Topology aware method improves sensing communication and charging in low altitude networks
Topology-Aware Integrated Sensing, Communication, Charging in Massive Low-Altitude Wireless Network
Information Theory
Summary
Low-altitude wireless networks need to handle sensing, communication, and charging all at once, which is complicated because these functions depend on each other. The authors present a new method that views devices on the ground and in the air as connected points in a graph, making it easier to manage their interactions. Their approach uses this graph to quickly adjust how resources are shared, working well even when the network is very large or changes a lot. Tests show that this method does a better job than current techniques at coordinating multiple functions efficiently and reliably.
What this means in practice
- •For network schedulers: Manage coordination of sensing, communication, and charging resources efficiently in massive airborne and terrestrial wireless networks using a low-complexity topology reconfiguration method.
- •For wireless system integrators: Design systems that support dynamic resource adjustment in large-scale low-altitude networks by representing device interactions as bipartite graphs to improve scalability and robustness.
Authors
Han Yu, Jiajun He, Zhaofeng Liu, Hing Cheung So
Abstract
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.