User Stories

AERPAW gave me the freedom to design and run complex UAV experiments at scale, with the kind of detailed data and support that’s hard to find anywhere else.”

Participating in the AERPAW challenge has been one of the most influential experiences of my graduate studies. It directly strengthened my research capabilities, broadened my technical perspective, and helped shape my future research direction in mobile wireless systems and autonomous networking.”

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The AERPAW testbed and its digital twin have enabled the successful validation of various machine learning (ML) models in drone-assisted communication scenarios. For example, multiple ML models over Riemannian manifolds were successfully tested for uplink aerial link scheduling.”

AERPAW allows us to validate our simulation study results in a realistic field testing setups, which has helped us to improve the technology readiness levels of our research products”

As we go through customer discovery interviews for NSF I-Corps I am actively thinking about ways to further utilize the AERPAW platform and its datasets. Currently, the datasets related to either spectrum or UAV tracking/localization especially come into mind.”

AERPAW enables access to high-fidelity 3D air-to-ground channel data across different altitudes and trajectories, along with multi-protocol experimentation, which is essential for validating next-generation aerial and non-terrestrial networks, expanding to satellite constellations.”