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 energy consumption by $75.7\%$ while achieving a similar AoI 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指标，在视频直播和元宇宙平台等实时多播应用中引起了广泛研究兴趣。本文考虑一种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策略最小化期望时间平均AoI。由于该问题具有NP-hard特性且调度与路由决策相互交织，现有方法难以直接应用。针对上述挑战，我们将原问题分解为两个子任务，每个子任务均可采用强化学习方法求解。进而提出一种基于图注意力网络（GATs）的创新框架，该框架能够有效捕获图结构信息并具有卓越的泛化能力。为验证所提框架的有效性，我们在三个数据集（包括名为AS-733的真实世界数据集）上开展实验，结果表明：与基线方法相比，本方案在实现相近AoI的同时可将能耗降低75.7%。