Flexible Payload Configuration for Satellites using Machine Learning
This addresses resource allocation inefficiencies for satellite operators in heterogeneous traffic scenarios, representing an incremental improvement over existing methods.
The paper tackles the inefficiency of uniform power and bandwidth distribution in satellite communications under heterogeneous traffic by proposing a machine learning-based approach to Radio Resource Management, treating it as a regression problem with integrated objectives and constraints, and introducing a context-aware metric to evaluate allocation impact.
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.