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

TL;DR

This paper introduces a topology-aware coordination framework for low-altitude wireless networks, enabling efficient multi-functional cooperation among heterogeneous devices to improve network performance.

Contribution

It proposes a novel topology-aware architecture that models the network as a sparse graph for better coordination and efficiency in diverse multi-tier wireless systems.

Findings

Outperforms baseline approaches in coordination efficiency

Models network as a sparse graph for better task management

Achieves higher multi-task coordination efficiency

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.