Harnessing Supervised Learning for Adaptive Beamforming in Multibeam Satellite Systems

January 1, 2024·
Flor Ortiz
,
Juan A Vasquez-Peralvo
,
Jorge Querol
,
Eva Lagunas
,
Jorge L González Rios
,
Luis Garces
,
Victor Monzon-Baeza
,
Symeon Chatzinotas
· 1 min read
Abstract
In today’s ever-connected world, the demand for fast and widespread connectivity is insatiable, making multibeam satellite systems an indispensable pillar of modern telecommunications infrastructure. However, the evolving communication landscape necessitates a high degree of adaptability. This adaptability is particularly crucial for beamforming, as it enables the adjustment of peak throughput and beamwidth to meet fluctuating traffic demands by varying the beamwidth, side lobe level (SLL), and effective isotropic radiated power (EIRP). This paper introduces an innovative approach rooted in supervised learning to efficiently derive the requisite beamforming matrix, aligning it with system requirements. Significantly reducing computation time, this method is uniquely tailored for real-time adaptation, enhancing the agility and responsiveness of satellite multibeam systems. Exploiting the power of supervised learning, this research enables multibeam satellites to respond quickly and intelligently to changing communication needs, ultimately ensuring uninterrupted and optimized connectivity in a dynamic world.
Type
Publication
2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN)
publication

Abstract & Overview

In today’s ever-connected world, the demand for fast and widespread connectivity is insatiable, making multibeam satellite systems an indispensable pillar of modern telecommunications infrastructure. However, the evolving communication landscape necessitates a high degree of adaptability. This adaptability is particularly crucial for beamforming, as it enables the adjustment of peak throughput and beamwidth to meet fluctuating traffic demands by varying the beamwidth, side lobe level (SLL), and effective isotropic radiated power (EIRP). This paper introduces an innovative approach rooted in supervised learning to efficiently derive the requisite beamforming matrix, aligning it with system requirements. Significantly reducing computation time, this method is uniquely tailored for real-time adaptation, enhancing the agility and responsiveness of satellite multibeam systems. Exploiting the power of supervised learning, this research enables multibeam satellites to respond quickly and intelligently to changing communication needs, ultimately ensuring uninterrupted and optimized connectivity in a dynamic world.

Publication Metadata

  • Authors: Flor Ortiz, Juan A Vasquez-Peralvo, Jorge Querol, Eva Lagunas, Jorge L González Rios, Luis Garces, Victor Monzon-Baeza, Symeon Chatzinotas
  • Venue: 2024 IEEE International Conference on Machine Learning for Communication (2024)
  • Research Areas: Satellite Communications, Beamforming & MIMO
  • DOI: 10.1109/icmlcn59089.2024.10624792
  • Open Access Repository: ORBiLu – University of Luxembourg
Satellite Communications Beamforming & MIMO