AeroSpec: An Adaptive Spectrum Framework for Autonomous Aerial Systems: Optimization, Decentralized Markets, and Deployment

About This Grant

Unmanned aerial systems (UAS) are expected to see rapid growth in the coming decade with millions of autonomous flights annually. To fully unlock the potentials of UAS and advanced air mobility (AAM) technologies, most flights will need to operate in beyond visual line-of-sight (BVLOS) scenarios. Despite being autonomous, BVLOS flight operations require reliable communication links with ground stations and other aircraft to ensure safety and efficiency in the national airspace. However, there is currently no dedicated licensed spectrum for these safety-critical communications, leading operators to rely on unlicensed or experimental licenses without protection from harmful interference. Aligned with the National Spectrum Strategy Implementation Plan, this project aims to develop a new dynamic frequency management system (DFMS) tailored for UAS operations. The DFMS will allow interference-protected spectrum access for autonomous flight communications, supporting the safe and scalable integration of UAS into the airspace. This project advances spectrum science and engineering for aerial use cases and informs public policy on efficient spectrum utilization by sharing extensive spectrum measurement data. It also promotes STEM education by engaging graduate and undergraduate students in interdisciplinary research at the interaction of wireless communications, aerospace engineering, and economics. This project aims to design and validate a DFMS that enables adaptive, location- and time-based spectrum access for UAS operations, particularly in the 5030–5091 MHz band. The project combines theoretical developments in spectrum optimization, cooperative sensing, and decentralized spectrum markets with multi-vehicle real-world flight tests. Specific research tasks include (1) developing a comprehensive system and architecture design of the DFMS to enable dynamic spectrum allocation, (2) conducting multi-vehicle flight tests to gather extensive spectrum data, which are crucial for testing and refining spatiotemporal interference and channel models, (3) enabling spectrum situational awareness through adaptive machine learning (ML)-based algorithms trained on collected spectrum data, establishing joint cooperative sensing and access, and ultimately achieving automated spectrum monitoring and enforcement, and (4) developing a decentralized market system with advanced reservation, implementing the “pay-as-you-fly” concept for UAS operators seeking access to interference-protected aviation-grade spectrum in the 5030-5091 MHz band. The project results in extensive datasets of spatiotemporal channel measurements and interference characteristics to inform and support current and future spectrum policy efforts by the broader research community, regulatory agencies, and standardization committees. 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.