I am honored to receive the NSF CAREER award for the project “Adaptive Communications and Trajectory Design for UAV-assisted Wireless Networks: a Multi-Scale Decision Framework” from the National Science Foundation. Thank you, NSF
The demand for wireless broadband is growing in the United States and across the world. Unmanned aerial vehicles (UAVs) are envisioned as key components of 5G wireless technology and beyond: thanks to their low cost, improved line-of-sight over terrestrial base stations, and controllable mobility, they will enable low-cost wireless broadband access. Nonetheless, UAVs’ integration into wireless networks poses unique challenges on the network and physical layers, due to the intricate coupling between trajectory design and communication resources to be jointly optimized, and uncertain air-to-ground channel propagation conditions. Furthermore, UAVs need to seamlessly operate under sources of randomness and uncertainty typical of wireless networks. This project aims to design techniques to enable real-time physical-layer adaptation of the communication resources, and adaptive trajectory designs to optimize communication performance and energy-efficiency of the system. This research addresses the global industrial and societal need for ubiquitous wireless broadband access by enabling a cost-effective integration of UAVs into wireless networks. This research integrates an educational and outreach program designed to foster research interests and participation of underrepresented students in electrical engineering, through activities created in collaboration with programs at ASU and local high schools.
This project develops a novel decision-making framework to address the critical need for adaptation in UAV-assisted wireless networks operating under uncertainty. Adaptive techniques are developed that leverage the high mobility of UAVs to optimize communication metrics such as latency, throughput, outage probability, area spectral efficiency, energy efficiency, by focusing on the interplay between network-level optimization and physical-layer communication, trajectory design, and control. A key novelty is a multi-scale decision framework to achieve scalable design. The framework leverages multiple spatio-temporal scales induced by the coupling between trajectory and channel propagation conditions to centralize slow timescale trajectory decisions and decentralize fast timescale communications decisions. The design aspect leverages unique features of single- and multi-antennas, operating at sub-6GHz or millimeter-wave frequencies, and provides adaptation to uncertain and dynamic channel conditions. The second goal consists of designing adaptive multi-UAV wireless systems, including UAV selection, user association, resource allocation, optimal charging schedules to enable uninterrupted operation, and contention-based access schemes to improve coverage and grant-free access. The research results are tested experimentally on NSF PAWR AERPAW by designing a software-defined-radio implementation. The experimental results are integrated into theoretical models for continuous improvement and testing.