# Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks
###### Abstract
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Strategy Types**} & \multirow{2}{*}{**Authors**} & \multicolumn{4}{c}{**Handover Factors**} & \multirow{2}{*}{**Performance**} \\ \cline{3-4} \cline{6-6}  & & **Service Times** & & & **Channels** & **Distance** & **Others** \\ \hline \multirow{8}{*}{**Single attribute**} & [PERSON] et al. [9] & \multirow{8}{*}{\(\checkmark\)} & \multirow{8}{*}{\(\checkmark\)} & & reduce the handover times \\  & & & & & balance load, \\  & & & & & reduce handover failure rate \\ \cline{3-6}  & & & & \(\checkmark\) & & avoid link interruption \\ \cline{2-6}  & [PERSON] et al. & \(\checkmark\) & & & velocity-aware & handover prediction, \\  & [10] & & & & & find the shortest path \\ \cline{2-6}  & [PERSON] et al. & \(\checkmark\) & & & routing delay & reduce the propagation delay \\  & [11] & & & & & \\ \cline{2-6}  & [PERSON] et al. & \(\checkmark\) & & GPS, & minimize the handover times \\  & [12] & & & multiple satellite & & \\ \cline{2-6}  & [PERSON] et al. & \(\checkmark\) & & traffic & reduces handover failures rate, \\  & [13] & & & & prediction & improves channel utilization \\ \cline{2-6}  & [PERSON] et al. & \(\checkmark\) & & & & optimal handover strategies \\  & [14] & & & & & for end-to-end communication \\ \hline \multirow{8}{*}{**Multiple attribute**} & [PERSON] et al. & \multirow{8}{*}{\(\checkmark\)} & \multirow{8}{*}{\(\checkmark\)} & traffic, rate & reducing the dropping rate, \\  & [15] & & & & demand & guarantee the QoS of mobile users. \\ \cline{2-6}  & [PERSON] et al. & \(\checkmark\) & & \(\checkmark\) & minimize the handover times, \\  & [17] & & & & decrease call-dropping probability \\ \cline{2-6}  & [PERSON] et al. & \(\checkmark\) & \(\checkmark\) & & load-aware & balance load, \\  & [18] & & & & maintain low signaling overhead \\ \cline{2-6}  & [PERSON] et al. & \(\checkmark\) & \(\checkmark\) & & single strength & reduce handover times, \\  & [19] & & & & balance load and guarantee QoS \\ \cline{2-6}  & [PERSON] et al. & \(\checkmark\) & \(\checkmark\) & number of users, & reduce handover times, \\  & [20] & & & satellite power & balance load and guarantee SNR \\ \hline \multirow{2}{*}{**Multiple attribute**} & [PERSON] et al. & \(\checkmark\) & \(\checkmark\) & routing delay & reduce handover times, failure rate and transition delay \\ \cline{2-6}  & [21] & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing handover strategies. the snap shot-based handover strategies cannot guarantee the long term performance of the dynamic system. To solve these problems, the effects of on-board caching and joint-effect of multiple attributes on handover strategies are analyzed in this paper. Furthermore, an intelligent handover strategy based on deep reinforcement learning (DRL) is proposed to reduce the dropping probability and call blocking rate. The main contributions are listed as below. 1. A novel framework for caching-aware intelligent handover strategies is proposed for LEO satellite networks. Different from existing handover strategies, the joint-effect of multiple attributes, including remaining service time, remaining idle channels, and remaining caching capacity, on handover performance are investigated with dynamic network topology. 2. To adapt to the dynamic topology of satellite systems, the inter-satellite handover process is modeled as a Markov decision process, and the process for the intelligent handover strategy is provided in detail. 3. An intelligent handover algorithm based on DRL is proposed. The algorithm can make decisions on when will the handover be activated and select the target satellite in each time slot. Moreover, the DRL algorithm can make continuous handover decisions, which makes the whole system obtain the maximum long-term benefits. Simulation results demonstrate the effectiveness of the proposed handover strategy.