AI-Driven Low-Altitude Economy: Spectrum, Mobility, and Validation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908801315110912 |
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| author | Tekbıyık, Kürşat Raouf, Amir Hossein Fahim Güvenç, İsmail Chen, Mingzhe Kurt, Güneş Karabulut Lesage-Landry, Antoine |
| author_facet | Tekbıyık, Kürşat Raouf, Amir Hossein Fahim Güvenç, İsmail Chen, Mingzhe Kurt, Güneş Karabulut Lesage-Landry, Antoine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Driven Low-Altitude Economy: Spectrum, Mobility, and Validation Tekbıyık, Kürşat Raouf, Amir Hossein Fahim Güvenç, İsmail Chen, Mingzhe Kurt, Güneş Karabulut Lesage-Landry, Antoine Signal Processing Machine Learning 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. |
| title | AI-Driven Low-Altitude Economy: Spectrum, Mobility, and Validation |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2506.01378 |