Research on Smart Wireless Aerial Networks Facilitating Digital Twin Construction

This paper proposes a comprehensive framework for constructing smart wireless aerial networks to support high-fidelity digital twin (DT) systems. To meet the stringent data rate demands of airborne Light Detection and Ranging (LiDAR) sensing, we analyze the required throughput for point cloud transmission and design a millimeter-wave (mmWave) aerial link budget model. A software-defined architecture integrating Network Function Virtualization/Software Defined Networking (NFV/SDN) is introduced to enable dynamic Unmanned Aerial Vehicle (UAV) orchestration, routing, and network slicing. To enhance robustness and efficiency, we propose two application-layer innovations: a multi-route redundant communication framework and a semantic image transmission protocol using deep joint source-channel coding (DJSCC) with feature-based elastic compression. Furthermore, we implement a multi-agent reinforcement learning strategy to enable autonomous UAV placement and relay network formation in dynamic environments. Simulation results demonstrate the proposed system's scalability, adaptability, and potential to enable reliable and efficient DT construction across diverse deployment scenarios.