# Traffic-Predictive Routing Strategy for Satellite Networks
###### Abstract  
To address the issues of uneven satellite network load and unstable link connections, a globally adaptive satellite network routing strategy based on traffic prediction (G-AODV) is proposed by enhancing the existing Ad hoc On-Demand Distance Vector Routing (AODV) protocol. To prevent heavily loaded nodes from becoming intermediate nodes, G-AODV introduces a traffic prediction mechanism in the route discovery phase. The routing request packet adopts the corresponding forwarding control policy based on the comparison between the predicted traffic load value at the next moment and the dynamic threshold. At the same time, a path replacement strategy is adopted to replace paths before node congestion occurs to achieve load balancing. Considering the unstable characteristics of the satellite chain that easily breaks, the route maintenance phase selects a path repair method by judging the node stability to avoid secondary breaks in the route. The simulation results show that in scenarios with different packet delivery rates, compared with the three comparative routing strategies, the packet delivery rate of G-AODV is increased by up to 20%, the packet loss rate is reduced by 22%, and the end-to-end delay is significantly reduced. In scenarios with different communication connection pairs, G-AODV's packet delivery rate increased by up to 20%, the packet loss rate decreased by 18%, and the end-to-end delay was still reduced. traffic prediction; satellite network routing; node stability; forwarding control mechanism 20241301  
to the characteristics of the satellite network. One of the typical characteristics of satellite networks is the unbalanced network load. From the perspective of an individual satellite, the relative motion between the satellite and the Earth results in constant fluctuations in the traffic load. For example, a satellite covering land areas may transition to covering oceans after a certain period. From the overall point of view, due to the uneven distribution of user density and traffic demand of satellite networks, the utilization of satellite network resources is unbalanced. In addition, the unstable connection of inter-satellite links is caused by the complex working environment of satellite networks. This feature will cause data packet loss and data incompleteness during data transmission. The instability of satellite links can also cause routers to make inappropriate decisions, such as choosing the shortest path over the unstable link rather than the more reliable path. The unpredictable nature of satellite link instability poses ongoing challenges in maintaining a consistent and reliable communication environment. As a result, this complex interplay of factors heightens the likelihood of data loss, compromises data integrity, and overall contributes to a decline in network performance. To address the issues of uneven satellite network load and unstable link connections, this paper proposes a globally adaptive satellite network routing strategy, named G-ADDV, based on traffic prediction. This strategy is an improvement upon the original AODV on-demand routing protocol. In the route discovery phase, the traffic prediction mechanism is merged. As traffic load data are typically time-series data, neural networks demonstrate excellent performance in handling such data. They can capture temporal correlations, aiding in the time-series prediction of future traffic loads [5]. Therefore, in this paper, the traffic load of the satellite is predicted by the neural network model. The forward control mechanism and path update mechanism are used to avoid heavy load nodes appearing in the routing path. In the route maintenance stage, to avoid the secondary fracture of the repaired link, the node stability is defined, and the route repair mode is selected according to the broken position of the link. The contributions of this paper are as follows:  
1. During the route discovery phase, a traffic prediction mechanism is introduced.