The Parameters Comparison of the “Starlink” LEO Satellites Constellation for Different Orbital Shells

Abstract

Communications-integrated satellite-terrestrial networks used for global broadband services have gained a high degree of interest from scientists and industries worldwide. The most convenient structures for such use are low Earth orbit satellites, since they fly closer to the Earth compared to the other orbits, and consequently provide significantly lower latency, which is essential for reliable and safe communications. Among these efforts is the Starlink satellites constellation, developed and partly deployed by the United States Company SpaceX. The constellation is planned to be organized in three spatial shells, each of them made up of several hundreds of small-dimensioned and light-weighted low Earth orbit specially designed satellites to provide broadband services, intending to offer global Earth coverage through their interoperability, combined also with the ground stations as a part of the satellite-terrestrial integrated network. By October 24, 2020, 893 satellites are situated in orbit of altitudes of 550 km under different inclinations, determining the first Starlink orbital shell. Two next generations are planned to be situated at altitudes of 1,110 and 340 km, to complete the appropriate infrastructure of three Starlink satellite shells, toward a global presence of broadband internet services. These three orbital shells offer different space views seen from the ground station (user) because of their different altitudes, thus in this paper a few parameters which describe the satellite’s behavior considered from the ground station’s (user’s) point of view are compared. These parameters in fact stem from the space orbital parameters and are defined as: horizon plane wideness, slant range, latency, and coverage area. A comparison is given for the three Starlink orbit layers, with identification of appropriate drawbacks and advantages as performance indicators. By the end, these parameters are applied to geometrically interpret and confirm the handover process among satellites. This paper may serve to highlight the new challenges of the satellite-terrestrial integrated network, providing some theoretical analysis and performance comparisons for the satellites in different orbit layers seen from the ground station (user) perspective.

The Current State and Future Directions of Modeling Thermosphere Density Enhancements During Extreme Magnetic Storms

“…The understanding of the thermosphere response to magnetic storms and the accurate capability of predicting subsequent satellite orbital drag effects is of paramount interest of, e.g., the U.S. Federal Government and the private sector (National Science and Technology Council, 2015a,b;Cakaj, 2021). The correct orbital tracking of low-Earth orbit (LEO) satellites particularly in a time window of 72 h into the storm is of great interest for taking actions such as predetermined maneuvers as a means to avoid a satellite's collision with debris in space or even with other satellites (Pardini and Anselmo, 2009;Wang, 2010;Lewis, 2019).…”

LEO Satellites in 5G and Beyond Networks: A Review From a Standardization Perspective

“…For efficient communication and for savings within a link budget, higher elevation angles are used. For example, OneWeb constellation applies the elevation of 55 • for users' stations and Starlink, for the first shell (layer at the altitude of 550 km), applies an elevation angle of 40 • for users' stations [18]. On the other hand, a 5G microcell coverage area is approximately 12.5 km 2 .…”

Visibility of LEO Satellites under Different Ground Network Distributions

“…Efficient signal transmission methods between LEO satellites and ground stations have been studied for different LEO constellations and from different points of view. The Starlink orbits were evaluated from the ground station's point of view, which were used to confirm the handover process between different satellites (Cakaj, 2021). In Liu et al, (2019), iterative ground station deployment was developed based on marginal revenue maximization, which is used to optimize the system's throughput.…”