拓扑感知的大规模低空无线网络中的集成感知、通信与充电
Topology-Aware Integrated Sensing, Communication, Charging in Massive Low-Altitude Wireless Network
- Technical University of Berlin（柏林工业大学）
- Linköping University（林雪平大学）
- Northeastern University（东北大学）
- City University of Hong Kong（香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出拓扑感知框架，利用二部图建模低空网络，实现感知、通信与充电的高效协调，算法低复杂度且优于现有方法。
AI中文摘要：
未来的低空无线网络（LAWNs）预计将同时支持感知、通信和充电功能，导致产生紧密耦合的多目标优化问题，其中异构功能之间存在强相互依赖性。然而，现有的多目标框架通常依赖于针对特定问题的复杂公式和交替优化过程，这些方法在大规模、高度动态的部署中面临高计算复杂性和有限的可扩展性。在本文中，我们提出了一种统一的拓扑感知（TA）框架，将地面和非地面设备抽象为节点，并将其交互建模为边，形成LAWN的二部图表示。通过利用这种图表示，我们基于统一的资源调整规则开发了一种低复杂度的拓扑重配置算法。仿真结果表明，所提出的基于TA的方法在多功能协调效率方面始终优于最先进的方法，同时在涉及大规模地面和空中用户设备的场景中保持鲁棒性和可扩展性。
英文摘要：
Future low-altitude wireless networks (LAWNs) are expected to simultaneously support sensing, communication, and charging, resulting in tightly coupled multi-objective optimization problems with strong interdependencies among heterogeneous functions. However, existing multi-objective frameworks typically rely on complex problem-specific formulations and alternating optimization procedures, which suffer from high computational complexity and limited scalability in large-scale, highly dynamic deployments. In this paper, we propose a unified topology-aware (TA) framework that abstracts terrestrial and non-terrestrial devices as nodes, and models their interactions as edges, forming a bipartite graph representation of the LAWN. By leveraging this graph representation, we develop a low-complexity topology reconfiguration algorithm based on a unified resource-adjustment rule. Simulation results demonstrate that the proposed TA-based method consistently outperforms state-of-the-art approaches in multi-functional coordination efficiency, while maintaining robustness and scalability in scenarios involving massive terrestrial and airborne user equipments.
