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.

A LEO Satellite Handover Strategy Based on Graph and Multiobjective Multiagent Path Finding

“…He et al [16] exploited DRL to derive the handover strategy for multiuser to minimize average handover under the satellite load constraints. Considering caching capacity, remaining idle channels, and remaining service time, Leng et al [17] proposed a DRL-based intelligent handover strategy to enhance the performance of the system. Xu et al [18] developed a DRL-based LEO handover mechanism, which can maximize the QoE of mobile terminals by predicting the handover factor.…”