Topology-Aware Coordination for Multi-Functional Low-Altitude Wireless Networks

2026-02-24

AI Summary

This work addresses the challenge of inefficient coordination among heterogeneous multi-vendor devices in low-altitude wireless networks, which hinders the exploitation of their complementary strengths. To this end, the paper proposes a multifunctional collaborative framework that categorizes network nodes into three distinct roles—edge mobile terminals, distributed mobile terminals, and computing centers—and introduces a topology-aware sparse graph modeling approach to enable task-aware unified coordination. By integrating hierarchical role assignment, lightweight signal processing, and graph-structure-aware coordination mechanisms, the framework achieves significantly higher multi-task collaboration efficiency compared to baseline methods lacking topological awareness, thereby demonstrating its effectiveness and technical advancement.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Low-altitude wireless networks (LAWNs) are expected to consist of multi-tier, heterogeneous terrestrial and non-terrestrial devices, where effective coordination is essential to fully unlock the complementary capabilities of diverse systems from different vendors. To address this issue, we propose a novel multi-functional coordination framework that enables seamless cooperation within the LAWN while supporting efficient execution of diverse network functions. In the proposed architecture, each device or infrastructure element is assigned to a specific functional role, namely, edge mobile terminal (E-MT), distributed MT (D-MT), or computing center. E-MTs are equipped with lightweight, independent signal processing and computing capabilities, while D-MTs and the computing center handle regional and global coordination, respectively. To enhance the overall network efficiency, we model the LAWN as a sparse graph, where nodes represent network nodes and edges are defined according to a set of controllable connection rules. This topology-aware (TA) representation allows for efficiently solving various coordination tasks across the network. Numerical results show that the proposed TA coordination framework outperforms baseline approaches that lack topological insights, achieving higher efficiency in multi-task coordination. Finally, we discuss key technical challenges and outline potential solutions for future deployment.

Problem

• Low-altitude wireless networks
• multi-functional coordination
• heterogeneous networks
• topology-aware coordination
• multi-vendor interoperability

Innovation

• topology-aware coordination
• low-altitude wireless networks
• sparse graph modeling
• multi-functional framework
• heterogeneous network coordination