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.

翻译：卫星通信作为现代互联的核心，为海事、航空以及地面网络无法覆盖的偏远地区提供了通信延展服务。当前地球静止轨道（GEO）系统采用多波束覆盖与部分频率复用方案，在各波束间均匀分配功率与带宽。然而，近期研究表明，该技术在异构流量场景中存在局限性，导致通信效率低下。针对这一问题，本文提出一种基于机器学习（ML）的无线资源管理（RRM）方法。我们将RRM任务建模为回归型ML问题，通过将RRM目标与约束条件纳入损失函数，使ML算法以最小化该损失函数为目标进行优化。此外，我们提出一种上下文感知型ML评估指标，该指标不仅评估ML模型性能，更关注其资源分配决策对通信系统整体性能的影响。