Evaluation of Intelligent Resource Allocation Methods for Interference-Limited Satellite Networks

Satellite systems serve as a crucial tool for non-terrestrial networks, offering a solution to areas where terrestrial network coverage is limited. With the advanced capabilities of modern satellite systems that leverage frequency reuse across multiple beams, the provision of efficient 6G services becomes possible worldwide. However, achieving this efficiency requires an optimal resource allocation (RA) strategy that considers the interference-limited environment and the constraints of limited power and bandwidth. This research paper provides a comprehensive review of existing studies on RA problems in interference-limited multi-beam satellite systems, specifically exploring low-complexity methods. The paper thoroughly investigates the operational principles underlying various RA methods that use simple machine learning algorithms. Additionally, it conducts an in-depth analysis of the advantages and disadvantages associated with these methods. To validate the effectiveness of the proposed low-complexity techniques, the paper presents simulation results obtained from a multi-beam satellite system operating in a Low Earth Orbit (LEO). These results serve as evidence for the efficacy of employing simple RA methods based on linear machine learning.