in the offline training stage. At this time, the depth range of the transmitting transducer is 1-26 m and the depth step between transmitting transducers is set to 5 m. The pink triangle represents the position of the receiving hydrophone, the depth range is 1-26 m, and the depth step between them is set to 5 m. The horizontal distance between the transmitting transducer and the receiving hydrophone varies in the range of 4-5 km and the distance step is set to 200 m. The green circle represents the position of the transmitting transducer in the online test stage. Their positions are randomly arranged within the depth range of 1-26 m and the horizontal distance from the receiving hydrophone range of 4-5 km. The parameter settings of the UWA channel simulation are given in Table 1. \begin{table}
\begin{tabular}{l c} \hline \hline
**Simulation Parameter** & **Input Value** \\ \hline Sea depth (m) & 26.5 \\ Transmitting transducer depth (m) & 1:5:26 \\ Receiving hydrophone depth (m) & 1:5:26 \\ Distance (km) & 4:0:2:5 \\ Transducer beam angle (\({}^{\circ}\)) & \(-\)50:50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: UWA channel simulation parameters. Figure 6: Position layout of transmitter and receiver in training stage and test stage. Figure 7: SSP obtained through actual measurement. In the simulation, the ocean bottom parameters [32] are set. In this paper, the flat seabed with very fine sand is considered, in which the density ratio is 1.268, the sound velocity ratio is 1.0568, and the attention coefficient is 0.01875\(dB/m\). Figure 8 shows the shallow water CIR obtained through SSP through BELLIOP software. Figure 9a-c shows CIRs obtained by controlling the change of the position of the transmitting transducer and the change of the horizontal distance between the transmitting transducer and the receiving hydrophone. The shallow water environment generally has the characteristics of high temporal and spatial variability.