Flexible Payload Configuration for Satellites using Machine Learning
Marcele O. K. Mendonca Flor G. Ortiz-Gomez Jorge Querol Eva Lagunas Juan A. Vásquez Peralvo Victor Monzon Baeza Symeon Chatzinotas Bjorn Ottersten
Satellite communications, essential for modern connectivity, extend access to
maritime, aeronautical, and remote areas where terrestrial networks are
unfeasible. Current GEO systems distribute power and bandwidth uniformly across
beams using multi-beam footprints with fractional frequency reuse. However,
recent research reveals the limitations of this approach in heterogeneous
traffic scenarios, leading to inefficiencies. To address this, this paper
presents a machine learning (ML)-based approach to Radio Resource Management
(RRM).
  We treat the RRM task as a regression ML problem, integrating RRM objectives
and constraints into the loss function that the ML algorithm aims at
minimizing. Moreover, we introduce a context-aware ML metric that evaluates the
ML model's performance but also considers the impact of its resource allocation
decisions on the overall performance of the communication system.