# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 1 Introduction
### Related Works  
Currently, scholars have conducted in-depth research on caching strategies for low-Earth-orbit (LEO) satellites. Some scholars aim to reduce the bandwidth resource consumption when satellites transmit cached content, balancing network load distribution as an optimization objective. The authors in [23] propose a dual-layer caching model based on content delivery for satellite-ground communication. This model deploys one layer of cache at ground stations and another layer on satellites, reducing satellite bandwidth consumption through the joint optimization of the dual-layer cache. The literature [24] presents a caching deployment method for LEO satellite networks based on named data networking (NDN), allocating loads in the network and maximizing the effectiveness of internal caching. This method considers the placement of cached data based on the topology of the satellite constellation. The results show that, by using only a small number of caching nodes, the length of the cache path can be reduced to one-third, aiding in load distribution within the network.  
Furthermore, some scholars aim to reduce resource consumption in the network by increasing cache hit rates. [PERSON] et al. employ active learning methods to understand the popularity of content, allowing the system to balance between caching new content and current content [25]. [PERSON] et al. propose a replacement algorithm called ALIRS to improve scalability, maintaining cache hit rates while reducing service latency [26]. [PERSON] et al. formulated a cache placement problem based on obtained request probabilities, aiming to maximize cache hit rates under storage capacity constraints [27]. They developed a dynamic programming algorithm to obtain the optimal caching strategy.  
To enhance satellite service quality for users to obtain a better experience, the authors in [28] propose a quality of experience (QoE)-based optimization scheme for video stream caching placement. This scheme considers the required video stream rates and social relationships among users to optimize cache placement. The results show that the proposed caching method significantly outperforms traditional methods in terms of QoE.  
The authors in [29] study joint caching placement and content delivery in satellite-ground integrated cloud wireless access networks. The literature minimizes long-term power consumption through optimizing cache placement, access point (AP) clustering, and multicast beamforming. In [12], the authors propose a content layout optimization-based caching algorithm for LEO satellite constellation networks. They designed an optimized caching policy to cache popular content preferred by users on satellites. This scheme enhances service quality in various scenarios and achieves more efficient content distribution within the satellite network.  
However, the above-mentioned solutions mainly focus on the optimal caching placement at specific time points, neglecting the cooperation between satellites and resulting in the underutilization of cache space. Therefore, the authors in [30] propose a cooperative content-sharing method between satellites to maximize the utilization of the limited storage space on individual satellites. By increasing the connectivity status between satellites and ground stations to ensure coordinated content transmission, they effectively reduce the average service latency and address challenges using cooperative caching between multiple satellites and base stations. In [31], to jointly consider meeting user resources, the authors model the data-caching problem in satellite-ground networks as a joint optimization problem involving caching, resource allocation, and computational resources, and they solve it using deep Q learning algorithms.