# AI-Driven Low-Altitude Economy: Spectrum, Mobility, and Validation

arXiv: https://arxiv.org/abs/2506.01378 · HTML: https://mlsanity.com/paper/2506.01378/

## 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.

## Builds on (most cited first, 6 of 6)

- [Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective](https://mlsanity.com/paper/1812.06821/) (2018)
- [Fundamental Tradeoffs in Communication and Trajectory Design for UAV-Enabled Wireless Network](https://mlsanity.com/paper/1805.07038/) (2018)
- [LSTM-Based Distributed Conditional Generative Adversarial Network For Data-Driven 5G-Enabled Maritime UAV Communications](https://mlsanity.com/paper/2205.04196/) (2022)
- [Digital Twins and Testbeds for Supporting AI Research with Autonomous Vehicle Networks](https://mlsanity.com/paper/2404.00954/) (2024)
- [Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks](https://mlsanity.com/paper/2502.05116/) (2025)
- [Federated Learning for UAV-Based Spectrum Sensing: Enhancing Accuracy Through SNR-Weighted Model Aggregation](https://mlsanity.com/paper/2411.11159/) (2024)

## 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.