VINES: Track 1: NSF-RCF: Elevating Resilience in Aerial Platform Integrated THz Backhaul

Wireless communication links using very high frequency signals in the terahertz band can deliver extremely fast data and high capacity, making them important components in future communication networks. These links can serve as backhauls, which connect core networks to smaller local networks, in places where fiber backhaul deployment is too difficult, too costly, or too disruptive. A promising approach to relay terahertz backhaul signals between distant locations is to combine communication hardware with aerial platforms such as drones or high-altitude balloons. Terahertz wireless connectivity can thereby be established in almost any scenario, including disaster recovery situations or temporary operations. However, such systems are not resilient, being highly sensitive to signal beam misalignment and atmospheric disturbances. This international collaborative project brings together experts from Oklahoma State University, USA and the University of Oulu, Finland to address these challenges. The intellectual merit of this project lies in developing new models, algorithms, and experimental insights for achieving resilience in terahertz backhaul with aerial platforms. The work is organized into three coordinated research thrusts. The first thrust performs first-of-their-kind aerial integrated measurement campaigns to collect terahertz communication and sensing data, enabling quantitative analysis of blockage, misalignment, and adversarial effects. The second thrust develops AI learning-based methods for turbulence prediction and beam optimization, with real-time tracking. The third thrust focuses on network level strategies, including dynamic optimization and self-healing mechanisms for networks with multiple mobile aerial platforms. This project addresses the need for resilient wireless infrastructure by enabling reliable terahertz connectivity in environments where fiber is impractical. The outcomes support applications such as remote sensing, autonomous systems, disaster response, and temporary communication networks. The developed techniques can extend to other mobile networks, including autonomous driving and drone-based logistics. By integrating measurement-driven analysis, adaptive learning methods, physical-layer modeling, and secure communication strategies, the project establishes a broad research foundation upon which next-generation communications can advance further. Open datasets, open-source tools, and collaboration between United States and Finnish institutions will promote cutting-edge workforce development and international scientific exchange. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.