# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 6 Conclusions  
Firstly, this paper introduced the traffic characteristics of the Space-Integrated Ground Information Intelligent Network and pointed out the shortcomings of the existing algorithms in dealing with the traffic prediction problem. Then, we proposed an algorithm to fulfill the demand of the intelligent network traffic prediction task of the Space-Integrated Ground Information Intelligent Network, improve the training speed before the prediction, and the accuracy during the prediction as well as the algorithm running speed, and efficiently process the network traffic data with large sample size, high burst, and high volatility. The methods of segmenting network traffic data according to the degree of self-correlation, the GTCN algorithm combining the gating unit and the TCN algorithm are proposed. Then, the traditional algorithm of ARIMA and variations of RNN, such as LSTM, GRU, TCN, and GTCN algorithms were compared under the same experimental environment. We demonstrated that the proposed algorithm, GTCN, can improve the prediction accuracy, especially when traffic bursts occur. It can handle the high volatility and high burst of network traffic well and can greatly enhance the predicted efficiency to complete the Space-Integrated Ground Information Intelligent Network traffic prediction task. However, since the GTCN is a variation of CNN, to speed up the training by using parallel processing, join the gating control unit, and so on, the memory resources consumption for training shall be higher than other algorithms. Therefore, compared with the air base and sea base which require more stringent computing resources, GTCN is more suitable to be applied to the ground base, where not only can the resources be afforded and guaranteed but also convenient for the foundation of the Space-Integrated Ground Information Intelligent Network as a whole and the planning of intelligent networks of other units. As a preparatory study for other tasks of the Space-Integrated Ground Information Intelligent Network, the traffic prediction proposed in this paper lays a foundation for the research of queue scheduling and congestion control.  
Conceptualization, C.P. and L.Y.; methodology, Y.W.; software, Y.W.; validation, C.P., L.Y. and Y.W.; formal analysis, Y.W.; investigation, Y.W.; resources, L.Y.; data curation, Y.W.;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{_Model_} & \multicolumn{3}{c}{_Training_} & \multicolumn{3}{c}{_Running_} \\ \cline{2-5}  & **MIMU(Mb)** & **AMU(Mb/s)** & **MIMU(Mb)** & **AMU(Mb/s)** \\ \hline LSTM & 50.12 & 45.90 & 182.33 & 164.24 \\ GRU & 45.41 & 47.12 & 185.17 & 157.13 \\ ARIMA & - & - & 207.92 & 197.32 \\ TCN & 70.14 & 46.10 & 102.38 & 94.13 \\ GTCN & 90.01 & 41.31 & 115.05 & 112.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Memory Occupancy of Different Algorithms.  
writing--original draft preparation, Y.W.; writing--review and editing, C.P. and L.Y.; visualization, Y.W.; supervision, L.Y.; project administration, C.P.; funding acquisition, C.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China] grant number [No.61722105, No. 61931004] And the APC was funded by [Dalian University].  
The authors declare no conflict of interest.