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

• URL: http://arxiv.org/abs/2506.01378v1
• Date: Mon, 02 Jun 2025 07:12:44 GMT
• Title: From Turbulence to Tranquility: AI-Driven Low-Altitude Network
• Abstract summary: Low Altitude Economy (LAE) networks own transformative potential in urban mobility, emergency response, and aerial logistics.<n>These networks face significant challenges in spectrum management, interference mitigation, and real-time coordination across dynamic and resource-constrained environments.<n>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.
• Score: 17.660082508775957
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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