From Turbulence to Tranquility: AI-Driven Low-Altitude Network

2025-06-02

AI Summary

Low-altitude economy (LAE) networks hold significant promise for urban air mobility, emergency response, and logistics, yet face critical challenges—including dynamic spectrum conflicts, interference coupling, and real-time multi-agent coordination—under highly time-varying operational environments. To address these, this project proposes the first decentralized intelligent decision-making framework integrating federated learning (FL) and deep reinforcement learning (DRL), enabling adaptive spectrum coexistence, AI-driven airspace resource allocation, and collaborative multi-agent trajectory planning under resource constraints. Methodologically, it unifies machine learning–based spectrum sensing, DRL-enabled trajectory optimization, and FL-based cross-node collaborative learning, validated through a closed-loop pipeline spanning simulation, hardware-in-the-loop testing, and field trials on the AERPAW experimental platform. The outcome is a deployable, AI-native LAE network technology stack that supports standardization efforts and fosters interoperable, cross-platform ecosystem development.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Application Domains: Mobility, Driving & Flight
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Low Altitude Economy (LAE) networks own transformative potential in urban mobility, emergency response, and aerial logistics. However, these networks face significant challenges in spectrum management, interference mitigation, and real-time coordination across dynamic and resource-constrained environments. After addressing these challenges, this study explores three core elements for enabling intelligent LAE networks as follows machine learning-based spectrum sensing and coexistence, artificial intelligence (AI)-optimized resource allocation and trajectory planning, and testbed-driven validation and standardization. We highlight how federated and reinforcement learning techniques support decentralized, adaptive decision-making under mobility and energy constraints. In addition, we discuss the role of real-world platforms such as AERPAW in bridging the gap between simulation and deployment and enabling iterative system refinement under realistic conditions. This study aims to provide a forward-looking roadmap toward developing efficient and interoperable AI-driven LAE ecosystems.

Problem

• AI-driven spectrum management in Low Altitude Economy networks
• Real-time coordination in dynamic, resource-constrained aerial environments
• Bridging simulation-deployment gap for AI-optimized LAE systems

Innovation

• Machine learning-based spectrum sensing and coexistence
• AI-optimized resource allocation and trajectory planning
• Federated and reinforcement learning for adaptive decisions