# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 1 Introduction  
With the global coverage of satellite networks and the development of communication technologies, a wide range of applications, including data collection and remote sensing [1, 2, 3], surveillance [4], and global broadband Internet access [5], are emerging. In particular, unlike geostationary earth orbit (GEO) satellites and medium earth orbit (MEO) satellites, which are located at altitudes of about 36,000 km and 2000\(\sim\)36,000 km, respectively, low earth orbit (LEO) satellites are located at altitudes of 500\(\sim\)2000 km and can guarantee better signal quality and lower propagation delay [6]. Therefore, they are perceived as an attractive platform for edge computing [7, 8, 9] and a key enabler for ubiquitous 5G and 6G services [10]. In remote sensing and surveillance applications as major applications of LEO satellite networks, when LEO satellites meet ground stations (GSs), they need to transmit collected data to GSs (i.e., download) for further data processing or delivery. Unlike GEO satellites' relatively stationary mobility from the point of view of a GS, LEO satellites orbit the earth with high mobility. This mobility of LEO satellite imposes the following unique characteristics. First of all, the amount of transmittable data of a LEO satellite highly depends on its contact time with GS, which is determined by its orbit. In addition, the type and the amount of the retaining data of the LEO satellite are highly affected by its trajectory. For example, a satellite that has passed through deserts or suburbs will likely have a smaller amount and less important types of data compared with those that have passed urban areas. In addition, a satellite can collect more important data when it rotates its orbit during the daytime than the night time. To deliver data efficiently for a given time, there have been studies on data compression for satellite networks [11; 12]. However, in these studies, loss of original data is inevitable owing to the compression process, and they require additional resources and time for the compression and decompression processes. Meanwhile, ref. [13; 14; 15] investigated sum rate maximization problems. [PERSON] et al. [13] considered an integrated terrestrial-satellite network aided by an intelligent reflecting surface (IRS), which is a promising technology for satellite communications in terms of security [16], energy saving [17], and performance improvement [18] by making incoming signals constructive or destructive by shifting phases. They proposed an iterative algorithm with the objective to maximize the weighted sum rate of all users. [PERSON] et al. [14; 15] tackled the spectrum scarcity issue in LEO satellites, and they proposed a cognitive-radio (CR)-enabled satellite network using the rate splitting multiple access (RSMA) technology, where GEO and LEO satellites work as primary and secondary nodes, respectively. They also formulated a joint problem of user association and beam resource management for LEO satellites and proposed a greedy algorithm with relaxation techniques. However, they only considered a single LEO satellite; no communications links between LEO satellites were considered. On the other hand, refs. [19; 20; 21] investigated data-transmission scheduling problems. [PERSON] et al. [19] proposed a greedy data -scheduling algorithm for satellite networks consisting of multiple satellites and GSs. [PERSON] et al. [20] formulated an integer linear programming (ILP) problem using a graph considering the resources of satellites and proposed an iterative algorithm for maximizing the sum of successfully scheduled tasks' priorities. [PERSON] et al. [21] formulated a joint optimization problem of observation and transmission for agile earth-observing satellite networks and proposed a semi-definite relaxation method and genetic algorithm. However, the above-mentioned studies were unable to resolve the issue of the under-utilization of communication resources when the satellites with sufficient contact time do not have a plenty of data.