# Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks
###### Abstract  
Recently, many Low Earth Orbit (LEO) satellite networks are being implemented to provide seamless communication services for global users. Since the high mobility of LEO satellites, handover strategy has become one of the most important topics for LEO satellite systems. However, the limited on-board caching resource of satellites make it difficult to guarantee the handover performance. In this paper, we propose a multiple attributes decision handover strategy jointly considering three factors, which are caching capacity, remaining service time and the remaining idle channels of the satellites. Furthermore, a caching-aware intelligent handover strategy is given based on the deep reinforcement learning (DRL) to maximize the long-term benefits of the system. Compared with the traditional strategies, the proposed strategy reduces the handover failure rate by up to nearly 81% when the system caching occupancy reaches 90%, and it has a lower call blocking rate in high user arrival scenarios. Simulation results show that this strategy can effectively mitigate handover failure rate due to caching resource occupation, as well as flexibly allocate channel resources to reduce call blocking. LEO handover strategy; multiple attributes; caching; deep reinforcement learning 2021223022  
handover [8], mainly focus on handover based on the receiving signal quality, remaining service time, and so on. [PERSON] et al. [9] proposed three different handover criteria: the maximum remaining service time, the maximum number of idle channels, and the minimum distance, which could be applied to new coming or handover calls. The strategy based on the maximum remaining service time can greatly reduce the handover times, delay, and signaling cost. On basis of this strategy, [PERSON] et al. [10] proposed a velocity-aware handover prediction method to find the shortest path of the time-expanded graph dynamically. [PERSON] et al. [11] proposed a distributed handover method by taking into account the impact of routing, and it can reduce the propagation delay while keeping the handover times acceptable. [PERSON] et al. [12] proposed a simple real-time handover strategy, which exploited both the global positioning system (GPS) infrastructure and multiple satellites, to minimize the expected handover times. The strategy based on the maximum number of idle channels selected the candidate satellite with the largest number of available channels. Thus, the load distribution of satellite systems can be balanced, and the limited network resources can be efficiently utilized. [PERSON] et al. [13] proposed a dynamic channel reservation scheme based on priorities. The traffic, which is predicted based on the deterministic movement property of LEO satellites, was used to obtain the thresholds for reserved channels. It can effectively reduce handover failure rate and improve channel utilization by dynamically adjusting the thresholds according to the traffic conditions. The strategy based on the minimum distance selected the candidate satellite by considering the distance between the satellites to avoid link interruption. [PERSON] et al. [14] proposed a graph theory-based inter-satellite handover strategy, which adopted the shortest path algorithm to obtain the optimal handover scheme. Furthermore, other single-attribute handover criteria can be achieved with this handover model by changing the path weights. Since the above studies only consider the effect of a single attribute of the candidate satellite, they cannot achieve a good trade-off between handover times, system load, and success rates of handover. [PERSON] et al. [15] proposed a multi-layer handover management framework and different handover procedures based on handover prediction which can reduce handover delay and signaling cost. Furthermore, they also proposed a dynamic handover optimization method, which takes traffic, rate demand, and channel gain into account, aiming at reducing the dropping rate and guaranteeing the QoS of mobile terminals. [PERSON] et al.