# Traffic-Predictive Routing Strategy for Satellite Networks
## 4 Route Discovery
### Forwarding Control Mechanism  
Each satellite node sends HELLO packets to the neighbor node to detect the current traffic value and the predicted traffic value. The load threshold is set based on the obtained traffic value. The routing request packet is forwarded based on the comparison between the predicted node traffic value and the load threshold. In the context of the existing limitations in satellite network traffic prediction methods, which fail to fully exploit the long-range correlations among data, resulting in significant  
Figure 1: The overall structure of the G-AODV routing protocol. forecasting errors and low prediction accuracy, the literature [23] proposes an innovative satellite network traffic prediction strategy based on an improved Gated Recurrent Unit (GRU) neural network. This forecasting method cleverly integrates attention mechanisms with the GRU neural network to more comprehensively capture the relationships and hidden states among traffic data sequences, thereby significantly improving prediction accuracy. To further optimize model performance, a Particle Swarm Optimization (PSO) algorithm is introduced for determining the optimal combination of hyperparameters. Through the sophisticated adjustments made by this algorithm, the model ensures higher prediction efficiency and better generalization performance in traffic prediction tasks. Under simulated satellite network conditions, extensive simulation validations demonstrate that this prediction method exhibits excellent predictive performance and high accuracy in dynamic network environments. The effectiveness of the proposed approach is theoretically substantiated. This innovative satellite network traffic prediction strategy provides robust support for future satellite communication systems in terms of traffic management. Based on the current traffic value \(Curr\_traffic_{i}\), each satellite node utilizes the traffic prediction model from the literature [23] to forecast the traffic value \(Next\_traffic_{i}\) for the next time step. In the G-AODV protocol, individual satellite nodes broadcast HELLO packets to neighboring nodes to probe the network traffic load in their vicinity. The G-AODV routing protocol necessitates the allocation of new storage space \(Next\_traffic_{i}\) in the HELLO packets of the original AODV. This space is designated for storing the anticipated network traffic values of the satellite nodes for the next time step. Each node then incorporates this value into its neighbor list. The improved HELLO package structure is shown in Table 2.  
The actual traffic values of neighboring nodes directly influence the current load state of the intermediate node, while traffic prediction values provide an estimate of future load trends. To more accurately reflect the actual load conditions of the intermediate node, it is necessary to set a traffic threshold by combining the current traffic values and traffic prediction values. The traffic threshold \(L\) is defined based on the arithmetic average of the actual traffic values and prediction values of all neighboring nodes in the neighbor list. The calculation of the traffic threshold \(L\) is as follows:  
\[L=\frac{1}{n}{\sum\limits_{i=1}^{n}{\frac{Curr\_traffic_{i}+Next\_traffic_{i}} {2}}} \tag{1}\]  
In the formula, \(Curr\_traffic_{i}\) represents the current traffic load value of the satellite node \(i\), \(Next\_traffic_{i}\) represents the predicted traffic load value of the satellite node \(i\), and \(n\) represents the number of neighbor nodes of the satellite node \(i\). Compare the node traffic prediction value with the network traffic threshold \(L\). If the traffic prediction value \(Next\_traffic_{i}\) is less than the network traffic threshold \(L\), the RREQ