From Turbulence to Tranquility: AI-Driven Low-Altitude Network
This work addresses spectrum and coordination problems for urban mobility and logistics networks, but it is incremental as it builds on existing AI techniques without introducing a new paradigm.
The study tackles challenges in Low Altitude Economy networks, such as spectrum management and real-time coordination, by exploring machine learning-based spectrum sensing, AI-optimized resource allocation, and testbed-driven validation to develop efficient and interoperable AI-driven ecosystems.
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.