# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 1 Introduction  
### Background and Motivations  
With the emergence and widespread adoption of 5G systems, the limitations inherent to terrestrial networks, particularly in terms of the coverage area and construction costs, have become increasingly pronounced. This has spurred a quest for alternative solutions to mitigate these drawbacks. Concurrently, as communication technology continues to evolve and our understanding of space deepens, there has been a burgeoning interest in exploring the potential of low-Earth-orbit (LEO) satellite internet as a viable alternative. LEO satellite networks offer several distinct advantages over traditional terrestrial networks. Firstly, they provide extensive coverage, spanning vast geographic areas that may be challenging or cost-prohibitive for terrestrial infrastructure to reach. Secondly, the deployment costs associated with LEO satellites are comparatively lower than those of ground-based networks, making them an attractive option for expanding connectivity to underserved or remote regions. Additionally, LEO satellites boast a significant capacity, enabling them to support a large volume of data traffic with minimal latency. Furthermore, the integration of LEO satellite networks with other high-altitude platforms, such as UAV networks, presents opportunities to enhance the delivery of services to ground users. By leveraging the flexibility and mobility of UAVs, satellite internet providers can offer more convenient and tailored services, thereby improving the overall user experience [1]. In the context of future communication technologies, such as 6G, satellite internet emerges as a key breakthrough direction. Its inherent advantages make it well-suited for various applications, including civilian and emergency communication, Internet-of-Things (IoT) connectivity, military operations, and beyond [2; 3; 4]. As such, the potential for the further development and widespread application of satellite internet is vast and promising [5]. Amidst the construction and deployment of LEO constellations such as Starlink, the conventional LEO satellite service model, which relies on transparent forwarding, is nolonger suited to meet the rapidly evolving demands of businesses. Various applications now require lower latency and smoother content delivery, necessitating more responsive and efficient services at the user end. Advancements in storage technology and computing chips have empowered LEO satellites to offer computing and caching services for content. However, the exponential surge in demand for low-latency mobile applications and multimedia services poses a significant challenge. According to Cisco's Visual Networking Index (VNI) report [6; 7], IP video traffic is expected to double by 2022, constituting 82% of the total IP traffic. In response to the escalating traffic demands, LEO satellite caching has garnered attention as an innovative solution. By pre-caching popular content on LEO satellites, which can function as fog nodes, timely and dependable content services can be delivered to edge users [8; 9; 10; 11]. In satellite networks, numerous challenges are encountered, such as the time-varying topology of the satellite network. The neighboring satellites of a satellite vary at different times. Satellites move at high speeds, resulting in increased switching frequencies and lower cache hit probabilities. In the model proposed in the paper, content requested by users is considered a cache hit for both the satellite and its neighboring satellites, which significantly differs from ground-based networks. The dynamic movement of LEO satellites results in continuous changes in the coverage area of satellites and the connected edge user terminals, necessitating frequent data replacement in the cache [12; 13; 14; 15]. However, the cache space of each satellite is limited, and redundant data may accumulate if identical data segments are repeatedly cached across the satellite constellation, leading to suboptimal cache space utilization.