Frequency Plan Design for Multibeam Satellite Constellations Using Integer Linear Programming

Upcoming large satellite constellations and the advent of tighter steerable beams will offer unprecedented flexibility. Consequently, this will require resource management strategies to be operated in high-dimensional and dynamic environments, as existing satellite operators are unaccustomed to oper...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 23; no. 4; pp. 3312 - 3327
Main Authors: Garau-Luis, Juan Jose, Torrens, Sergi Aliaga, Vila, Guillem Casadesus, Pachler, Nils, Crawley, Edward F., Cameron, Bruce G.
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Bandwidth
Channel allocation
Flexibility
Frequency assignment
integer linear programming (ILP)
Integer programming
Linear programming
Optimization
Power consumption
Radio spectrum management
radiofrequency
Resource management
Satellite communications
Satellite constellations
Satellites
Scalability
space communications
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:Upcoming large satellite constellations and the advent of tighter steerable beams will offer unprecedented flexibility. Consequently, this will require resource management strategies to be operated in high-dimensional and dynamic environments, as existing satellite operators are unaccustomed to operational flexibility and automation. Frequency assignment policies have the potential to drive constellations' performance in this new context, but are no exception to scalability and fast operation requirements. Most of existing frequency assignment methods fail to fulfill these requirements, or are unable to meet them without falling short on efficiency. In this paper we propose a new frequency assignment method that prioritizes operational requirements. We present an algorithm based on Integer Linear Programming that fully defines a frequency plan while respecting key system constraints such as handovers, interference, and gateway dimensioning. We can encode goals such as bandwidth maximization or power reduction and optimize plans according to such objectives. In our experiments on systems with 20 to 5,000 beams, we find this method allocates at least 100% more bandwidth and reduces power consumption by 40% compared to previous benchmarks. To ensure scalability, we also introduce an iterative approach of the formulation, which achieves substantial gains in runtime with minimal loss of performance.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2023.3307349