Age of Information (AoI) is an emerging metric used to assess the timeliness of information, gaining research interest in real-time multicast applications such as video streaming and metaverse platforms. In this paper, we consider a dynamic multicast network with energy constraints, where our objective is to minimize the expected time-average AoI through energy-constrained multicast routing and scheduling. The inherent complexity of the problem, given the NP-hardness and intertwined scheduling and routing decisions, makes existing approaches inapplicable. To address these challenges, we decompose the original problem into two subtasks, each amenable to reinforcement learning (RL) methods. Subsequently, we propose an innovative framework based on graph attention networks (GATs) to effectively capture graph information with superior generalization capabilities. To validate our framework, we conduct experiments on three datasets including a real-world dataset called AS-733, and show that our proposed scheme reduces the average weighted AoI by 62.9% and reduces the energy consumption by at most 72.5% compared to baselines.

翻译：信息年龄（AoI）是一种用于评估信息时效性的新兴度量指标，在视频流和元宇宙平台等实时多播应用中日益受到研究关注。本文考虑一个具有能量约束的动态多播网络，目标是通过能量受限的多播路由与调度最小化期望时间平均AoI。鉴于该问题的NP难特性以及调度与路由决策的相互耦合性，现有方法难以适用。为应对这些挑战，我们将原问题分解为两个子任务，每个子任务均适合采用强化学习方法求解。随后，我们提出一种基于图注意力网络（GATs）的创新框架，以有效捕获图结构信息并具备优异的泛化能力。为验证框架有效性，我们在三个数据集（包括名为AS-733的真实数据集）上进行实验，结果表明：与基线方法相比，所提方案将加权平均AoI降低了62.9%，并将能耗最多降低72.5%。