AI-Driven Low-Altitude Economy: Spectrum, Mobility, and Validation
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Abstract (arXiv)
The Low Altitude Economy (LAE) network, with its transformative capabilities, is a candidate to become one of the major technological developments of the next decade for air mobility. However, the expected unprecedented density, mobility, and heterogeneity pose challenges and require new approaches, as it renders traditional rule-based approaches inadequate. To address these challenges, this study introduces artificial intelligence (AI)-based approaches and validation frameworks for transitioning AI-enabled technologies from simulation-based studies to practical and deployable systems. This study discusses essential enablers for intelligent LAE networks. First, AI-based spectrum sensing and coexistence utilizing the distributed nature of LAE nodes is introduced. Then, joint resource allocation and trajectory optimization driven by reinforcement learning is discussed. Bridging the gap between simulation and deployment through experimental platforms such as Aerial Experiments and Research Platform for Advanced Wireless (AERPAW), which are critical for validating models under realistic and non-stationary airspace conditions, is also addressed. The study concludes by highlighting open issues and outlining a forward-looking roadmap for the development of efficient, interoperable, and scalable AI-driven LAE ecosystems.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 2 Jun 2025.
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References · 6
- 2018Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspectivecited by 5
- 2018Fundamental Tradeoffs in Communication and Trajectory Design for UAV-Enabled Wireless Networkcited by 4
- 2022LSTM-Based Distributed Conditional Generative Adversarial Network For Data-Driven 5G-Enabled Maritime UAV Communicationscited by 3
- 2024Digital Twins and Testbeds for Supporting AI Research with Autonomous Vehicle Networkscited by 3
- 2025Optimizing Wireless Resource Management and Synchronization in Digital Twin Networkscited by 3
- 2024Federated Learning for UAV-Based Spectrum Sensing: Enhancing Accuracy Through SNR-Weighted Model Aggregationcited by 1
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- 2026Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective
- 2025Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning
- 2024Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach
- 2026UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning
- 2025Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking
- 2025Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL
- 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches
- 2025Large Language Model-enhanced Reinforcement Learning for Low-Altitude Economy Networking