# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 1 Introduction  
LEO satellites operate in orbits ranging from approximately 200 to 2000 km above Earth. Because of their close proximity to Earth, the signal transmission time between LEO satellites and Earth is very short. This provides a significant advantage over satellites at higher altitudes, as LEO satellites have very low latency, making applications such as real-time communication and remote operations feasible [1]. Additionally, for Earth observation, LEO satellites are capable of capturing high-resolution images. The amount of the Earth's surface captured by the satellite's camera depends on the altitude, with LEO satellites offering detailed information. They are commonly used in industries such as climate change monitoring and disaster response [2]. Another key feature of LEO satellites is that they are highly affected by Earth's gravity, which causes them to move relatively quickly. The lower the orbit, the shorter the time it takes for the satellite to complete one orbit, allowing it to pass the same location more frequently and enabling rapid global coverage. By operating LEO satellites in large numbers and forming a network, it is possible to overcome geographical constraints and provide flexible and reliable communication services [3]. Such LEO satellite networks are a core component in modern satellite communications, enabling stable communication in regions with inadequate infrastructure, such as oceans, mountainous areas, and developing countries [4]. For example, there is the Starlink network, an LEO satellite network developed by SpaceX with the goal of providing fast and reliable internet connections worldwide. This network is expected to bring various social and economic benefits beyond simply providing internet services [5]. However, despite the many advantages of LEO satellites, they face several challenges and limitations due to their unique environment in comparison to ground-based communication networks. LEO satellites orbit the Earth rapidly, which causes frequent changes in the connection status between communication nodes. This rapid mobility results in unstable satellite-to-satellite links, leading to frequent communication link disconnection [6]. In satellite communications, where reliable data transmission is critical, link disconnections can cause packet loss, reducing communication quality. This means that continuous reconnections with new satellites must be established due to the constantly changing topology of the satellite network [7]. Frequent updates to routing tables or configurations can introduce overhead, and particularly with variable latency, this can impact network stability. Furthermore, since LEO satellite networks must dynamically set up routes, designing effective routing protocols is challenging and becomes more complex as the number of satellites increases. Efficient routing in LEO satellite networks, which provide essential services to modern society, remains a major challenge, and various algorithmic studies are being conducted to address this issue. While these approaches open up new possibilities beyond the limitations of traditional algorithms, LEO satellites face constraints in energy and computational resources. Satellites must orbit quickly, and to secure wide coverage, many satellites need to be deployed cost-effectively. Therefore, LEO satellite routing algorithms must consider the resource limitations of onboard computers and optimize energy consumption [8; 9]. These limited computational resources and power supply pose significant constraints on performing large-scale data processing or complex computations. However, real-time inference still needs to be performed in such an environment, making computational time a critical factor. Real-time inference refers to the ability of the satellite system to process data and make decisions in real time, requiring a quick response. Optimization is necessary to avoid prolonged computation times, and methods to maximize the efficient use of limited resources are essential.