Luan Yuchen, Sun Fukun, Zhou Jiaen
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100045, China.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2024 May 24;24(11):3370. doi: 10.3390/s24113370.
Satellite fog computing (SFC) achieves computation, caching, and other functionalities through collaboration among fog nodes. Satellites can provide real-time and reliable satellite-to-ground fusion services by pre-caching content that users may request in advance. However, due to the high-speed mobility of satellites, the complexity of user-access conditions poses a new challenge in selecting optimal caching locations and improving caching efficiency. Motivated by this, in this paper, we propose a real-time caching scheme based on a Double Deep Q-Network (Double DQN). The overarching objective is to enhance the cache hit rate. The simulation results demonstrate that the algorithm proposed in this paper improves the data hit rate by approximately 13% compared to methods without reinforcement learning assistance.
卫星雾计算（SFC）通过雾节点之间的协作实现计算、缓存和其他功能。卫星可以通过预先缓存用户可能提前请求的内容来提供实时且可靠的星地融合服务。然而，由于卫星的高速移动性，用户接入条件的复杂性在选择最佳缓存位置和提高缓存效率方面带来了新的挑战。受此启发，在本文中，我们提出了一种基于双深度Q网络（Double DQN）的实时缓存方案。总体目标是提高缓存命中率。仿真结果表明，与没有强化学习辅助的方法相比，本文提出的算法将数据命中率提高了约13%。
