In this paper, we begin by describing the Starlink constellation’s configuration plan, treating each satellite as a network node, naming and numbering the various nodes, and then classifying the laser interstellar links (LISLs) according to their orbital alignment and whether they are permanently visible. Whereupon, the method for calculating the respective theoretical interstellar distances required for establishing LISLs under two distinct orbital scenarios, co-orbital and hetero-orbital are analyzed, and the optimal phasing factors are proposed by solving an optimization algorithm for the shortest distance. The OneWeb and Starlink constellations, as well as the constellation states of various phasing factors are simulated, respectively. The Starlink constellation with F = 11 obtains the best coverage between 60° north and south latitudes predicated upon the analysis of the constellation N Asset Coverage. Following that, the first phase of the Starlink constellation deployment was modeled in order to analyze and count the number of permanent LISLs in orbit, adjacent to, and nearby. Subsequently, the characteristics of azimuthal, elevation, and range (AER) were subsequently analyzed to ascertain their variation law and to compile the number of permanent and temporary LISLs that could be established at various inter-distance ranges. Finally, predicated on the simulation results, the optimal LISLs connection strategy for the Starlink constellation is evaluated and a static topology for the constellation is constructed.

Due to numerous Low Earth Orbit (LEO) satellites, urgent analysis of many temporary inter-satellite links (ISLs) is necessary for mega constellation networks. Therefore, introducing a dynamic link in topology design is crucial for increasing constellation redundancy and improving routing options. This study presents one class of static topology of satellites (STLS) and two types of dynamic topology of satellites (DTLS). Firstly, a call model based on global population density distribution is determined using world population density by provincial administrative divisions. Then, using a common simulation platform, the Dijkstra algorithm obtains random paths between 10,000 pairs of urban ground stations, adopting a time slice division strategy. Finally, 3 indexes are obtained within 66-time slices: average call distance, number of hops, and total time delay. Results show that DTLS1 reduces these indexes by 3.58%, 3.72%, and 3.57%, respectively, compared with DTLS2 under the same conditions, indicating that DTLS1 has the best network performance, transmitting traffic quickly in any direction through the reverse track, thereby verifying the related hypothesis.