# Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks
## 5 Discussion and Conclusions  
In this paper, a caching-aware intelligent handover strategy is proposed for the LEO satellite network. The strategy is different from existing handover strategies. First, the handover strategy focuses not only on the selection of the optimal candidate satellite for handover but also on the handover moment when it will be activated. That is to say the intelligent handover strategy needs to judge whether the handover can be activated at this moment and to select the candidate satellite. Secondly, the effects of multiple factors, including remaining service time, remaining idle channels, and remaining caching capacity, on the handover strategy are jointly considered. Then, DRL is adopted to make continuous intelligent handover judgment, which enables sequential decision to maximize long-term gains by interacting with the environment.  
Via simulation, it is verified that the caching-aware intelligent handover strategy has a significant performance improvement in both the handover failure rate and call blocking rate. The performance of this strategy is compared with typical RST, NIC single-attribute handover, and MAF multi-attribute strategies. When the system caching occupancy is 10%, this strategy reduces the handover failure rate by nearly 40% compared to the RST handover strategy and nearly 20% compared to the MAF strategy. With the increasing of caching occupancy, the handover failure rate of the RST, NIC, and MAF handover strategies increase rapidly, while the caching-aware handover strategy proposed in this paper only has a failure probability of 18.3%. This shows that the handover strategy proposed in this paper can improve the quality of service for users in high caching occupancy scenarios. In addition, the system call blocking rates of each policy are compared for different user arrival rates. When the user arrival rate is high, the proposed intelligent handover strategy reduces the call blocking rate by 25% compared to the RST handover strategy and by 18.5% compared to the MAF handover strategy. Therefore, it can be concluded that the proposed strategy can effectively balance the system load, relieve the network pressure, reduce packet loss, and improve the quality of service for users.  
Moreover, the DRL used in this strategy can make intelligent and continuous handover decisions to obtain maximum long-term gains. The complexity of the DRL mainly depends on the dimensions of state and action space, which is affected by the number of users, satellite and handover factors considered. Generally, the training process of DRL will cost long time to converge. Once the training process is finished, the complexity of decision process is low. Therefore, the training process can be executed on ground gateways with a large amount of computing resources, and the decision process can be implemented on-board. Moreover, artificial intelligence applied in satellite communication systems is still an open topic, and we will also investigate other light-weight models that could be utilized for resource management in satellite networks in our future works.  
Conceptualization, T.L., Y.X., G.C., and W.W.; methodology, T.L., Y.X., and G.C.; software, T.L. and Y.X.; validation, T.L., Y.X., and G.C.; formal analysis, T.L., G.C., and W.W.; investigation, T.L., G.C. and W.W.; resources, T.L., Y.X., and G.C.; data curation, T.L.; writing--original draft preparation, T.L., Y.X.; writing--review and editing, G.C.; visualization, T.L., Y.X., and G.C.; supervision, T.L., G.C., and W.W.; project administration, G.C. and W.W.; funding acquisition, G.C. and W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [No.61971054].  
Not applicable.  
The authors declare no conflict of interest.