## 4 Conclusions  
Channel modeling is a crucial technology in wireless communication. Traditional channel modeling methods are time-consuming, laborious, and require manual fitting of data. The scheme using a neural network directly as a channel model has the problem of interpretability, which makes it difficult to provide more insight into channels for researchers. The channel modeling method proposed in this paper based on symbolic regression uses a neural network as a tool to automatically generate a channel model, which not only saves labor costs but also avoids the \"black box\" characteristic of neural networks channel model. As a new channel modeling method, this paper successfully generates the free space path loss model based on the terahertz band from simulation data using the improved PhySO symbolic regression method and verifies the effect of symbolic regression on channel modeling. When the channel data is collected, the channel formula can be automatically obtained from the data by the proposed method, that is why the proposed method has an important application prospect in the field of channel modeling.  
Of course, the project is not perfect, it still has problems such as the failure to get the final mathematical expression at one time, the unstable training epochs required for different random samples, and the need to retrain the network to establish the channel model each time facing new data sets. Therefore, further research will be conducted on the following topics:  
1. To establish a usable inter-satellite terahertz channel model, it is necessary to use actual measured channel data for modeling by the proposed method in the future.
2. To expand the practicality of the proposed method, the channel modeling research will be further carried out from the aspects of path loss, delay power distribution,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Sampling Points** & **Expressions** & **(a, b)** \\ \hline
50 & \(Pl=a\log(a^{2})\log(a^{2}df)+a+b\) & (10.00, 42.44) \\
200 & \(Pl=\sqrt{a}+\frac{a\log(aff)}{2}+2b\) & (40.00, 27.03) \\
400 & \(Pl=b\log(b\sqrt{adf})\) & (26.17, 40.00) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Typical Channel Expressions.  
Figure 9: Relationship between sampling points and training epochs.  
multipath angle spatial distribution, Doppler frequency shift, and other aspects of terahertz communication.
* To provide more concise and unified expressions, more efficient neural networks and deep reinforcement learning algorithms will be studied.
* To reduce algorithm training times and improve symbol regression performance when using new data sets, the method based on pre-trained models is a worthwhile research direction.  
Conceptualization, Y.H., B.S. and Z.L.; methodology, B.S.; software, B.S.; validation, B.S. and Z.L.; formal analysis, B.S. and Z.L.; investigation, B.S. and Z.L.; resources, Y.H.; data curation, B.S. and Z.L.; writing--original draft preparation, Y.H., B.S. and Z.L.; writing--review and editing, Y.H., B.S. and Z.L.; visualization, B.S.; supervision, Y.H.; project administration, Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development (R&D) Program, grant number 2019 YFB1803201.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.