# Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain
## 1 Introduction  
A large-scale satellite network has the characteristics of low propagation delay, high-dynamic, and multiple satellites that can be selectively accessed by the Earth station (ES). It has received more attention in constructing 5/6G and the Internet of Things (IoT) [1, 2]. In recent years, satellite Internet has ushered in a new wave of development. To satisfy the demand for high-capacity communication from ES, satellite Internet mainly uses large-scale satellite constellations. Typical large-scale constellations include the StarLink, the OneWeb, and the Amazon Kuiper. The SpaceX designed StarLink constellation is expected to include approximately 12,000 satellites in the low earth orbit and very low earth orbit constellations [3]. The first phase of the StarLink constellation consists of 4409 satellites, distributed in five different altitude Shells. The satellites on the same shell are at the same altitude from the ground. The Kuiper constellation plan consists of 3236 satellites [4]. The OneWeb constellation plan consists of 650 satellites [5]. Large-scale satellite networks can provide broadband access and high-speed Internet services in theory. However, the rapid topology changes of large-scale satellite networks, the limited resources of a single satellite, and the high switching frequency of earth-satellite links (ESLs) have brought new challenges to the access selection of satellite networks. Access selection is the primary link for ES to transmit data using a satellite network. The state of access satellite affects the quality of services (QoS), such as transmission delay, outage probability, and call blocking rate. Traditional access selection strategies include single parameter-based access selection algorithms and multi-attribute-based access selection algorithms. Access selection algorithms based on a single parameter include the longest coverage time access algorithm, the shortest transmission distance access algorithm, and load balancing algorithm [6; 7; 8; 9; 10; 11; 12]. These methods mainly select access satellites based on a single target attribute. For example, in Literature [6], to reduce the switching times, the access strategy is proposed with the goal of the longest coverage time. In Literature [10], to reduce the path loss and improve the signal-to-noise ratio, the access strategy was proposed with the shortest transmission distance as the goal. In Literature [11] is to achieve network load balancing. The ES will choose the satellite which is beneficial to the network load balancing as the access satellite. The most widely used access algorithm based on multiple attributes is the comprehensive weighting algorithm [13; 14; 15; 16; 17; 18]. For example, the weighted algorithm based on QoS proposed in Literature [17]. The algorithm chooses the access satellite after weighting the shadow effect, signal-to-noise ratio, and elevation angle. This method effectively reduces the call blocking rate and switching failure rate. Based on the comprehensive linear weighted multi-satellite coverage access selection strategy is proposed in [18]. The elevation angle, the distance between the ES and the satellite, the coverage time, and the number of idle channels are comprehensively weighted, which reduces the blocking rate and the outage probability of ESL. The combined free demand assignment multiple access Protocol (CFDAMA) has received much attention from researchers [19; 20; 21]. Free access is assigned by polling, and on-demand access is assigned by reservation [22]. To improve the access performance of CFDAMA, researchers have improved CFDAMA from different perspectives. For example, in literature [19], to realize the data transmission of the ESs in time, the coverage time of the satellite is fully utilized. A combined free/demand assignment multiple access schemes based on convolutional long short term memory network and transfer learning (CFDAMA-CLSTMTL) is proposed by periodically predicting the data generated by ESs.