发表机构
Nanjing University of Information Science and Technology; Nanyang Technological University; The University of Sydney（南京信息工程大学; 南洋理工大学; 悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种语义通信多无人机边缘计算框架，联合优化任务卸载、迁移与轨迹控制，并设计基于异构图注意力网络的多智能体强化学习算法，以最大化语义相似度并降低延迟和能耗。
AI 中文摘要
无人机辅助的移动边缘计算是未来6G网络的关键技术，提供广泛的覆盖和灵活的计算服务。然而，无人机资源的有限性和网络结构的动态变化使得维持高效率变得困难。现有方法往往忽略任务的语义信息以及无人机与移动终端之间的复杂关系，导致协调性差。本文提出了一种在语义通信使能的多无人机边缘计算系统中，针对任务卸载、任务迁移和轨迹控制的联合优化框架，旨在最大化语义相似度，同时最小化任务延迟和系统能耗。为解决该问题，我们开发了一种基于异构图注意力网络的多智能体双延迟深度确定性策略梯度算法。具体而言，我们使用异构图对网络拓扑进行建模，并应用异构图注意力网络提取重要的语义特征。这些特征随后被集成到多智能体双延迟深度确定性策略梯度框架中，以支持联合优化。
英文摘要
Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) is a key technology for future 6G networks, providing wide coverage and flexible computing services. However, the limited resources of UAVs and the dynamic changes in the network structure make it difficult to maintain high efficiency. Existing methods often ignore the semantic information of tasks and the complex relationships among UAVs and mobile terminals, resulting in poor coordination. This paper proposes a joint optimization framework for task offloading, task migration, and trajectory control in semantic communication-enabled multi-UAV edge computing systems, aiming to maximize semantic similarity while minimizing task latency and system energy consumption. To tackle the resultant problem, we develop a Heterogeneous Graph Attention Network-based Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (HAN-MATD3) algorithm. Specifically, we use a heterogeneous graph to model the network topology and apply HAN to extract important semantic features. These features are then integrated into the MATD3 framework to support joint optimization.
Comments14 pages, 10 figures
