## 4 Simulation Analysis  
The size of the training or test dataset depends on the complexity of the system and the DL algorithm. Using a small dataset may cause poor detection performance because the model would be incapable of fully learning the diverse characteristics of the system. Further, using a large dataset may result in increased computational complexity [30]. Thus, several simulations are conducted to determine the suitable dataset size and the parameters that could offer the best BER performance. It should be noted that the accuracy of the neural network is affected by the DL algorithm itself, which plays an important role in solving some nonlinear problems. Therefore, its performance and robustness need to be evaluated. Without a loss of generality, some common parameters are taken into consideration. The parameters used in the simulation are listed in Table 1.  
Table 2 shows the accuracy of the network under different learning rates. A validated system needs an appropriate learning rate. If the learning rate is too large, the network cannot converge, while if it is too small, the network will converge very slowly or be unable to finish learning. Moreover, the network may change from underfitting to overfitting as the learning rate increases [31]. It is evident from the table that 0.002 has the best performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline size of data & \(2\times 10^{8}\) & roll-off factor & 0.6 \\ training dataset & \(1.6\times 10^{8}\) & acceleration factor & 0.8 \\ test dataset & \(4\times 10^{7}\) & learning rate & 0.002 \\ batch size & 100 & neurons & 256 \\ dropout & 0.05 & layer & 10 \\ cycle index & 50 & activation function & Softmax \\ SNR range \(E_{b}/N_{0}\) & 15 dB–30 dB & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of simulation parameters. Figure 5: Internal calculation process diagram of the attention mechanism. Table 3 show the effect of accuracy with cycle index. The cycle index has a great impact on the network accuracy. If the cycle index is too low, it will lead to the less accurate prediction of the trained network. If the cycle index is too high, the computational complexity will increase dramatically. As shown in Table 3, the cycle index can be set at 50 for the best accuracy. The selection of hidden layers is another key point. A low or high number of hidden layers leads to the phenomena of underfitting or overfitting [31]. The relationship between the number of hidden layers and accuracy is shown in Table 4. The accuracy increases gradually with the number of hidden layers and declines when it reaches a certain value. In addition, studies in [32; 33] found that the increasing number of hidden layers results in a significant increase in computational complexity and overfitting. The causes of overfitting can be divided into three categories [34]. The first is a small dataset of training samples that cannot reflect the overall possible situations. This will lead to the less accurate prediction of the trained network. Therefore, the training dataset should cover all types of data as much as possible. The second is a network that cannot accurately estimate the relationship between input and output because of the excessive interference of training data. The third is the high complexity of the network. Under the circumstances, it should process many parameters to enable the network to accurately fit every data in the training dataset. As a result, the trained network cannot generalize to the test dataset. Therefore, the appropriate number of hidden layers is crucial to the system performance. The simulation experimental results in Table 5 show that the system has the best detection performance when the number of hidden layers is 8. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hidden Layers** & **Accuracy Rate** \\ \hline
6 & 97.632746\% \\
7 & 98.921215\% \\
8 & 99.950473\% \\
9 & 99.091451\% \\ \hline \hline \end{tabular}
\end{table}