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.

该研究论文介绍了一种基于机器学习的无线资源管理方法，加入环境感知度量评估通信系统整体性能的机器学习模型的资源分配决策的影响。

利用机器学习进行卫星灵活载荷配置

Flexible Payload Configuration for Satellites using Machine Learning

The definition of anomaly detection is the identification of an unexpected
event. Real-time detection of extreme events such as wildfires, cyclones, or
floods using satellite data has become crucial for disaster management.
Although several earth-observing satellites provide information about
disasters, satellites in the geostationary orbit provide data at intervals as
frequent as every minute, effectively creating a video from space. There are
many techniques that have been proposed to identify anomalies in surveillance
videos; however, the available datasets do not have dynamic behavior, so we
discuss an anomaly framework that can work on very high-frequency datasets to
find very fast-moving anomalies. In this work, we present a diffusion model
which does not need any motion component to capture the fast-moving anomalies
and outperforms the other baseline methods.

本研究提出了一种扩散模型来检测高频率数据中的快速异常，该模型不需要任何运动组件，优于其他基准方法，可用于卫星数据分析中。