Computational Models in Mega Constellation Satellite Communications
Session Number
Project ID: CMPS 12
Advisor(s)
Dr. Randall Berry; Northwestern University, McCormick School of Engineering
Discipline
Computer Science
Start Date
19-4-2023 9:05 AM
End Date
19-4-2023 9:20 AM
Abstract
Mega-Constellations are interconnected webs of thousands of satellites that deliver high-speed wireless communication to ground station clients. Major corporations, such as SpaceX and Amazon, utilize mega-constellations stationed at various altitudes in Low Earth Orbit (LEO). In recent years, the number of LEO satellites in orbit has greatly increased, resulting in conflicting bandwidth usage. Any such overlaps between various mega-constellations, known as interferences, are handled using the primitive “1/n” rule, where each interfering satellite receives an equal “1/nth” section of the shared bandwidth. However, the simplicity of this rule allows parties to intentionally create more interferences while accepting the same setbacks as independent systems operating in their own space. Through the creation of a Python Monte Carlo simulation, tradeoffs between interferences, successful transmissions, and signal coverage are investigated to generate new interference regulations and optimize satellite communications. The 2D simulation evaluates fixed satellites in time, analyzing the complex relationships and tradeoffs generated by different numbers of satellites and clients, transmission angles, satellite heights, and the number of companies. Both computational and parametric optimizations were implemented into the simulation, and 1D mathematical models based on ideal circumstances were investigated.
Computational Models in Mega Constellation Satellite Communications
Mega-Constellations are interconnected webs of thousands of satellites that deliver high-speed wireless communication to ground station clients. Major corporations, such as SpaceX and Amazon, utilize mega-constellations stationed at various altitudes in Low Earth Orbit (LEO). In recent years, the number of LEO satellites in orbit has greatly increased, resulting in conflicting bandwidth usage. Any such overlaps between various mega-constellations, known as interferences, are handled using the primitive “1/n” rule, where each interfering satellite receives an equal “1/nth” section of the shared bandwidth. However, the simplicity of this rule allows parties to intentionally create more interferences while accepting the same setbacks as independent systems operating in their own space. Through the creation of a Python Monte Carlo simulation, tradeoffs between interferences, successful transmissions, and signal coverage are investigated to generate new interference regulations and optimize satellite communications. The 2D simulation evaluates fixed satellites in time, analyzing the complex relationships and tradeoffs generated by different numbers of satellites and clients, transmission angles, satellite heights, and the number of companies. Both computational and parametric optimizations were implemented into the simulation, and 1D mathematical models based on ideal circumstances were investigated.