NSF-AoF: CNS Core: Small: AERIAL: Air-to-Ground Channel Modeling and Tracking at Millimeter-Wave

New York University, New York NY

Investigators

Abstract

Unmanned Aerial Vehicles (UAVs) are becoming indispensable in many emerging industries. The explosive proliferation of this technology has resulted in the creation of a plethora of new applications that pose stringent wireless communications performance requirements. Exchanging large volumes of sensory data in the minimum amount of time is critical in most operations such as surveillance, disaster response monitoring, and search-and-rescue. For this reason, the massive amount of available spectrum in the millimeter-wave (mmWave) bands represents a key enabling technology for future aerial wireless communications. In this realm, several fundamental questions remain unanswered. In particular, characterizing the mmWave signal propagation in mid-air as well as designing the channel tracking mechanisms to maximize aerial connectivity remain important open questions. Channel models represent a key building block to design wireless systems. However, global cellular standardization bodies rely on mmWave channel models that are based on terrestrial measurements. The goal of this project is twofold: (i) Conduct real-world UAV mmWave channel measurements to derive accurate channel models and (ii) design channel tracking algorithms that are aimed at maximizing UAV connectivity. The goal of this project is to advance the understanding of UAV mmWave channel propagation and, accordingly, design novel directional channel tracking algorithms to improve UAV mmWave connectivity. To do so, the team plans plan to: (1) Develop a custom UAV-based mmWave channel sounder with beam steerable phased array antenna to enable channel measurements that are optimized for UAVs; (2) Conduct extensive A2G and A2A mmWave measurements using the channel sounder developed in (1); (3) Train generative neural networks to derive realistic aerial mmWave channel models and compare them with existing 3GPP models; (4) Develop AI-based aerial channel-tracking algorithms to improve UAV mmWave connectivity. The approach adopted by the team will accelerate innovation in a multitude of UAV applications that demand high data rates and low latency. 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.