# Traffic-Predictive Routing Strategy for Satellite Networks
## 7 Summary  
Aiming at the problems of satellite network load imbalance and unstable link connections, this paper proposes a satellite network routing strategy based on traffic prediction (G-AODV). To achieve load balancing, G-AODV introduces a traffic prediction mechanism in the route discovery phase and uses a neural network model to predict the traffic load at the next moment. A forwarding control strategy is adopted based on the comparison between the traffic prediction value at the next moment and the dynamic threshold, thus preventing the heavily loaded node from becoming an intermediate node. This method prevents a large number of nodes from becoming heavily loaded nodes and thus avoids the possibility of bringing down the entire network. At the same time, paths are changed in advance before node congestion occurs, and data traffic is directed to low-load nodes by setting congestion signals. Considering the dynamics of satellite nodes and the instability characteristics of links, during the route maintenance stage, the route repair method is selected by judging the node stability, that is, local repair or source node repair. Through extensive simulation experiments, we have drawn the following conclusions: in scenarios with varying packet transmission rates, G-AODV demonstrates a significant performance advantage over three other routing strategies, namely E-AODV, DSDSR, and NCMDSDV. The packet delivery rate of G-AODV improves by up to 20%, with a reduction in packet loss of 22%, and a noticeable decrease in end-to-end latency. In scenarios with different numbers of communication connections, G-AODV continues to exhibit a substantial improvement in packet delivery rates by up to 20%, a decrease in packet loss of 18%, and a sustained  
Figure 11: Packet loss rate curve under different communication connection logarithmic scenarios.  
reduction in end-to-end latency. The consistency of these experimental results and the significance of performance improvement further emphasize the superior routing performance of G-AODV in actual satellite network environments. These observed outcomes not only support our conclusions quantitatively but also provide robust statistical evidence for our experiments, enhancing the persuasiveness of our claims regarding the performance enhancement of G-AODV.  
After simulation verification, this method has greatly improved performance, but it also has some limitations. In real-time applications, such as video communications or emergency communications, routing decisions based on traffic prediction may introduce some additional delays. These additional delays can negatively impact user experience. Additionally, using neural networks for traffic prediction may require substantial energy and computing resources, especially in satellite networks with a large number of nodes and limited resources. It should be noted that the satellite network topology changes frequently, including the addition and departure of nodes, the interruption of links, etc. In this case, the predictive model needs to be adjusted in time to adapt to changes, otherwise its accuracy will be affected.  
Conceptualization, Z.L. ([PERSON]); methodology, Z.L. ([PERSON]) and Z.L. ([PERSON]); software, Z.L. ([PERSON]) and W.L.; validation, Z.L. ([PERSON]) and L.W.; investigation, Z.L. ([PERSON]), W.L. and L.W.; writing--original draft preparation, Z.L. ([PERSON]); writing--review and editing, Z.L. ([PERSON]), W.L. and L.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The processed data required to reproduce these findings cannot be shared as the data also form part of an ongoing study.  
The authors declare that they have no known competing financial interest of personal relationships that could have appeared to influence the work reported in this paper.