## 3 Architecture Design
### Overview of Architecture Design  
Space Air Ground Integration Network (SAGIN), due to its advantages in global coverage, high dynamism, energy efficiency, and security, has become one of the crucial application scenarios for traffic load prediction research in 6G satellite networks. As shown in Figure 2, SAGIN's traffic prediction system model based on machine learning incorporates air networks, satellite systems, and ground networks. The space layer consists of communication satellites distributed across different orbits. The aerial layer consists of diverse communication devices, including airships and unmanned aerial vehicles (UAVs), while satellite networks encompass satellites with varying altitudes, classified as GEO, MEO, and LEO. The ground layer is composed of multiple ground gateways, various ground communication networks, and numerous end-users with different end devices. Lower satellites in the network may be obstructed by higher satellites. Satellite networks are classified as a separate AS (Automatic System). As mentioned in the first and second sections, different researchers have mostly used scenarios involving low earth orbit satellite networks, two-tier satellite networks, or other 5G mobile networks for traffic prediction. These networks typically operate with stationary satellite orbits and predefined coverage areas, constraining network flexibility and leading to increased communication latency and diminished real-time performance. However, SAGIN networks are inherently more complex and dynamic. Multi-tiered components interact in a continuously evolving environment. This heightened complexity and ever-changing conditions demand advanced traffic prediction techniques to effectively manage network resources, a challenge not typically encountered in conventional satellite networks. Therefore, to achieve load balancing across the 6G network system, we present a traffic load prediction model using machine learning and the SAGIN structure in this study.  
As different terminal types and usage scenarios lead to varying demands on computing, storage, and communication capabilities, the capacity wealth of some network nodes can be severely limited or wasted. Therefore, to achieve the aforementioned goals, the system must allocate network bandwidth resources appropriately, irrespective of the terminal type or usage scenario.  
Although compared with 5G, the propagation speed and various indicators of 6G have improved significantly, but the lack of network resources is an eternal problem; in particular, the competition for bandwidth resources still exists and has an increasingly fierce trend. Accurately predicting traffic patterns can optimize resource allocation and balance network load, leading to enhanced network performance. Dynamic control of resources according to different demands of different user terminals is the premise and guarantee of realizing dynamic resource allocation. In order to further utilize bandwidth resources, we must improve real-time control of network resources according to communication needs and thus improve utilization, a goal in which real-time estimation of traffic plays an important role.